Notebooks Classificador

2026-05-14 16:20:33 +01:00
parent 9ae334410d
commit 2062a91985
734 changed files with 75472 additions and 1730 deletions
@@ -19,9 +19,9 @@
    "Notebook roadmap:\n",
    "1. `01_eda` maps sources, labels, balance, and leakage risks.\n",
    "2. `02_preprocessing` turns those risks into deterministic input handling and controlled augmentation choices.\n",
-    "3. `03_phase1_analysis` compares baseline models under one shared protocol.\n",
+    "3. `03_baselines` compares baseline models under one shared protocol.\n",
-    "4. `04_phase2_analysis` tests preprocessing/model ablations.\n",
+    "4. `04_ablation_questions` tests preprocessing/model ablations.\n",
-    "5. `05_gradcam_analysis` checks where trained models look using existing checkpoints.\n"
+    "5. `05_gradcam` checks where trained models look using existing checkpoints.\n"
   ]
  },
  {
@@ -14,7 +14,7 @@
    "\n",
    "Face crops are generated offline with `classifier/tools/facecrop.py`, producing `cropped/classifier/`. Training configs then choose either raw `data/` or the cropped directory through `data_dir`; face cropping is not hidden inside the transform pipeline.\n",
    "\n",
-    "Roadmap link: this notebook implements the defenses motivated by `01_eda`; `03_phase1_analysis` then asks which baseline model benefits under that fixed protocol.\n"
+    "Roadmap link: this notebook implements the defenses motivated by `01_eda`; `03_baselines` then asks which baseline model benefits under that fixed protocol.\n"
   ]
  },
  {
@@ -716,7 +716,7 @@
    "- augmentation is train-only and can either improve robustness or over-regularize;\n",
    "- normalization is tested as a color-shortcut diagnostic, while ImageNet/default normalization remains the standard pretrained-model default.\n",
    "\n",
-    "Next: `03_phase1_analysis.ipynb` uses this fixed protocol to compare SimpleCNN and pretrained ResNet18 baselines.\n"
+    "Next: `03_baselines.ipynb` uses this fixed protocol to compare SimpleCNN and pretrained ResNet18 baselines.\n"
   ]
  }
 ],
@@ -1,642 +0,0 @@
 # Deepfake Detection Classifier - Implementation Plan
 ## Overview
 This document provides a comprehensive implementation plan for refactoring the deepfake detection classifier project. Each task includes a checkbox to track completion.
 ---
 ## Phase 0: Pre-Implementation Setup
 ### Infrastructure and Configuration
 - [x] Create `classifier/configs/shared.json` with shared parameters:
  - seed: 42
  - val_ratio: 0.1
  - test_ratio: 0.1
  - batch_size: 32
  - optimizer: {type: "adamw", lr: 1e-4, weight_decay: 1e-4}
  - scheduler: {type: "cosine_annealing", T_max: 15}
  - early_stopping_patience: 5
  - num_workers: 4
  - cv_folds: 5
  - data_dir: "data"
  - face_crop_margin: 0.6
 - [x] Implement config loading/merging so experiment configs inherit `shared.json` defaults and override only the variables under test
 - [x] Resolve shared nested fields such as `optimizer.lr`, `optimizer.weight_decay`, and `scheduler.T_max` into the training arguments used by the runner
 - [x] Update existing configs to reference `shared.json` or otherwise document which shared defaults they intentionally override
 - [x] Define one CV protocol for all phases:
  - outer fold: held-out test fold
  - inner validation split: group-aware split from the remaining training folds for early stopping/model selection
  - final reported metrics: aggregate held-out test-fold results across the 5 outer folds
 ### Data Preparation
 - [x] Verify dataset structure and integrity
 - [x] Check that real and fake images are properly organized by source
 - [x] Verify no data leakage between train/val/test splits or CV folds (group-aware by basename)
 ### Cleanup
 - [x] Remove `classifier/tools/ensemble.py` (not part of reorganization plan, conflicts with explainability goals)
 - [x] Remove robustness evaluation from `classifier/tools/analyze.py` (lines 51-104, 82-104, 144) - not part of experimental plan
 - [x] Remove any unused or obsolete config files from previous experiments (see detailed list below)
 - [X] Clean up old output directories if needed (keep important results for reference)
 #### Config Files to Remove (39 total)
 **Root configs (6):**
 - [x] `classifier/configs/resnet18_quick.json`
 - [x] `classifier/configs/resnet18.json`
 - [x] `classifier/configs/simple_cnn_large.json`
 - [x] `classifier/configs/simple_cnn_micro.json`
 - [x] `classifier/configs/simple_cnn_small.json`
 - [x] `classifier/configs/simple_cnn.json`
 **Phase 1 old configs (7):**
 - [x] `classifier/configs/phase1/p1_cnn_base.json` (uses lr=1e-3, epochs=20 - should be 1e-4, 15)
 - [x] `classifier/configs/phase1/p1_cnn_aug.json`
 - [x] `classifier/configs/phase1/p1_resnet18_base.json` (duplicate of new baseline)
 - [x] `classifier/configs/phase1/p1_resnet18_aug.json`
 - [x] `classifier/configs/phase1/holdout/` (entire directory - 6 configs, source holdout not in new plan)
 **Phase 2 old configs (7):**
 - [x] `classifier/configs/phase2/p2_resnet18_224.json` (should be p2a_resnet18_224.json)
 - [x] `classifier/configs/phase2/p2_resnet18_facecrop.json` (should be p2b_resnet18_facecrop.json)
 - [x] `classifier/configs/phase2/p2_resnet18_frozen.json` (frozen backbone not in new plan)
 - [x] `classifier/configs/phase2/p2_resnet34_224.json` (ResNet34 should be in Phase 3)
 - [x] `classifier/configs/phase2/p2_resnet34.json` (ResNet34 should be in Phase 3)
 - [x] `classifier/configs/phase2/p2_resnet50_frozen.json` (ResNet50 should be in Phase 3)
 - [x] `classifier/configs/phase2/p2_resnet50.json` (ResNet50 should be in Phase 3)
 **Phase 3 old configs (4):**
 - [x] `classifier/configs/phase3/p3_efficientnet_b2.json` (EfficientNet-B2 not in new plan, only B0)
 - [x] `classifier/configs/phase3/p3_resnet18_facecrop_full.json` (ResNet18 full dataset should be Phase 4)
 - [x] `classifier/configs/phase3/p3_resnet18_freqaug.json` (frequency augmentation not in new plan)
 - [x] `classifier/configs/phase3/p3_vit_b16.json` (ViT not in new plan, replaced with ConvNeXt/MobileNet)
 - Note: `p3_efficientnet_b0.json` - REMOVED (will be recreated after Phase2 with correct settings)
 **Source holdout (6):**
 - [x] `classifier/configs/source_holdout/` (entire directory - 6 configs, source holdout not in new plan)
 **Ablation (3):**
 - [x] `classifier/configs/ablation/` (entire directory - 3 configs, ablation studies not in new plan)
 **Configs to KEEP (3):**
 - ✅ `classifier/configs/shared.json`
 - ✅ `classifier/configs/phase1/p1_simplecnn_baseline.json`
 - ✅ `classifier/configs/phase1/p1_resnet18_baseline.json`
 **Phase 2 alias configs removed (8):**
 - [x] `classifier/configs/phase2/p2b_resnet18_128.json` (alias for p1_resnet18_baseline)
 - [x] `classifier/configs/phase2/p2b_simplecnn_128.json` (alias for p1_simplecnn_baseline)
 - [x] `classifier/configs/phase2/p2c_resnet18_nofacecrop.json` (alias for p2b_resnet18_224)
 - [x] `classifier/configs/phase2/p2c_simplecnn_nofacecrop.json` (alias for p2b_simplecnn_224)
 - [x] `classifier/configs/phase2/p2d_resnet18_noaug.json` (alias for p2b_resnet18_224)
 - [x] `classifier/configs/phase2/p2d_simplecnn_noaug.json` (alias for p2b_simplecnn_224)
 - [x] `classifier/configs/phase2/p2e_resnet18_facecrop_only.json` (alias for p2c_resnet18_facecrop)
 - [x] `classifier/configs/phase2/p2e_simplecnn_facecrop_only.json` (alias for p2c_simplecnn_facecrop)
 Note: Comparison pairs (baseline vs treatment) are defined in the analysis notebook as a mapping dict, not as separate config files.
 ---
 ## Phase 1: Architecture Baseline
 ### 1.1 Experiment Configs
 - [x] Create `classifier/configs/phase1/p1_simplecnn_baseline.json`
  - backbone: simple_cnn
  - cnn_preset: medium
  - dropout: 0.0
  - epochs: 15
  - batch_size: 32
  - lr: 1e-4 (consistent with ResNet)
  - weight_decay: 1e-4
  - image_size: 128
  - data_dir: data
  - early_stopping_patience: 5
  - subsample: 0.2
  - face_crop: false
  - augment: false
  - seed: 42
 - [x] Create `classifier/configs/phase1/p1_resnet18_baseline.json`
  - backbone: resnet18
  - pretrained: true
  - epochs: 15
  - batch_size: 32
  - lr: 1e-4
  - weight_decay: 1e-4
  - image_size: 128
  - data_dir: data
  - early_stopping_patience: 5
  - subsample: 0.2
  - face_crop: false
  - augment: false
  - seed: 42
 ### 1.2 Code Updates
 - [x] Implement 5-fold stratified group cross-validation by basename in training pipeline
 - [x] Update `classifier/src/training/trainer.py` to support CV
 - [x] Update `classifier/src/evaluation/evaluate.py` to support CV
 - [x] Ensure all metrics report mean ± std and confidence intervals across folds
 ### 1.3 Training
 - [x] Train SimpleCNN with 5-fold stratified group CV (via pipeline: `python -m pipeline run classifier/configs/phase1/p1_simplecnn_baseline.json`)
 - [x] Train ResNet18 with 5-fold stratified group CV (via pipeline: `python -m pipeline run classifier/configs/phase1/p1_resnet18_baseline.json`)
 - [x] Save all checkpoints and metrics (pipeline automatically fetches outputs to classifier/outputs/)
 ### 1.4 Analysis
 - [x] Use `classifier/notebooks/03_phase1_analysis.ipynb` for Phase 1 analysis
 - [x] Compare SimpleCNN vs ResNet18 performance
 - [x] Overall metrics (AUC, Accuracy, F1) with mean ± std and confidence intervals
 - [x] Per-source metrics (text2img, inpainting, insight)
 - [x] Train/val/test performance curves
 - [x] Confusion matrices
 - [x] Statistical significance testing
 - [x] Generate Grad-CAM visualizations (10-20 images per model)
 - [x] Document conclusions: Which baseline is better and why
 ---
 ## Phase 2: Preprocessing Impact
 ### 2.1 Shortcut Analysis (2A)
 - [x] Create `classifier/configs/phase2/p2a_t1_original.json`
  - backbone: resnet18
  - image_size: 224
  - subsample: 0.2
  - seed: 42
  - augment: false
  - normalization: imagenet
  - data_dir: data
 - [x] Create `classifier/configs/phase2/p2a_t2_real_norm.json`
  - extends: p2a_t1_original.json
  - normalization: real_norm
  - **Normalization**: Calculate mean/std from real training images only within each fold
 - [x] Geometry diagnostic was explored and then removed from the codebase (`src/evaluation/geometry.py` no longer exists):
  - Current pipeline always square-crops before resize, reducing rectangle-vs-square shortcut risk.
  - Shortcut analysis now relies on normalization and held-out-source evidence artifacts.
 - [ ] Train the 2 shortcut configs with 5-fold stratified group CV
 - [ ] Compare results:
  - Standard vs matched-geometry eval for `p2a_t1_original` (letterboxing impact)
  - `p2a_t1_original` vs `p2a_t2_real_norm` (color distribution shortcut)
 - [x] Create `classifier/configs/phase2/p2a_t3_holdout_text2img.json`
  - extends: p2a_t1_original.json
  - train_sources: ["wiki", "inpainting", "insight"]
  - eval_sources: ["wiki", "inpainting", "insight", "text2img"]
 - [x] Create `classifier/configs/phase2/p2a_t3_holdout_inpainting.json`
  - extends: p2a_t1_original.json
  - train_sources: ["wiki", "text2img", "insight"]
  - eval_sources: ["wiki", "text2img", "insight", "inpainting"]
 - [x] Create `classifier/configs/phase2/p2a_t3_holdout_insight.json`
  - extends: p2a_t1_original.json
  - train_sources: ["wiki", "text2img", "inpainting"]
  - eval_sources: ["wiki", "text2img", "inpainting", "insight"]
 - [ ] Train the 3 source holdout configs with 5-fold stratified group CV
 - [ ] Compare held-out source performance vs in-source performance:
  - Calculate AUC for held-out source (text2img, inpainting, insight)
  - Compute Δ (in-source AUC - held-out AUC)
  - If Δ > 0.05-0.10, model is learning source-specific features
 ### 2.2 Resolution Impact (2B)
 - [x] Create `classifier/configs/phase2/p2b_simplecnn_224.json`
  - backbone: simple_cnn
  - image_size: 224
  - subsample: 0.2
  - augment: false
  - seed: 42
  - data_dir: data
 - [x] Create `classifier/configs/phase2/p2b_resnet18_224.json`
  - backbone: resnet18
  - image_size: 224
  - subsample: 0.2
  - augment: false
  - seed: 42
  - data_dir: data
 - [ ] Train both 224 configs with 5-fold stratified group CV
 - [ ] Compare 128×128 vs 224×224 for each model
  - 128 baseline is `p1_*_baseline` (comparison mapping in notebook)
 ### 2.3 Facecrop Impact (2C)
 - [x] Create `classifier/configs/phase2/p2c_simplecnn_facecrop.json`
  - backbone: simple_cnn
  - image_size: 224
  - subsample: 0.2
  - augment: false
  - seed: 42
  - data_dir: cropped/classifier
 - [x] Create `classifier/configs/phase2/p2c_resnet18_facecrop.json`
  - backbone: resnet18
  - image_size: 224
  - subsample: 0.2
  - augment: false
  - seed: 42
  - data_dir: cropped/classifier
 - [ ] Train both facecrop configs with 5-fold stratified group CV
 - [ ] Compare `p2b_resnet18_224` (no facecrop) vs `p2c_resnet18_facecrop` for each model
  - No-facecrop baseline is `p2b_*_224` (comparison mapping in notebook)
 ### 2.4 Augmentation Impact (2D)
 - [x] Create `classifier/configs/phase2/p2d_simplecnn_aug.json`
  - backbone: simple_cnn
  - image_size: 224
  - subsample: 0.2
  - seed: 42
  - augment: {hflip_p: 0.5, rotation_degrees: 10, brightness: 0.2, contrast: 0.2, saturation: 0.1, hue: 0.02, grayscale_p: 0.1, blur_p: 0.1, erase_p: 0.2, noise_p: 0.3, noise_std: 0.04}
  - data_dir: data
 - [x] Create `classifier/configs/phase2/p2d_resnet18_aug.json`
  - backbone: resnet18
  - image_size: 224
  - subsample: 0.2
  - seed: 42
  - augment: {hflip_p: 0.5, rotation_degrees: 10, brightness: 0.2, contrast: 0.2, saturation: 0.1, hue: 0.02, grayscale_p: 0.1, blur_p: 0.1, erase_p: 0.2, noise_p: 0.3, noise_std: 0.04}
  - data_dir: data
 - [ ] Train both augmentation configs with 5-fold stratified group CV
 - [ ] Compare `p2b_resnet18_224` (no aug) vs `p2d_resnet18_aug` for each model
  - No-aug baseline is `p2b_*_224` (comparison mapping in notebook)
 ### 2.5 Augmentation + Facecrop (2E)
 - [x] Create `classifier/configs/phase2/p2e_simplecnn_facecrop_aug.json`
  - backbone: simple_cnn
  - image_size: 224
  - subsample: 0.2
  - seed: 42
  - augment: {hflip_p: 0.5, rotation_degrees: 10, brightness: 0.2, contrast: 0.2, saturation: 0.1, hue: 0.02, grayscale_p: 0.1, blur_p: 0.1, erase_p: 0.2, noise_p: 0.3, noise_std: 0.04}
  - data_dir: cropped/classifier
 - [x] Create `classifier/configs/phase2/p2e_resnet18_facecrop_aug.json`
  - backbone: resnet18
  - image_size: 224
  - subsample: 0.2
  - seed: 42
  - augment: {hflip_p: 0.5, rotation_degrees: 10, brightness: 0.2, contrast: 0.2, saturation: 0.1, hue: 0.02, grayscale_p: 0.1, blur_p: 0.1, erase_p: 0.2, noise_p: 0.3, noise_std: 0.04}
  - data_dir: cropped/classifier
 - [ ] Train both facecrop+aug configs with 5-fold stratified group CV
 - [ ] Compare `p2c_resnet18_facecrop` (facecrop only) vs `p2e_resnet18_facecrop_aug` for each model
  - Facecrop-only baseline is `p2c_*_facecrop` (comparison mapping in notebook)
 ### 2.6 Phase 2 Analysis
 - [ ] Use `classifier/notebooks/04_phase2_analysis.ipynb` for Phase 2 analysis
 - [ ] For each experiment (2A-2E):
  - [ ] Load 5-fold stratified group CV results (mean ± std and confidence intervals)
  - [ ] Generate overall metrics (AUC, Accuracy, F1)
  - [ ] Generate per-source metrics (text2img, inpainting, insight)
  - [ ] Calculate train/val gap
  - [ ] Calculate pairwise source AUC variance (wiki-vs-source AUC variance)
  - [ ] Statistical significance testing vs baseline
  - [ ] Generate comparison visualizations (bar charts, heatmaps)
 - [ ] For 2C (Shortcut Analysis):
  - [ ] Compare original-test vs alternative geometry evidence if reintroduced in a dedicated tool/notebook
  - [ ] Compare ImageNet vs real-image-only normalization (color distribution shortcuts)
  - [ ] Load source holdout results (3 configs)
  - [ ] Calculate held-out source AUC vs in-source AUC for each holdout experiment
  - [ ] Compute Δ (in-source AUC - held-out AUC)
  - [ ] If Δ > 0.05-0.10, model is learning source-specific features
  - [ ] Generate source holdout comparison table
 - [ ] For each model/condition:
  - [ ] Generate Grad-CAM visualizations (10-20 images per condition)
  - [ ] Organize by experiment, prediction type, and source
 - [ ] Answer key questions:
  - [ ] Which preprocessing choices are statistically significant?
  - [ ] Do certain sources benefit more from specific preprocessing?
  - [ ] Is there an interaction between facecrop and augmentation?
  - [ ] Are shortcuts being learned (resolution, color distribution)?
  - [ ] Is the model learning source-specific features (source holdout)?
  - [ ] Does augmentation remove shortcuts or over-regularize?
  - [ ] What features do models focus on (based on Grad-CAM)?
 - [ ] Generate comprehensive metrics comparison table
 - [ ] Use paired fold-wise statistical tests for model comparisons, with bootstrap confidence intervals for key metrics where useful
 - [ ] Provide evidence-based conclusions for each experiment
 - [ ] Provide recommendations for Phase 3 (best preprocessing settings)
 ---
 ## Phase 3: Extended Architecture Exploration
 ### 3.1 Experiment Configs
 Use the best preprocessing choices from Phase 2. The placeholders below assume 224×224, face crop enabled, and no augmentation unless Phase 2 results justify different settings.
 - [x] Create `classifier/configs/phase3/p3_resnet34.json`
  - backbone: resnet34
  - pretrained: true
  - epochs: 15
  - batch_size: 32
  - lr: 1e-4
  - weight_decay: 1e-4
  - image_size: 224
  - augment: false (placeholder until Phase 2 results confirm)
  - subsample: 0.2
  - seed: 42
  - early_stopping_patience: 5
 - [x] Create `classifier/configs/phase3/p3_resnet50.json`
  - backbone: resnet50
  - pretrained: true
  - epochs: 15
  - batch_size: 32
  - lr: 1e-4
  - weight_decay: 1e-4
  - image_size: 224
  - augment: false (placeholder until Phase 2 results confirm)
  - subsample: 0.2
  - seed: 42
  - early_stopping_patience: 5
 - [x] Create `classifier/configs/phase3/p3_efficientnet_b0.json`
  - backbone: efficientnet_b0
  - pretrained: true
  - epochs: 15
  - batch_size: 32
  - lr: 1e-4
  - weight_decay: 1e-4
  - image_size: 224
  - augment: false (placeholder until Phase 2 results confirm)
  - subsample: 0.2
  - seed: 42
  - early_stopping_patience: 5
 - [x] Create `classifier/configs/phase3/p3_convnext_tiny.json`
  - backbone: convnext_tiny
  - pretrained: true
  - epochs: 15
  - batch_size: 32
  - lr: 5e-5 (reduced for ConvNeXt stability)
  - weight_decay: 1e-4
  - image_size: 224
  - augment: false (placeholder until Phase 2 results confirm)
  - subsample: 0.2
  - seed: 42
  - early_stopping_patience: 5
 - [x] Create `classifier/configs/phase3/p3_mobilenetv3_small.json`
  - backbone: mobilenet_v3_small
  - pretrained: true
  - epochs: 15
  - batch_size: 32
  - lr: 1e-4
  - weight_decay: 1e-4
  - image_size: 224
  - augment: false (placeholder until Phase 2 results confirm)
  - subsample: 0.2
  - seed: 42
  - early_stopping_patience: 5
 - [x] Remove `p3a_mobilenet_v3_large.json` (not in plan, MobileNet V3 Large fills no distinct niche)
 ### 3.2 Model Implementation
 - [x] Implement ConvNeXt-Tiny in `classifier/src/models/convnext.py`
 - [x] Implement MobileNetV3-Small in `classifier/src/models/mobilenet.py`
 - [x] Register both models in `classifier/src/models/__init__.py`
 ### 3.3 Training
 - [ ] Train ResNet34 with 5-fold stratified group CV
 - [ ] Train ResNet50 with 5-fold stratified group CV
 - [ ] Train EfficientNet-B0 with 5-fold stratified group CV
 - [ ] Train ConvNeXt-Tiny with 5-fold stratified group CV
 - [ ] Train MobileNetV3-Small with 5-fold stratified group CV
 - [ ] Save all checkpoints and metrics
 ### 3.4 Analysis
 - [ ] Use `classifier/notebooks/05_phase3_analysis.ipynb` for Phase 3 analysis
 - [ ] Load 5-fold stratified group CV results for all models (mean ± std and confidence intervals)
 - [ ] Generate overall metrics for each model
 - [ ] Generate per-source metrics for each model
 - [ ] Compare with Phase 1 baselines (ResNet18, SimpleCNN)
 - [ ] Statistical significance testing vs baselines
 - [ ] Generate Grad-CAM visualizations for top models (10-20 images each)
 - [ ] Parameter count vs performance analysis
 - [ ] Conclusions: Which architectures work best and why
 ---
 ## Phase 4: Final Analysis on Best Models
 ### 4.1 Select Top Models
 - [ ] Based on Phases 1-3 results, select top 3-4 models
 - [ ] Document selection criteria (e.g., top AUC, balanced performance, efficiency)
 ### 4.2 Data Quantity Scaling (4A)
 - [ ] For each selected model, create configs for different data sizes:
  - [ ] `classifier/configs/phase4/p4a_<model>_20pct.json` (subsample: 0.2)
  - [ ] `classifier/configs/phase4/p4a_<model>_50pct.json` (subsample: 0.5)
  - [ ] `classifier/configs/phase4/p4a_<model>_100pct.json` (subsample: 1.0)
 - [ ] In every 4A config, explicitly set the best Phase 2 preprocessing choices:
  - image_size: best from Phase 2A
  - face_crop: best from Phase 2B/E
  - augment: best from Phase 2D/E
 - [ ] Train each model with 5-fold stratified group CV at all three data sizes
 - [ ] Compare how each model scales with more data
 ### 4.3 Full Dataset Evaluation (4B)
 - [ ] For each selected model, create config for full dataset:
  - `classifier/configs/phase4/p4b_<model>_full.json` (subsample: 1.0)
 - [ ] In every 4B config, explicitly set the same best Phase 2 preprocessing choices used in 4A
 - [ ] Train each model on full dataset with 5-fold stratified group CV
 - [ ] Generate detailed per-source metrics
 - [ ] Generate Grad-CAM visualizations (10-20 images each)
 - [ ] Perform hard example analysis (false positives/negatives) with visualizations
 - [ ] Generate confidence distribution histograms
 - [ ] Cross-validation results (mean ± std with confidence intervals)
 ### 4.4 Analysis
 - [ ] Use `classifier/notebooks/06_phase4_analysis.ipynb` for Phase 4 analysis
 - [ ] Load data quantity scaling results
 - [ ] Load full dataset evaluation results
 - [ ] Generate comprehensive metrics comparison table
 - [ ] Generate per-source metrics for final models
 - [ ] Generate Grad-CAM galleries for final models
 - [ ] Perform hard example analysis with visualizations
 - [ ] Generate confidence distribution histograms
 - [ ] Final model comparison and selection
 - [ ] Conclusions and recommendations
 ---
 ## Notebooks and Analysis
 This section is the consolidated notebook checklist for the notebooks referenced in the phase sections above; do not create duplicate notebooks for the same phase.
 ### 5.1 Exploratory Data Analysis
 - [x] Create `classifier/notebooks/01_eda.ipynb`
 - [x] Dataset overview (real vs fake distribution, sources)
 - [x] Image resolution/aspect ratio analysis (identify potential shortcuts)
 - [x] Color distribution analysis (identify potential shortcuts)
 - [x] Sample visualization from each source
 - [x] Statistical summary of the dataset
 - [x] Data quality checks
 ### 5.2 Preprocessing Pipeline
 - [x] Create `classifier/notebooks/02_preprocessing.ipynb`
 - [x] Square crop and resize implementation demonstration
 - [x] Face crop (MTCNN) demonstration and effectiveness analysis
 - [x] Augmentation pipeline visualization (before/after examples)
 - [x] Z-score normalization comparison (ImageNet vs real-image-only)
 - [x] Data split verification (group-aware by basename, no overlap)
 - [x] Preprocessing impact visualization
 ### 5.3 Phase 1 Analysis
 - [x] Create `classifier/notebooks/03_phase1_analysis.ipynb`
 - [x] Load Phase 1 training results
 - [x] Generate 5-fold stratified group CV results (mean ± std with confidence intervals)
 - [x] Generate per-source metrics for each model
 - [x] Generate train/val/test performance curves
 - [x] Generate confusion matrices
 - [x] Perform statistical significance testing between models
 - [x] Generate Grad-CAM visualizations (10-20 images each)
 - [x] Document conclusions: Which baseline is better and why
 ### 5.4 Phase 2 Analysis
 - [x] Create `classifier/notebooks/04_phase2_analysis.ipynb`
 - [ ] Load all Phase 2 experiment results
 - [ ] For each experiment (2A-2E):
  - [ ] Generate 5-fold stratified group CV results (mean ± std with confidence intervals)
  - [ ] Generate overall metrics
  - [ ] Generate per-source metrics
  - [ ] Calculate train/val gap
  - [ ] Calculate pairwise source AUC variance (wiki-vs-source AUC variance)
  - [ ] Perform statistical significance testing
 - [ ] Generate comparison tables across all Phase 2 experiments
 - [ ] Generate comparison visualizations (bar charts, heatmaps)
 - [ ] For each model/condition, generate Grad-CAM visualizations (10-20 images)
 - [ ] Organize visualizations by experiment, model, prediction type, and source
 - [ ] Answer key analysis questions
 - [ ] Generate comprehensive metrics comparison table
 - [ ] Provide evidence-based conclusions for each experiment
 - [ ] Provide recommendations for Phase 3
 ### 5.5 Phase 3 Analysis
 - [ ] Create `classifier/notebooks/05_phase3_analysis.ipynb`
 - [ ] Load Phase 3 training results
 - [ ] Generate 5-fold stratified group CV results for each model (mean ± std with confidence intervals)
 - [ ] Generate per-source metrics for each model
 - [ ] Compare with Phase 1 baselines (ResNet18, SimpleCNN)
 - [ ] Perform statistical significance testing vs baselines
 - [ ] Generate Grad-CAM visualizations for top models (10-20 images each)
 - [ ] Parameter count vs performance analysis
 - [ ] Conclusions: Which architectures work best and why
 ### 5.6 Phase 4 Analysis
 - [ ] Create `classifier/notebooks/06_phase4_analysis.ipynb`
 - [ ] Load data quantity scaling results
 - [ ] Load full dataset evaluation results
 - [ ] Generate comprehensive metrics comparison table
 - [ ] Generate per-source metrics for final models
 - [ ] Generate Grad-CAM galleries for final models
 - [ ] Perform hard example analysis with visualizations
 - [ ] Generate confidence distribution histograms
 - [ ] Final model comparison and selection
 - [ ] Conclusions and recommendations
 ### 5.7 Grad-CAM Deep Dive (Optional)
 - [ ] Create `classifier/notebooks/07_gradcam_deep_dive.ipynb`
 - [ ] Load Grad-CAM results from all phases
 - [ ] Comprehensive Grad-CAM analysis across all phases and models
 - [ ] Feature visualization for different model architectures
 - [ ] CNN vs EfficientNet vs ConvNeXt comparison
 - [ ] What regions do different architectures focus on?
 - [ ] Are there systematic differences in attention patterns?
 - [ ] Evidence of shortcut removal analysis across phases
 - [ ] Temporal analysis: does model attention change with different preprocessing?
 - [ ] Generate visual explanations suitable for presentation
 ---
 ## Code Implementation Tasks
 ### Cross-Validation Implementation
 - [x] Update `classifier/src/training/trainer.py` to support 5-fold stratified group CV by basename
 - [x] Update `classifier/src/evaluation/evaluate.py` to support grouped CV splits
 - [x] Implement metric aggregation across folds (mean ± std)
 - [x] Ensure all metrics report confidence intervals
 - [x] Reuse the same fold assignments for comparable experiments so paired statistical tests are valid
 - [x] Rename `classifier/run_cv.py` to `classifier/run.py` (pipeline expects classifier/run.py)
 - [x] Rename `classifier/run_cv.py` to `classifier/run.py` (pipeline expects classifier/run.py)
 ### Model Implementations
 - [x] Implement ConvNeXt-Tiny in `classifier/src/models/convnext.py`
 - [x] Implement MobileNetV3-Small in `classifier/src/models/mobilenet.py`
 - [x] Register both models in `classifier/src/models/__init__.py`
 ### Normalization Implementation
 - [ ] Implement function to calculate mean/std from real training images only
 - [ ] Update `classifier/src/preprocessing/pipeline.py` to support custom normalization stats
 - [ ] Test ImageNet normalization vs real-image-only normalization
 ### Evaluation Improvements
 - [ ] Ensure test set uses `train=False` to disable augmentation
 - [ ] Ensure diagnostic evaluation transforms never change the training data
 - [ ] Verify CV fold assignments are identical across comparable experiments (same seed and basename grouping)
 - [ ] Implement per-source metrics with detection rate and false alarm rate
 - [ ] Implement pairwise AUC calculations
 - [ ] Implement train/val gap calculations
 - [ ] Implement pairwise source AUC variance calculations
 ### Grad-CAM Improvements
 - [x] Ensure Grad-CAM works for all model types (CNN-based)
 - [x] Implement Grad-CAM for ConvNeXt (last Conv2d found automatically by `find_conv()`)
 - [x] Implement Grad-CAM for MobileNetV3 (last Conv2d found automatically by `find_conv()`)
 - [ ] Organize Grad-CAM outputs by experiment, model, prediction type, source
 ---
 ## Final Report Preparation
 - [ ] Compile results from all phases
 - [ ] Create presentation slides (PDF format)
 - [ ] Brief description of deep learning solutions (discriminative + generative)
 - [ ] Description of implementation steps and improvements
  - [ ] Motivate choices for architecture, training strategy, etc.
  - [ ] Show intermediate results
  - [ ] Interpret results and what changed
  - [ ] What was decided to improve results
 - [ ] Classification performance results
  - [ ] Experimental setup
  - [ ] Train/val/test splits
  - [ ] Performance metrics chosen
 - [ ] Data generation performance results
  - [ ] Experimental setup
  - [ ] Performance metrics chosen
 - [ ] Discussion and conclusions
  - [ ] Comments on performance
  - [ ] Final remarks
 - [ ] Fill auto-evaluation file
 ---
 ## Summary
 Total tasks: ~150+
 This implementation plan covers:
 - ✅ All 4 phases with comprehensive experiments
 - ✅ 5-fold stratified group cross-validation for all experiments
 - ✅ 7 analysis notebooks for robust validation
 - ✅ Shortcut analysis (resolution/ratio + color distribution + source holdout)
 - ✅ Source holdout experiments to detect source-specific feature learning
 - ✅ Grad-CAM visualizations for explainability
 - ✅ Statistical analysis with confidence intervals
 - ✅ Per-source metrics for all experiments
 - ✅ Data quantity scaling analysis
 - ✅ Full dataset evaluation on best models
 - ✅ Comprehensive documentation and reporting
 **Key Features:**
 - Reproducible experiments with fixed seeds
 - Stratified group CV keeps basename groups together while balancing class distribution
 - Multiple shortcut analyses to prevent model cheating (resolution, color, source-specific)
 - Source holdout experiments to test generalization to unseen sources
 - Grad-CAM for explainability
 - Statistical rigor with confidence intervals
 - Per-source analysis to understand model behavior
 - Clear progression from baselines -> preprocessing -> architectures -> final evaluation
@@ -1,449 +0,0 @@
 # Classifier Reorganization Plan (v2)
 ## Analysis of Current Phasing Issues
 Your current phasing has several problems that make it difficult to present a rigorous, explainable report:
 ### Current Problems
 1. **Inconsistent comparison conditions**:
   - SimpleCNN uses lr=1e-3, ResNet uses lr=1e-4
   - SimpleCNN trains 20 epochs (no ES), ResNet18 trains 15 epochs (with ES)
   - Makes direct comparisons invalid
 2. **No cross-validation**:
   - Only a single 80/10/10 split
   - Results may be split-dependent
   - No confidence intervals on metrics
 3. **Augmentation testing is incomplete**:
   - Only tested on ResNet18 (Phase 3), not across architectures
   - Performance drop could mean: (a) removing shortcuts (good) or (b) over-regularization (bad)
   - No way to distinguish these cases
 4. **Facecrop impact not generalized**:
   - Only ResNet18 tested with facecrop
   - Don't know if EfficientNet or ViT benefit similarly
 5. **Full dataset only on one model**:
   - Only ResNet18 tested on full dataset
   - Don't know if data quantity helps all models equally
 6. **Test set integrity**:
   - Need to verify test set uses original images (no augmentation, no preprocessing or minimal if really necessary)
   - Need to ensure same train/val/test splits across all model comparisons
   - Need central config for shared parameters across phases
 ---
 ## Recommended Reorganization
 I suggest reorganizing into **4 phases** with clear, isolated variables. All phases use **5-fold stratified cross-validation** as standard practice to ensure balanced class distribution across folds.
 ### Phase 1: Controlled Baseline Comparison
 **Goal**: Compare simple architectures under identical conditions to establish baselines
 **Fixed conditions for ALL models**:
 - Data: 20% subsample
 - Resolution: 128×128
 - No face crop
 - No augmentation
 - Optimizer: AdamW (lr=1e-4, weight_decay=1e-4)
 - Scheduler: CosineAnnealingLR (T_max=15)
 - Epochs: 15 with early stopping (patience=5)
 - Batch size: 32
 - 5-fold stratified cross-validation (report mean ± std)
 | Model | Params | Expected AUC (mean ± std) |
 |-------|--------|---------------------------|
 | SimpleCNN | ~400k | ? |
 | ResNet18 | ~11.7M | ? |
 **This gives you**: Clean, comparable baseline for simple architectures with confidence intervals
 **These same 2 models will be used in Phase 2 for preprocessing experiments.**
 ---
 ### Phase 2: Preprocessing Impact (Same 2 Models from Phase 1)
 **Goal**: Test each preprocessing change on the SAME 2 models from Phase 1
 **Experimental questions**:
 - Does higher resolution improve performance?
 - Does face cropping improve performance?
 - Does augmentation improve or hurt performance?
 - Does augmentation interact with face cropping?
 - Is the model learning any shortcuts (e.g., resolution differences, aspect ratios, etc.)?
 #### 2A: Shortcut Analysis
 **Goal**: Establish whether the baseline model exploits geometry, colour, or source-specific shortcuts before drawing any conclusions from preprocessing experiments.
 **Test 1: Resolution/Ratio Shortcuts (Letterboxing)**
 - Train on original images (real=rectangular, fake=square); evaluate the same checkpoint under standard crop vs letterbox-padded real images to confirm geometry is or is not a discriminative cue
 - Models: **ResNet18**
 - Data: 20% subsample
 - 5-fold stratified CV (balanced class distribution)
 - Resolution: 224×224
 - No facecrop, no augmentation
 | Experiment | AUC | Train/Val Gap | Per-Source AUC Variance |
 |------------|-----|---------------|-------------------------|
 | Original images (standard eval) | ? | ? | ? |
 | Matched geometry (letterboxed real images) | ? | ? | ? |
 **Test 2: Color Distribution Shortcuts**
 - Compare: Train with ImageNet normalization stats vs real-image-only normalization stats
 - Models: **ResNet18**
 - Data: 20% subsample
 - 5-fold stratified CV (balanced class distribution)
 - Resolution: 224×224
 - No facecrop, no augmentation
 - ImageNet stats: mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)
 - Real-image stats: Calculate mean/std from real training images only, apply to all
 | Experiment | AUC | Train/Val Gap | Per-Source AUC Variance |
 |------------|-----|---------------|-------------------------|
 | ImageNet normalization | ? | ? | ? |
 | Real-image-only normalization | ? | ? | ? |
 **Test 3: Source-Specific Feature Learning (Source Holdout)**
 - Compare: Train on all sources vs train with one source held out
 - Models: **ResNet18**
 - Data: 20% subsample
 - 5-fold stratified CV (balanced class distribution)
 - Resolution: 224×224
 - No facecrop, no augmentation
 - Hold out each fake source (text2img, inpainting, insight) separately
 | Experiment | Held-out Source | Train Sources | Held-out AUC | In-Source AUC | Δ (In-Source - Held-out) |
 |------------|-----------------|---------------|--------------|---------------|--------------------------|
 | Baseline | None | All | - | ? | - |
 | Holdout text2img | text2img | wiki, inpainting, insight | ? | ? | ? |
 | Holdout inpainting | inpainting | wiki, text2img, insight | ? | ? | ? |
 | Holdout insight | insight | wiki, text2img, inpainting | ? | ? | ? |
 **Interpretation**: If held-out source AUC is significantly lower than in-source AUC (Δ > 0.05-0.10), the model is learning source-specific features. If AUC drop under matched geometry is significant, the model exploits aspect-ratio as a shortcut — this must be known before interpreting resolution or facecrop results.
 #### 2B: Resolution Impact (no facecrop, no augmentation)
 - Test: 128×128 vs 224×224
 - Models: **SimpleCNN, ResNet18**
 - Data: 20% subsample
 - 5-fold stratified CV (balanced class distribution)
 | Model | 128×128 AUC | 224×224 AUC | Δ |
 |-------|-------------|-------------|---|
 | SimpleCNN | ? | ? | ? |
 | ResNet18 | ? | ? | ? |
 #### 2C: Facecrop Impact (224×224, no augmentation)
 - Test: No facecrop vs MTCNN facecrop
 - Models: **SimpleCNN, ResNet18**
 - Data: 20% subsample
 - 5-fold stratified CV (balanced class distribution)
 | Model | No Facecrop AUC | Facecrop AUC | Δ |
 |-------|-----------------|--------------|---|
 | SimpleCNN | ? | ? | ? |
 | ResNet18 | ? | ? | ? |
 #### 2D: Augmentation Impact (224×224, without facecrop)
 - Test: No augmentation vs augmentation
 - Models: **SimpleCNN, ResNet18**
 - Data: 20% subsample
 - 5-fold stratified CV (balanced class distribution)
 - **Verify test set has no augmentation** (code inspection of `get_transforms(train=False, ...)`)
 - **Analyze shortcut removal**: Compare train/val gaps and per-source AUC balance
 | Model | No Aug AUC | With Aug AUC | Δ | Train/Val Gap (No Aug) | Train/Val Gap (With Aug) |
 |-------|------------|--------------|---|------------------------|--------------------------|
 | SimpleCNN | ? | ? | ? | ? | ? |
 | ResNet18 | ? | ? | ? | ? | ? |
 **Experimental question**: Does augmentation without facecrop improve or hurt performance?
 #### 2E: Augmentation + Facecrop Combined (224×224)
 - Test: Facecrop only vs Facecrop + augmentation
 - Models: **SimpleCNN, ResNet18**
 - Data: 20% subsample
 - 5-fold stratified CV (balanced class distribution)
 - **Analyze shortcut removal**: Compare train/val gaps and per-source AUC balance
 | Model | Facecrop Only AUC | Facecrop + Aug AUC | Δ | Train/Val Gap (Only) | Train/Val Gap (With Aug) |
 |-------|-------------------|--------------------|---|----------------------|--------------------------|
 | SimpleCNN | ? | ? | ? | ? | ? |
 | ResNet18 | ? | ? | ? | ? | ? |
 **Experimental question**: Does augmentation with facecrop improve or hurt performance compared to facecrop alone?
 **This gives you**:
 - Isolated impact of each preprocessing choice on SimpleCNN and ResNet18
 - Verification that the model is not learning shortcuts
 - Understanding of how augmentation interacts with face cropping
 - Shortcut removal analysis through train/val gap and per-source AUC metrics
 ---
 ### Phase 3: Extended Architecture Exploration
 **Goal**: Test additional architectures to find the best performing models
 **Fixed conditions** (based on best findings from Phase 2):
 - Data: 20% subsample
 - Resolution: Best from Phase 2A (likely 224×224)
 - Facecrop: Best from Phase 2B/E (likely Yes)
 - Augmentation: Best from Phase 2D/E (depends on experimental results)
 - Optimizer: AdamW (lr=1e-4, weight_decay=1e-4)
 - Scheduler: CosineAnnealingLR (T_max=15)
 - Epochs: 15 with early stopping (patience=5)
 - Batch size: 32
 - 5-fold stratified cross-validation (balanced class distribution)
 | Model | Params | Rationale |
 |-------|--------|-----------|
 | ResNet34 | ~21.8M | Deeper ResNet - test if more capacity helps |
 | ResNet50 | ~25.6M | Even deeper with bottleneck blocks |
 | EfficientNet-B0 | ~4.0M | Efficient compound scaling |
 | ConvNeXt-Tiny | ~29M | Modern CNN, different architecture family |
 | MobileNetV3-Small | ~2.5M | Lightweight efficiency comparison |
 **This gives you**: Extended architecture exploration to identify top-performing models for Phase 4
 - ResNet depth progression (18 -> 34 -> 50)
 - Efficient architectures (EfficientNet-B0, MobileNetV3-Small)
 - Modern CNN with different inductive bias (ConvNeXt-Tiny)
 - Size range (2.5M to 29M parameters)
 ---
 ### Phase 4: Final Analysis on Best Models
 **Goal**: Comprehensive evaluation of top-performing models from Phases 1-3
 **Select top 3-4 models** based on Phase 1-3 results (e.g., ResNet18, ResNet34, EfficientNet-B0, ConvNeXt-Tiny)
 #### 4A: Data Quantity Scaling
 Test how each best model scales with more data:
 | Model | 20% Data AUC | 50% Data AUC | 100% Data AUC | Δ (100% - 20%) |
 |-------|--------------|--------------|---------------|----------------|
 | Model 1 | ? | ? | ? | ? |
 | Model 2 | ? | ? | ? | ? |
 | Model 3 | ? | ? | ? | ? |
 | Model 4 | ? | ? | ? | ? |
 **Fixed conditions**:
 - Resolution: Best from Phase 2A
 - Facecrop: Best from Phase 2B/E
 - Augmentation: Best from Phase 2D/E
 - 5-fold stratified cross-validation (balanced class distribution)
 #### 4B: Comprehensive Evaluation on Full Dataset
 - Train best models on **full dataset** (100%)
 - Detailed per-source metrics (text2img, inpainting, insight)
 - Grad-CAM visualizations for explainability
 - Hard example analysis (false positives/negatives)
 - Confidence distribution analysis
 - Cross-validation results (mean ± std)
 **This gives you**: Final, comprehensive evaluation of the best models with full explainability
 ---
 ### Notebooks and Analysis
 **Goal**: Use Jupyter notebooks for comprehensive analysis and validation of each phase
 #### **01_eda.ipynb** - Exploratory Data Analysis
 - Dataset overview (real vs fake distribution, sources)
 - Image resolution/aspect ratio analysis (identify potential shortcuts)
 - Color distribution analysis (identify potential shortcuts)
 - Sample visualization from each source (text2img, inpainting, insight, wiki)
 - Statistical summary of the dataset
 - Data quality checks
 #### **02_preprocessing.ipynb** - Preprocessing Pipeline
 - Square crop and resize implementation demonstration
 - Face crop (MTCNN) demonstration and effectiveness analysis
 - Augmentation pipeline visualization (before/after examples)
 - Z-score normalization comparison (ImageNet vs real-image-only)
 - Data split verification (group-aware by basename, no overlap)
 - Preprocessing impact visualization
 #### **03_phase1_analysis.ipynb** - Phase 1: Architecture Baseline
 - SimpleCNN vs ResNet18 comparison
 - 5-fold stratified CV results (mean ± std with confidence intervals)
 - Per-source metrics for each model (text2img, inpainting, insight)
 - Train/val/test performance curves across epochs
 - Confusion matrices for each model
 - Statistical significance testing between models
 - Grad-CAM visualizations for both models (10-20 images each)
 - Conclusions: Which baseline is better and why
 #### **04_phase2_analysis.ipynb** - Phase 2: Preprocessing Impact
 - **2A**: Resolution impact (128×128 vs 224×224)
 - **2B**: Facecrop impact
 - **2C**: Shortcut analysis (resolution/ratio + color distribution)
 - **2D**: Augmentation impact (without facecrop)
 - **2E**: Augmentation + facecrop combined
 For each experiment:
 - 5-fold CV results (mean ± std with confidence intervals)
 - Per-source metrics (text2img, inpainting, insight)
 - Statistical significance testing vs baseline
 - Comparison tables across all Phase 2 experiments
 - Grad-CAM visualizations (10-20 images per condition)
 - Analysis of train/val gap changes
 - Analysis of per-source AUC variance changes
 **Overall Phase 2 conclusions**:
 - Which preprocessing choices work best and why
 - Are shortcuts being learned (resolution, color distribution)?
 - Does augmentation remove shortcuts or over-regularize?
 - Recommendations for Phase 3 (best preprocessing settings)
 #### **05_phase3_analysis.ipynb** - Phase 3: Extended Architecture Exploration
 - ResNet34, ResNet50, EfficientNet-B0, ConvNeXt-Tiny, MobileNetV3-Small
 - 5-fold CV results (mean ± std) for each model
 - Per-source metrics for each model
 - Comparison with Phase 1 baselines (ResNet18, SimpleCNN)
 - Statistical significance testing vs baselines
 - Grad-CAM visualizations for top models (10-20 images each)
 - Parameter count vs performance analysis
 - Conclusions: Which architectures work best and why
 #### **06_phase4_analysis.ipynb** - Phase 4: Final Analysis
 - **4A**: Data quantity scaling (20%, 50%, 100%) on top 3-4 models
 - **4B**: Comprehensive evaluation on full dataset
 - Detailed per-source metrics for final models
 - Grad-CAM visualizations for final models (10-20 images each)
 - Hard example analysis (false positives/negatives) with visualizations
 - Confidence distribution analysis (histograms)
 - Cross-validation results (mean ± std with confidence intervals)
 - Final model comparison and selection
 - Conclusions and recommendations
 #### **07_gradcam_deep_dive.ipynb** - Grad-CAM Deep Dive (optional)
 - Comprehensive Grad-CAM analysis across all phases and models
 - Feature visualization for different model architectures (CNN vs EfficientNet vs ConvNeXt)
 - Comparison of what different models focus on (face regions, backgrounds, artifacts)
 - Evidence of shortcut removal (or lack thereof) across phases
 - Temporal analysis: does model attention change with different preprocessing?
 - Visual explanations suitable for presentation
 **Notebook requirements**:
 - Each notebook should be self-contained and reproducible
 - Include statistical analysis with confidence intervals
 - Generate publication-ready visualizations
 - Address all experimental questions and hypotheses
 - Provide clear conclusions for each phase
 - Use consistent formatting and style across all notebooks
 - Save all results (metrics, figures, tables) for easy reference
 ---
 ## Key Improvements
 ### 1. Stratified Cross-Validation Implementation
 ```python
 # Use sklearn's StratifiedKFold to ensure balanced class distribution across folds
 from sklearn.model_selection import StratifiedKFold
 skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
 for fold, (train_idx, val_idx) in enumerate(skf.split(X, y)):
    # Train on train_idx, validate on val_idx
    # Store metrics per fold
 ```
 ### 2. Augmentation Shortcut Removal Analysis (Phase 2D/2E)
 Track these metrics with/without augmentation:
 | Metric | Without Aug | With Aug | Interpretation |
 |--------|-------------|----------|----------------|
 | Train AUC | 0.99 | 0.95 | ↓ Expected |
 | Val AUC | 0.90 | 0.89 | ↓ Slight |
 | **Train/Val Gap** | **0.09** | **0.06** | **↓ Good!** |
 | text2img AUC | 0.98 | 0.96 | ↓ Slight |
 | InsightFace AUC | 0.82 | 0.85 | **↑ Good!** |
 | **AUC Variance** | **0.08** | **0.06** | **↓ Good!** |
 **Interpretation**: If train/val gap ↓ AND per-source AUC variance ↓, augmentation is removing shortcuts.
 ### 3. Consistent Hyperparameters
 - Same lr for all models (1e-4 is safe for pretrained, may need adjustment for SimpleCNN)
 - Same epochs, ES patience, batch size
 - Only vary the architecture being tested
 ### 4. Test Set Integrity and Reproducibility
 **Test set from original source**:
 - Verify that test set uses original images with minimal preprocessing
 - Test set should use `get_transforms(train=False, ...)` to disable augmentation
 - Ensure test images are not preprocessed in a way that could affect model comparisons
 **Reproducible splits across models**:
 - The code already uses `cfg.get("seed", 42)` for reproducible splits
 - All experiments should use the same seed (42) to ensure identical train/val/test splits
 - This ensures fair comparison between models
 **Central config for shared parameters**:
 - Create a central config file (`classifier/configs/shared.json`) with parameters common across all phases
 - This includes: seed, val_ratio, test_ratio, batch_size, optimizer settings, etc.
 - Individual experiment configs can override these defaults
 Example shared config:
 ```json
 {
  "seed": 42,
  "val_ratio": 0.1,
  "test_ratio": 0.1,
  "batch_size": 32,
  "optimizer": {
    "type": "adamw",
    "lr": 1e-4,
    "weight_decay": 1e-4
  },
  "scheduler": {
    "type": "cosine_annealing",
    "T_max": 15
  },
  "early_stopping_patience": 5,
  "num_workers": 4
 }
 ```
 ---
 ## Summary Table for Report
 | Phase | Variable Tested | Models | Data | Resolution | Facecrop | Augment | CV |
 |-------|-----------------|--------|------|------------|----------|---------|----|
 | 1 | Architecture Baseline | SimpleCNN, ResNet18 | 20% | 128 | No | No | 5-fold stratified |
 | 2A | Shortcut Analysis | ResNet18 | 20% | 224 | No | No | 5-fold stratified |
 | 2A-Holdout | Source Holdout | ResNet18 | 20% | 224 | No | No | 5-fold stratified |
 | 2B | Resolution | SimpleCNN, ResNet18 | 20% | 128/224 | No | No | 5-fold stratified |
 | 2C | Facecrop | SimpleCNN, ResNet18 | 20% | 224 | ± | No | 5-fold stratified |
 | 2D | Augmentation (no facecrop) | SimpleCNN, ResNet18 | 20% | 224 | No | ± | 5-fold stratified |
 | 2E | Augmentation + Facecrop | SimpleCNN, ResNet18 | 20% | 224 | Yes | ± | 5-fold stratified |
 | 3 | Extended Architectures | ResNet34, ResNet50, EffNet-B0, ConvNeXt-Tiny, MobileNetV3-Small | 20% | Best | Best | Best | 5-fold stratified |
 | 4A | Data Quantity | Top 3-4 models | 20/50/100% | Best | Best | Best | 5-fold stratified |
 | 4B | Final Evaluation | Top 3-4 models | 100% | Best | Best | Best | 5-fold stratified |
 This structure gives you:
 - ✅ Identical comparison conditions across all phases
 - ✅ 5-fold stratified cross-validation with confidence intervals (ensures balanced class distribution)
 - ✅ Same 2 baseline models (SimpleCNN, ResNet18) tested across all preprocessing variations (Phase 2)
 - ✅ Shortcut analysis to verify no bias (Phase 2C)
 - ✅ Experimental questions about augmentation impact (Phase 2D/2E)
 - ✅ Shortcut removal analysis via train/val gap and per-source AUC metrics
 - ✅ Facecrop tested on baseline models (Phase 2B)
 - ✅ Extended architecture exploration with proven models (Phase 3)
 - ✅ Final comprehensive analysis on best models (Phase 4)
 - ✅ Data quantity scaling on multiple best models (Phase 4A)
 - ✅ Clear, isolated variables per phase
 - ✅ Explainable progression for report
 **Key Experimental Questions in Phase 2**:
 - **2C (Shortcut Analysis)**: Is the model learning any shortcuts (e.g., resolution differences, aspect ratios, etc.)?
 - **2D (Augmentation without facecrop)**: Does augmentation improve or hurt performance?
 - **2E (Augmentation with facecrop)**: Does augmentation improve or hurt performance compared to facecrop alone?
@@ -1,279 +0,0 @@
 # Generator Plan
 The assignment rewards *iterative improvement with intermediate results*. This plan is structured around **model evolution as the spine**: each step has a *because* tied to an observed failure of the previous step. Pipeline ablations are honest but de-emphasized — they clear the table for the real story.
 ---
 ## Standard Settings (Applied Everywhere Unless Noted)
 | Setting | Value | Reason |
 |---------|-------|--------|
 | Batch size | 64 | Consistent across experiments |
 | Mixed precision | float16 + GradScaler | Speed |
 | EMA decay | 0.9999 | Sample from EMA weights for GANs |
 | FID evaluation | Every 25 epochs | Objective quality tracking |
 | FID n_real | 5000 | Held-out real images |
 | Default epochs | 100 | Best-of-each in Phase 4 retrains to 200 |
 Per-model optimizer/hyperparameters are listed inside each phase.
 ---
 ## Phase 1 — Pipeline Selection *(quick, one figure)*
 **Goal**: Pick the data pipeline used for every downstream experiment. Don't dwell here — this is clearing the table, not the story.
 Fixed model: **DCGAN at 64×64** (cheapest baseline, fast iteration). One variable per experiment.
 | Experiment | Variable | Variants | Decision |
 |---|---|---|---|
 | 1A | Resolution | 64×64 vs 128×128 | Pick by FID — assumed transferable |
 | 1B | Face crop + alignment | Full image vs MTCNN-aligned | Pick by FID — assumed transferable |
 | 1C | Augmentation | H-flip only vs H-flip + rotation ±5° + mild color jitter | Per-family: validate inside Phase 2 for GAN, default to H-flip-only for VAE/DDPM |
 | 1D | Combined dataset | Aligned only vs aligned + raw mixed | Pick by FID — expected to underperform aligned-only |
 **Caveat on transferability**: Phase 1 uses DCGAN as a proxy to choose the pipeline cheaply, then assumes the choice transfers to VAE and DDPM. Resolution and alignment are largely architecture-invariant (more pixels help everyone; structural consistency helps any spatial prior). Augmentation is *not* — diffusion models benefit less from aug, and MSE-VAE may even be hurt by color jitter. So 1C is treated as an **indicative** result for GANs and re-checked per family rather than baked in globally.
 **1D — combined dataset rationale**: Mixing aligned + raw doubles the variance the generator must model (face anywhere/any scale + face fixed) and dilutes the geometric prior. Hypothesis: combined < aligned-only. Cheap to test (one extra DCGAN run). Included for completeness so the report shows we considered it rather than asserting it.
 **MTCNN alignment** (one-time preprocessing, cached to disk):
 ```python
 from facenet_pytorch import MTCNN
 from skimage.transform import SimilarityTransform, warp
 import numpy as np
 from PIL import Image
 mtcnn = MTCNN(keep_all=False, device='cuda')
 REF_LANDMARKS = np.array([   # reference positions in 128×128
    [38.0, 51.0],  # left eye
    [90.0, 51.0],  # right eye
    [64.0, 71.0],  # nose
    [45.0, 95.0],  # left mouth
    [83.0, 95.0],  # right mouth
 ], dtype=np.float32)
 def align_face(img: Image.Image, out_size: int = 128):
    boxes, _, landmarks = mtcnn.detect(img, landmarks=True)
    if boxes is None:
        return None
    tform = SimilarityTransform()
    tform.estimate(landmarks[0], REF_LANDMARKS)
    aligned = warp(np.array(img), tform.inverse,
                   output_shape=(out_size, out_size),
                   order=3, preserve_range=True).astype(np.uint8)
    return Image.fromarray(aligned)
 ```
 **Augmentation philosophy** — only structure-preserving transforms (face-aligned crops are consistent by design):
 | Transform | Apply? | Reason |
 |---|---|---|
 | Horizontal flip | Yes, p=0.5 | Faces are symmetric |
 | Rotation | Yes, ±5° | Residual head tilt post-alignment |
 | Color jitter | Yes, mild | brightness ±0.1, contrast ±0.1, saturation ±0.05 |
 | Translation | No | Breaks alignment |
 | Vertical flip | No | Meaningless for faces |
 | Strong blur / noise | No | Teaches the model to generate blur |
 **Output**: ~1 page in the report. Best pipeline carries forward to all phases.
 ---
 ## Phase 2 — GAN Evolution *(main spine)*
 **Goal**: The richest narrative — each step has a clear *because* from observed failure. This is the strongest part of the storyline; keep it front and center.
 Best pipeline from Phase 1 fixed throughout.
 ---
 ### 2.1 — DCGAN *(baseline)*
 Simplest GAN baseline. BCE loss, no gradient penalty.
 - Adam β1=0.5, β2=0.999, lr=2e-4
 - ngf=ndf=64, latent_dim=100
 - Resolution: 64×64
 **Expected failure**: mode collapse, training instability, oscillating losses. Document these explicitly — they motivate 2.2.
 ---
 ### 2.2 — WGAN-GP
 **Because**: DCGAN showed mode collapse and instability → Wasserstein loss + gradient penalty.
 - Adam β1=0.0, β2=0.9, lr_g=lr_d=1e-4
 - ngf=ndf=64, latent_dim=128, n_critic=2, gp_lambda=10
 - Resolution: 64×64
 **Expected**: more stable training, better diversity. Likely remaining issues: texture artifacts, limited global coherence at higher resolution.
 ---
 ### 2.3 — WGAN-GP + Spectral Norm + GroupNorm + Self-Attention
 **Because**: WGAN-GP showed texture artifacts / limited coherence → principled Lipschitz constraint and long-range dependencies.
 - Generator: BatchNorm → GroupNorm (no batch-size coupling)
 - Critic: InstanceNorm → Spectral Normalization (principled Lipschitz constraint)
 - Self-attention at 16×16 in both generator and critic
 ```python
 class SelfAttention(nn.Module):
    def __init__(self, in_ch):
        super().__init__()
        mid = max(in_ch // 8, 1)
        self.q = nn.Conv2d(in_ch, mid, 1, bias=False)
        self.k = nn.Conv2d(in_ch, mid, 1, bias=False)
        self.v = nn.Conv2d(in_ch, in_ch, 1, bias=False)
        self.gamma = nn.Parameter(torch.zeros(1))
        self._mid = mid
    def forward(self, x):
        b, c, h, w = x.shape
        q = self.q(x).view(b, self._mid, -1).transpose(-2, -1)
        k = self.k(x).view(b, self._mid, -1)
        v = self.v(x).view(b, c, -1)
        attn = torch.softmax(q @ k * self._mid ** -0.5, dim=-1)
        return x + self.gamma * (v @ attn.transpose(-2, -1)).view(b, c, h, w)
 ```
 ---
 ### 2.4 — Scale to 128×128 *(if 2.3 looks coherent at 64×64)*
 **Because**: 2.3 produces coherent samples at 64×64 → does the architecture hold up at higher resolution?
 Same architecture as 2.3, retrained at 128×128. Add attention at 32×32 if memory permits.
 ---
 ### Phase 2 Results
 | Step | Model | FID @ 100ep ↓ | Main observed failure | Motivates next step |
 |---|---|---|---|---|
 | 2.1 | DCGAN | ? | ? | ? |
 | 2.2 | WGAN-GP | ? | ? | ? |
 | 2.3 | WGAN-GP + SN + Attn | ? | ? | ? |
 | 2.4 | + 128×128 | ? | ? | — |
 For each step: FID curve, 16-sample grid, one paragraph on what failed and why the next change addresses it.
 ---
 ## Phase 3 — VAE Track
 **Goal**: A self-contained evolution story for the likelihood-based family. Every step motivated by a known limitation of the previous.
 | Step | Model | Because |
 |---|---|---|
 | 3.1 | Vanilla VAE (MSE) | Baseline — expect blur |
 | 3.2 | + Perceptual loss (VGG) | MSE blur is fundamental to pixel-space reconstruction |
 | 3.3 | + PatchGAN discriminator (VQGAN-lite) | Perceptual loss still lacks local texture realism |
 **3.1 — Vanilla VAE**: Adam lr=1e-3, latent_dim=256, β=1.0. Plain convolutional encoder/decoder, MSE reconstruction.
 **3.2 — Perceptual loss**: VGG-16 feature matching at relu1_2, relu2_2, relu3_3.
 **3.3 — Patch discriminator**: PatchGAN adversarial loss targeting local texture realism.
 ```
 L = L_mse + λ_perc·L_vgg + λ_adv·L_adv + β·L_kl
 λ_perc=0.1, λ_adv=0.1, β=0.0001
 ```
 **Decoder fix** (applied from 3.1 onward): replace `ConvTranspose2d` with `Upsample(nearest) + Conv2d` — eliminates checkerboard artifacts.
 | Step | Model | FID ↓ | Main observed failure |
 |---|---|---|---|
 | 3.1 | VAE MSE | ? | ? |
 | 3.2 | + Perceptual | ? | ? |
 | 3.3 | + PatchGAN | ? | ? |
 ---
 ## Phase 4 — DDPM Track
 **Goal**: A self-contained evolution story for the diffusion family.
 | Step | Model | Because |
 |---|---|---|
 | 4.1 | DDPM linear + ε-pred | Baseline |
 | 4.2 | + cosine schedule | Linear schedule wastes capacity at low timesteps |
 | 4.3 | + v-prediction | ε-prediction is unstable across the full trajectory |
 | 4.4 | + wider U-Net / more attention | If 4.3 still underfits |
 **4.1 — Baseline**: AdamW lr=2e-4, base_ch=128, T=1000, attention at 8×8 and 16×16. DDIM sampling, 100 steps.
 **4.2 — Cosine schedule**:
 ```python
 def cosine_betas(T: int, s: float = 0.008):
    t = torch.linspace(0, T, T + 1)
    f = torch.cos((t / T + s) / (1 + s) * math.pi / 2) ** 2
    alpha_bar = f / f[0]
    betas = 1 - alpha_bar[1:] / alpha_bar[:-1]
    return betas.clamp(0, 0.999)
 ```
 **4.3 — v-prediction**: replaces ε target with `v = √ᾱ·ε − √(1−ᾱ)·x₀`.
 **4.4 — Wider U-Net**: base_ch 128 → 192, attention at 8×8, 16×16, 32×32.
 | Step | Model | FID ↓ | Main observed failure |
 |---|---|---|---|
 | 4.1 | DDPM linear + ε | ? | ? |
 | 4.2 | + cosine | ? | ? |
 | 4.3 | + v-pred | ? | ? |
 | 4.4 | + wider | ? | ? |
 ---
 ## Phase 5 — Cross-Family Comparison
 **Goal**: Side-by-side comparison of the best from each family (2.4, 3.3, 4.4) under identical conditions.
 Best-of-each retrained for 200 epochs at the same resolution and pipeline.
 ### 5A — Quantitative
 | Model | FID ↓ | IS ↑ | LPIPS diversity ↑ | Params | Train time |
 |---|---|---|---|:---:|:---:|
 | Best GAN (2.4) | ? | ? | ? | ? | ? |
 | Best VAE (3.3) | ? | ? | ? | ? | ? |
 | Best DDPM (4.4) | ? | ? | ? | ? | ? |
 ### 5B — Qualitative
 - **Visual grids**: 16-image sample grids per finalist
 - **Progression**: epoch 10 → 50 → 100 → 200 side by side
 - **Latent interpolation**: smooth transitions between two latent codes (GAN, VAE)
 - **Diversity**: average pairwise LPIPS distance across 100 generated images
 - **Failure modes**: worst-generated images per model
 ---
 ## Compute Budget Notes
 Three families × multiple steps is a lot of runs. If compute is tight:
 - **Keep the GAN track complete** (2.1 → 2.4) — it carries the strongest narrative.
 - **VAE and DDPM can drop the last step each** (stop at 3.2 and 4.3) without hurting the story.
 - Phase 1 ablations can use 50 epochs instead of 100 — pipeline deltas show up early.
 ---
 ## Summary
 | Phase | Purpose | Models | Output |
 |---|---|---|---|
 | 1 | Pipeline selection | DCGAN @ 64×64 across data variants | Best pipeline |
 | 2 | GAN evolution (main spine) | DCGAN → WGAN-GP → +SN+Attn → 128×128 | GAN failure→fix narrative |
 | 3 | VAE evolution | VAE → +Perceptual → +PatchGAN | VAE failure→fix narrative |
 | 4 | DDPM evolution | DDPM → cosine → v-pred → wider | DDPM failure→fix narrative |
 | 5 | Cross-family comparison | Best of each, retrained 200ep | Final FID + IS + qualitative |
 **The narrative**: baseline fails in a specific way → fix targets that failure → new failure emerges → next fix targets that → repeat per family → compare families on equal footing.
@@ -1,340 +0,0 @@
 # Generator Pipeline Summary (Phases 0-5)
 ## Scope
 This document summarizes the full story told across the generator notebooks:
 - `phase0_analysis.ipynb`
 - `phase1_analysis.ipynb`
 - `phase2_analysis.ipynb`
 - `phase3_analysis.ipynb`
 - `phase4_analysis.ipynb`
 - `phase5_analysis.ipynb`
 It covers pipeline design, experiment evolution, result analysis, and final sample outcomes. The last section provides a super-detailed list of what is still missing and what should be included.
 Important constraint for follow-up work:
 - No additional model training is assumed or recommended.
 - All suggested improvements below are limited to post-hoc analysis, evaluation, documentation, stress tests, or re-use of already trained checkpoints and generated samples.
 ## 1) End-to-End Story of the Pipeline
 ### Phase 0: Baseline sanity check
 Goal:
 - Verify training loops for GAN, VAE, and DDPM are working end-to-end.
 - Create intentionally rough baseline outputs to compare later improvements.
 What was done:
 - Trained baseline WGAN-GP, VAE, DDPM, and a small DDPM variant.
 - Used raw/un-aligned images.
 - Focused on training curves and visual samples rather than strong quantitative quality.
 Findings:
 - WGAN produced coarse face-like blobs.
 - VAE produced blurry mean-like reconstructions/samples.
 - DDPM showed better local texture but still noisy.
 - Main takeaway: data quality/preprocessing is a major bottleneck.
 Outputs:
 - Run logs in `generator/outputs/logs/`.
 - Sample grids/checkpoints in `generator/outputs/samples/`.
 ---
 ### Phase 1: Data pipeline ablation and lock-in
 Goal:
 - Identify the best data/preprocessing recipe using cheap proxy experiments.
 - Lock pipeline decisions before expensive model evolution.
 What was done:
 - Four ablation groups with short DCGAN runs:
 1. Resolution (64 vs 128)
 2. Alignment (raw vs MTCNN aligned)
 3. Augmentation (simple vs richer augmentation)
 4. Dataset mixing (aligned-only vs aligned+raw)
 Findings:
 - Alignment is the strongest lever.
 - 64x64 is better than 128x128 under the tested budget.
 - Richer augmentation helps in the proxy setup.
 - Mixing aligned and raw data hurts quality.
 Decision locked for future phases:
 - Use aligned faces, 64x64, no raw/aligned mixing.
 Outputs:
 - Comparative FID plots and ablation figures in `generator/outputs/figures/`.
 ---
 ### Phase 2: GAN evolution (architecture and stability)
 Goal:
 - Solve GAN collapse behavior and improve quality under the locked data pipeline.
 What was done:
 - Progressive GAN experiments:
 1. Baseline DCGAN-like setup
 2. WGAN-GP objective update
 3. Add spectral normalization + GroupNorm + self-attention
 4. Test 128x128 at similar budget
 Findings:
 - Objective change alone gave small gains.
 - Biggest jump came from stability/capacity design (SN + GroupNorm + attention).
 - 128x128 regressed under fixed compute budget.
 Decision:
 - Best GAN recipe kept at 64x64 with SN + attention stack.
 Outputs:
 - Best checkpoints and phase comparison samples in `generator/outputs/models/`, `generator/outputs/samples/`, and `generator/outputs/figures/`.
 ---
 ### Phase 3: VAE evolution (composite objective)
 Goal:
 - Improve VAE from overly smooth outputs to better perceptual quality.
 What was done:
 - Step-wise loss composition:
 1. MSE + KL baseline
 2. Add perceptual (VGG) loss
 3. Add adversarial (PatchGAN) component
 Findings:
 - Perceptual loss provided major detail recovery.
 - Adding adversarial loss provided further gain.
 - Loss components were complementary.
 Decision:
 - Keep VAE with MSE + weighted KL + perceptual + PatchGAN terms.
 Outputs:
 - Prior samples, reconstructions, and loss/FID trends in `generator/outputs/samples/` and `generator/outputs/figures/`.
 ---
 ### Phase 4: DDPM evolution (schedule, target, width)
 Goal:
 - Improve diffusion quality via more modern design choices.
 What was done:
 - Sequential DDPM upgrades:
 1. Baseline linear schedule + epsilon prediction
 2. Cosine schedule
 3. Cosine + v-prediction
 4. Wider UNet/capacity increase
 Findings:
 - Schedule alone gave small gains.
 - v-prediction produced the major improvement.
 - Wider network improved further, at higher training cost.
 Decision:
 - Best DDPM setup: cosine schedule + v-prediction + wider backbone.
 Outputs:
 - Noise schedule visuals, progression grids, and best samples in `generator/outputs/figures/` and `generator/outputs/samples/`.
 ---
 ### Phase 5: Best-of-family final comparison
 Goal:
 - Fair head-to-head across the best GAN, VAE, and DDPM recipes.
 - Conclude practical model choice using quality vs compute trade-offs.
 What was done:
 - Trained/evaluated best recipes from phases 2-4 on same pipeline constraints.
 - Compared FID curves, final samples, progress snapshots, and interpolation behavior.
 Main result:
 - DDPM achieved best quality (best FID in this project).
 - GAN was close in quality but much faster in training/inference.
 - VAE was fastest to train but clearly behind in final sample quality.
 Practical interpretation:
 - If absolute sample quality is primary: DDPM.
 - If quality-speed balance is primary: GAN.
 - If quick prototyping/low compute is primary: VAE.
 Outputs:
 - Final family samples and comparisons in `generator/outputs/samples/` and `generator/outputs/figures/`.
 ## 2) Evolution of Decisions Across Phases
 1. Phase 0 showed baseline failure patterns and established motivation for targeted improvements.
 2. Phase 1 proved data preprocessing (especially alignment) is the foundation.
 3. Phase 2 showed GAN quality breakthrough came from stability/capacity changes, not only loss swap.
 4. Phase 3 showed VAE quality improves strongly via loss composition.
 5. Phase 4 showed diffusion gains were driven mostly by prediction target choice and then model width.
 6. Phase 5 demonstrated final family ranking and trade-offs under common conditions.
 ## 3) What Is Already Well Covered
 - Clear multi-phase narrative from baseline to final comparison.
 - Systematic ablation mindset in each phase.
 - Good use of saved artifacts (logs, figures, samples).
 - Strong comparative storytelling in final phase (quality vs speed vs practicality).
 ## 4) Super-Detailed Missing / Should-Be-Included Section
 This section is intentionally exhaustive. Every item below is designed to work with the models, checkpoints, samples, and logs that already exist.
 ### A. Evaluation and analysis gaps
 1. Missing multi-metric evaluation beyond FID.
 Should include:
 - KID, Precision/Recall (for generative coverage vs fidelity), and optionally IS computed on the already-trained outputs.
 - A short explanation of what each metric captures and where FID can be misleading.
 2. No uncertainty/statistical significance around reported FID.
 Should include:
 - Bootstrap confidence intervals over the already generated sample sets.
 - Mean +- std tables across repeated FID subsampling on the saved outputs.
 3. Missing mode coverage/diversity analysis.
 Should include:
 - Precision-recall split for generative models.
 - Cluster-level coverage checks using the generated samples already on disk.
 - Nearest-neighbor distance plots for generated vs. training data.
 4. Missing per-attribute quality analysis.
 Should include:
 - Analysis by pose, illumination, expression, and age bands using the existing samples.
 - Generated-vs-real attribute distribution matching.
 5. Missing metric protocol sensitivity analysis.
 Should include:
 - FID stability under different sample counts and bootstrap resampling.
 - A clear explanation of why phase-to-phase absolute FID comparability can fail.
 6. Missing human-perception validation.
 Should include:
 - A small blind ranking study using the already generated sample grids.
 - A comparison between human preference and metric preference.
 ### B. Post-hoc experiment analysis gaps
 1. Loss-weight behavior is not interpreted deeply enough.
 Should include:
 - A post-hoc explanation of how the chosen perceptual/adversarial weights affected the saved VAE outputs.
 - A summary table of the observed trade-off across the completed runs, without proposing new training.
 2. Family-specific preprocessing effects are not fully separated.
 Should include:
 - A careful read of how the locked aligned-64 pipeline interacts with each family’s final samples.
 - Visual comparisons that isolate preprocessing benefits already visible in the saved figures.
 3. Hyperparameter conclusions are narrow.
 Should include:
 - A consolidated summary of which configurations already worked best and which were discarded.
 - No new sweeps; only interpretation of the existing trained runs.
 4. Generalization checks are missing.
 Should include:
 - Evaluation of the existing checkpoints on any available held-out or alternate data, if such data already exists.
 - If no extra data exists, explicitly state that generalization was not tested.
 5. Failure-case experiments are not explicitly catalogued.
 Should include:
 - A concise “negative results” subsection per phase with what failed and why, based only on the completed experiments.
 ### C. Reproducibility gaps
 1. Seeds are not consistently documented.
 Should include:
 - A run-level seed log for the completed experiments.
 2. Environment and hardware specs are missing in notebook narrative.
 Should include:
 - GPU, CUDA, PyTorch, Python, and key package versions.
 3. Config traceability could be clearer inside notebooks.
 Should include:
 - Printed key config values in each phase notebook.
 - A direct link from each run name to its exact config JSON.
 4. Checkpoint selection policy should be formalized.
 Should include:
 - A clear rule for when final EMA or best EMA is used and why.
 5. Reproduction guide is missing in notebooks folder.
 Should include:
 - Step-by-step commands to replay the notebooks and re-open the saved artifacts.
 ### D. Practical deployment/evaluation gaps
 1. Inference speed and memory profiling is incomplete.
 Should include:
 - Throughput, latency, and VRAM table for the already trained GAN/VAE/DDPM checkpoints.
 2. Sample count vs. quality behavior is missing.
 Should include:
 - FID-vs-number-of-generated-samples curve using already saved samples or deterministic re-sampling from existing checkpoints.
 3. Robustness/distribution shift testing is missing.
 Should include:
 - Corruption robustness tests (blur, noise, compression) applied to the existing outputs.
 - Optional out-of-domain face evaluation if a suitable held-out dataset already exists.
 4. Model selection guide should be more operational.
 Should include:
 - A decision table by target constraints: best quality, best latency, lowest compute burden, easiest analysis, and most stable outputs.
 ### E. Ethics and risk gaps
 1. Dataset bias assessment is not included.
 Should include:
 - Demographic/attribute distribution report if labels are available.
 - Generated distribution parity analysis against the real data.
 2. Misuse and deepfake risk section is missing.
 Should include:
 - Clear misuse statement and mitigation suggestions.
 3. Memorization/privacy leakage checks are missing.
 Should include:
 - A nearest-neighbor memorization audit and threshold-based discussion using the trained models' samples.
 4. Responsible use guidance is absent.
 Should include:
 - Recommended and discouraged use cases in the summary/report.
 ### F. Documentation quality gaps
 1. Mathematical objective definitions are incomplete in narrative form.
 Should include:
 - Formal equations for the VAE composite loss with explicit coefficients.
 2. Architectural diagrams are missing.
 Should include:
 - Compact diagrams for the GAN, VAE, and DDPM best variants.
 3. Troubleshooting guidance is missing.
 Should include:
 - Common failure patterns (loss explosion, collapse, OOM) and practical fixes that reflect what already happened in the project.
 4. Literature baseline context is limited.
 Should include:
 - Comparison table versus well-known references, with protocol caveats.
 ## 5) Recommended Next-Step Priorities
 ### Priority 1 (fast and high impact)
 1. Add bootstrap uncertainty bands and confidence intervals to the existing FID comparisons.
 2. Add precision/recall and KID alongside FID for the current sample sets.
 3. Add an explicit FID protocol box in all notebooks.
 4. Add a short model selection guide and reproducibility/environment block.
 ### Priority 2 (medium effort, strong value)
 1. Add a negative-results appendix and troubleshooting notes based on the completed runs.
 2. Add inference throughput/VRAM benchmarking for the already trained checkpoints.
 3. Add per-attribute and nearest-neighbor analysis using existing outputs.
 ### Priority 3 (larger effort, publication-level completeness)
 1. Human preference study on the saved sample grids.
 2. Fairness/bias and memorization audits.
 3. Cross-dataset generalization analysis if another dataset already exists in the project environment.
 ## 6) Final Bottom-Line Conclusion
 The notebook set tells a coherent and strong experimental story: baseline failures -> pipeline correction -> family-specific improvements -> final cross-family comparison. The final evidence shows a clear quality-speed trade-off: DDPM gives the best sample quality, GAN gives near-best quality with far better speed, and VAE remains useful when compute and iteration speed dominate.
 Because no further training is planned, the most valuable remaining work is not new model fitting. It is post-hoc analysis of the models already trained: broader evaluation metrics, uncertainty estimates, robustness checks, memorization/privacy checks, and clearer documentation of protocol and limitations.
@@ -0,0 +1,10 @@
 {
  "run_name": "p1_resnet18_baseline",
  "backbone": "resnet18",
  "pretrained": true,
  "epochs": 15,
  "image_size": 128,
  "subsample": 0.2,
  "augment": false,
  "data_dir": "data"
 }
@@ -0,0 +1,11 @@
 {
  "run_name": "p1_simplecnn_baseline",
  "backbone": "simple_cnn",
  "cnn_preset": "medium",
  "dropout": 0.0,
  "epochs": 15,
  "image_size": 128,
  "subsample": 0.2,
  "augment": false,
  "data_dir": "data"
 }
@@ -0,0 +1,11 @@
 {
  "run_name": "p2a_t1_original",
  "backbone": "resnet18",
  "pretrained": true,
  "epochs": 15,
  "image_size": 224,
  "subsample": 0.2,
  "augment": false,
  "data_dir": "data",
  "normalization": "imagenet"
 }
@@ -0,0 +1,5 @@
 {
  "extends": "p2a_t1_original.json",
  "run_name": "p2a_t2_real_norm",
  "normalization": "real_norm"
 }
@@ -0,0 +1,15 @@
 {
  "extends": "p2a_t1_original.json",
  "run_name": "p2a_t3_holdout_inpainting",
  "train_sources": [
    "wiki",
    "text2img",
    "insight"
  ],
  "eval_sources": [
    "wiki",
    "text2img",
    "insight",
    "inpainting"
  ]
 }
@@ -0,0 +1,15 @@
 {
  "extends": "p2a_t1_original.json",
  "run_name": "p2a_t3_holdout_insight",
  "train_sources": [
    "wiki",
    "text2img",
    "inpainting"
  ],
  "eval_sources": [
    "wiki",
    "text2img",
    "inpainting",
    "insight"
  ]
 }
@@ -0,0 +1,15 @@
 {
  "extends": "p2a_t1_original.json",
  "run_name": "p2a_t3_holdout_text2img",
  "train_sources": [
    "wiki",
    "inpainting",
    "insight"
  ],
  "eval_sources": [
    "wiki",
    "inpainting",
    "insight",
    "text2img"
  ]
 }
@@ -0,0 +1,10 @@
 {
  "run_name": "p2b_resnet18_224",
  "backbone": "resnet18",
  "pretrained": true,
  "epochs": 15,
  "image_size": 224,
  "subsample": 0.2,
  "augment": false,
  "data_dir": "data"
 }
@@ -0,0 +1,11 @@
 {
  "run_name": "p2b_simplecnn_224",
  "backbone": "simple_cnn",
  "cnn_preset": "medium",
  "dropout": 0.0,
  "epochs": 15,
  "image_size": 224,
  "subsample": 0.2,
  "augment": false,
  "data_dir": "data"
 }
@@ -0,0 +1,10 @@
 {
  "run_name": "p2c_resnet18_facecrop",
  "backbone": "resnet18",
  "pretrained": true,
  "epochs": 15,
  "image_size": 224,
  "subsample": 0.2,
  "augment": false,
  "data_dir": "cropped/classifier"
 }
@@ -0,0 +1,11 @@
 {
  "run_name": "p2c_simplecnn_facecrop",
  "backbone": "simple_cnn",
  "cnn_preset": "medium",
  "dropout": 0.0,
  "epochs": 15,
  "image_size": 224,
  "subsample": 0.2,
  "augment": false,
  "data_dir": "cropped/classifier"
 }
@@ -0,0 +1,22 @@
 {
  "run_name": "p2d_resnet18_aug",
  "backbone": "resnet18",
  "pretrained": true,
  "epochs": 15,
  "image_size": 224,
  "subsample": 0.2,
  "data_dir": "data",
  "augment": {
    "hflip_p": 0.5,
    "rotation_degrees": 10,
    "brightness": 0.2,
    "contrast": 0.2,
    "saturation": 0.1,
    "hue": 0.02,
    "grayscale_p": 0.1,
    "blur_p": 0.1,
    "erase_p": 0.2,
    "noise_p": 0.3,
    "noise_std": 0.04
  }
 }
@@ -0,0 +1,23 @@
 {
  "run_name": "p2d_simplecnn_aug",
  "backbone": "simple_cnn",
  "cnn_preset": "medium",
  "dropout": 0.0,
  "epochs": 15,
  "image_size": 224,
  "subsample": 0.2,
  "data_dir": "data",
  "augment": {
    "hflip_p": 0.5,
    "rotation_degrees": 10,
    "brightness": 0.2,
    "contrast": 0.2,
    "saturation": 0.1,
    "hue": 0.02,
    "grayscale_p": 0.1,
    "blur_p": 0.1,
    "erase_p": 0.2,
    "noise_p": 0.3,
    "noise_std": 0.04
  }
 }
@@ -0,0 +1,22 @@
 {
  "run_name": "p2e_resnet18_facecrop_aug",
  "backbone": "resnet18",
  "pretrained": true,
  "epochs": 15,
  "image_size": 224,
  "subsample": 0.2,
  "data_dir": "cropped/classifier",
  "augment": {
    "hflip_p": 0.5,
    "rotation_degrees": 10,
    "brightness": 0.2,
    "contrast": 0.2,
    "saturation": 0.1,
    "hue": 0.02,
    "grayscale_p": 0.1,
    "blur_p": 0.1,
    "erase_p": 0.2,
    "noise_p": 0.3,
    "noise_std": 0.04
  }
 }
@@ -0,0 +1,23 @@
 {
  "run_name": "p2e_simplecnn_facecrop_aug",
  "backbone": "simple_cnn",
  "cnn_preset": "medium",
  "dropout": 0.0,
  "epochs": 15,
  "image_size": 224,
  "subsample": 0.2,
  "data_dir": "cropped/classifier",
  "augment": {
    "hflip_p": 0.5,
    "rotation_degrees": 10,
    "brightness": 0.2,
    "contrast": 0.2,
    "saturation": 0.1,
    "hue": 0.02,
    "grayscale_p": 0.1,
    "blur_p": 0.1,
    "erase_p": 0.2,
    "noise_p": 0.3,
    "noise_std": 0.04
  }
 }
@@ -0,0 +1,8 @@
 {
  "pretrained": true,
  "epochs": 15,
  "image_size": 224,
  "subsample": 0.2,
  "augment": false,
  "data_dir": "cropped/classifier"
 }
@@ -0,0 +1,5 @@
 {
  "extends": "_base.json",
  "run_name": "p3_convnext_tiny",
  "backbone": "convnext_tiny"
 }
@@ -0,0 +1,5 @@
 {
  "extends": "_base.json",
  "run_name": "p3_efficientnet_b0",
  "backbone": "efficientnet_b0"
 }
@@ -0,0 +1,5 @@
 {
  "extends": "_base.json",
  "run_name": "p3_mobilenetv3_small",
  "backbone": "mobilenet_v3_small"
 }
@@ -0,0 +1,5 @@
 {
  "extends": "_base.json",
  "run_name": "p3_resnet34",
  "backbone": "resnet34"
 }
@@ -0,0 +1,5 @@
 {
  "extends": "_base.json",
  "run_name": "p3_resnet50",
  "backbone": "resnet50"
 }
@@ -0,0 +1,7 @@
 {
  "pretrained": true,
  "epochs": 15,
  "image_size": 224,
  "augment": false,
  "data_dir": "cropped/classifier"
 }
@@ -0,0 +1,6 @@
 {
  "extends": "_base.json",
  "run_name": "p4_convnext_tiny_100pct",
  "backbone": "convnext_tiny",
  "subsample": 1.0
 }
@@ -0,0 +1,6 @@
 {
  "extends": "_base.json",
  "run_name": "p4_convnext_tiny_50pct",
  "backbone": "convnext_tiny",
  "subsample": 0.5
 }
@@ -0,0 +1,6 @@
 {
  "extends": "_base.json",
  "run_name": "p4_efficientnet_b0_100pct",
  "backbone": "efficientnet_b0",
  "subsample": 1.0
 }
@@ -0,0 +1,6 @@
 {
  "extends": "_base.json",
  "run_name": "p4_efficientnet_b0_50pct",
  "backbone": "efficientnet_b0",
  "subsample": 0.5
 }
@@ -0,0 +1,6 @@
 {
  "extends": "_base.json",
  "run_name": "p4_resnet50_100pct",
  "backbone": "resnet50",
  "subsample": 1.0
 }
@@ -0,0 +1,6 @@
 {
  "extends": "_base.json",
  "run_name": "p4_resnet50_50pct",
  "backbone": "resnet50",
  "subsample": 0.5
 }
@@ -0,0 +1,13 @@
 {
  "seed": 42,
  "cv_folds": 5,
  "batch_size": 32,
  "num_workers": 4,
  "early_stopping_patience": 5,
  "lr": 1e-4,
  "weight_decay": 1e-4,
  "T_max": 15,
  "data_dir": "data"
 }
@@ -0,0 +1,16 @@
 run,n_candidates,n_images,heldout_source,candidate_auc,candidate_acc,panel,expanded_panel,fine_panel
 p1_simplecnn_baseline,192,10,,0.8378182870370371,0.7395833333333334,classifier\outputs\gradcam\p1_simplecnn_baseline\panel.png,,
 p1_resnet18_baseline,192,10,,0.9769965277777778,0.9270833333333334,classifier\outputs\gradcam\p1_resnet18_baseline\panel.png,,
 p2a_t1_original,192,10,,0.9984085648148149,0.9895833333333334,classifier\outputs\gradcam\p2a_t1_original\panel.png,,
 p2a_t2_real_norm,192,10,,0.9939236111111112,0.9791666666666666,classifier\outputs\gradcam\p2a_t2_real_norm\panel.png,,
 p2a_t3_holdout_text2img,240,10,text2img,0.9264322916666667,0.775,classifier\outputs\gradcam\p2a_t3_holdout_text2img\panel.png,,
 p2a_t3_holdout_inpainting,240,10,inpainting,0.9819878472222222,0.9333333333333333,classifier\outputs\gradcam\p2a_t3_holdout_inpainting\panel.png,,
 p2a_t3_holdout_insight,240,10,insight,0.9549696180555556,0.7625,classifier\outputs\gradcam\p2a_t3_holdout_insight\panel.png,,
 p2b_simplecnn_224,192,10,,0.8207465277777778,0.7447916666666666,classifier\outputs\gradcam\p2b_simplecnn_224\panel.png,,
 p2b_resnet18_224,192,10,,0.9984085648148149,0.9895833333333334,classifier\outputs\gradcam\p2b_resnet18_224\panel.png,,
 p2c_simplecnn_facecrop,192,10,,0.8058449074074073,0.7552083333333334,classifier\outputs\gradcam\p2c_simplecnn_facecrop\panel.png,,
 p2c_resnet18_facecrop,192,16,,0.9911747685185185,0.890625,classifier\outputs\gradcam\p2c_resnet18_facecrop\panel.png,classifier\outputs\gradcam\p2c_resnet18_facecrop\panel_expanded.png,classifier\outputs\gradcam\p2c_resnet18_facecrop\panel_fine_layer3.png
 p2d_simplecnn_aug,192,10,,0.7565104166666666,0.65625,classifier\outputs\gradcam\p2d_simplecnn_aug\panel.png,,
 p2d_resnet18_aug,192,10,,0.9733796296296297,0.9270833333333334,classifier\outputs\gradcam\p2d_resnet18_aug\panel.png,,
 p2e_simplecnn_facecrop_aug,192,10,,0.7358217592592592,0.6510416666666666,classifier\outputs\gradcam\p2e_simplecnn_facecrop_aug\panel.png,,
 p2e_resnet18_facecrop_aug,192,10,,0.9910300925925927,0.9322916666666666,classifier\outputs\gradcam\p2e_resnet18_facecrop_aug\panel.png,,
@@ -0,0 +1,4 @@
 panel,path
 architecture capacity,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\evolution\05_evolution_architecture_capacity.png
 resnet pipeline,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\evolution\05_evolution_resnet_pipeline.png
 simplecnn pipeline,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\evolution\05_evolution_simplecnn_pipeline.png
@@ -0,0 +1,138 @@
 {
  "run": "p1_resnet18_baseline",
  "fold": 0,
  "n_candidates": 192,
  "candidate_metrics": {
    "accuracy": 0.9270833333333334,
    "auc_roc": 0.9769965277777778,
    "f1": 0.950354609929078,
    "confusion_matrix": [
      [
        44,
        4
      ],
      [
        10,
        134
      ]
    ]
  },
  "heldout_source": null,
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\29\\19558629_1985-02-28_2007.jpg",
      "basename": "19558629_1985-02-28_2007.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 7.202164852060378e-06,
      "logit": -11.841121673583984,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\83\\2667583_1985-05-02_2009.jpg",
      "basename": "2667583_1985-05-02_2009.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 8.81600226421142e-06,
      "logit": -11.638933181762695,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\08\\3523408_1963-02-01_2015.jpg",
      "basename": "3523408_1963-02-01_2015.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 17.020292282104492,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\17\\32590217_1942-09-19_2014.jpg",
      "basename": "32590217_1942-09-19_2014.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9999988079071045,
      "logit": 13.679031372070312,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\31\\2346431_1980-06-11_2007.jpg",
      "basename": "2346431_1980-06-11_2007.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 18.888574600219727,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\97\\3832797_1984-01-19_2010.jpg",
      "basename": "3832797_1984-01-19_2010.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.9967204928398132,
      "logit": 5.7167792320251465,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\49\\25392649_1991-05-29_2013.jpg",
      "basename": "25392649_1991-05-29_2013.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.8840320110321045,
      "logit": 2.031179428100586,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\56\\14853156_1981-05-28_2009.jpg",
      "basename": "14853156_1981-05-28_2009.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.0010116391349583864,
      "logit": -6.895171165466309,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\10\\726410_1912-05-31_1997.jpg",
      "basename": "726410_1912-05-31_1997.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.0036017918027937412,
      "logit": -5.622715473175049,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\81\\3028481_1951-07-05_1977.jpg",
      "basename": "3028481_1951-07-05_1977.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.4881739914417267,
      "logit": -0.04731296747922897,
      "selection": "borderline"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_resnet18_baseline\\01_confident_true_wiki_wiki_19558629_1985-02-28_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_resnet18_baseline\\02_confident_true_wiki_wiki_2667583_1985-05-02_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_resnet18_baseline\\03_confident_true_inpainting_inpainting_3523408_1963-02-01_2015.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_resnet18_baseline\\04_confident_true_insight_insight_32590217_1942-09-19_2014.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_resnet18_baseline\\05_confident_true_text2img_text2img_2346431_1980-06-11_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_resnet18_baseline\\06_strong_false_positive_wiki_3832797_1984-01-19_2010.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_resnet18_baseline\\07_strong_false_positive_wiki_25392649_1991-05-29_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_resnet18_baseline\\08_strong_false_negative_insight_14853156_1981-05-28_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_resnet18_baseline\\09_strong_false_negative_insight_726410_1912-05-31_1997.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_resnet18_baseline\\10_borderline_wiki_3028481_1951-07-05_1977.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_resnet18_baseline\\panel.png",
  "expanded_panel_path": null,
  "fine_panel_path": null
 }
@@ -0,0 +1,138 @@
 {
  "run": "p1_simplecnn_baseline",
  "fold": 0,
  "n_candidates": 192,
  "candidate_metrics": {
    "accuracy": 0.7395833333333334,
    "auc_roc": 0.8378182870370371,
    "f1": 0.8031496062992126,
    "confusion_matrix": [
      [
        40,
        8
      ],
      [
        42,
        102
      ]
    ]
  },
  "heldout_source": null,
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\67\\779067_1968-10-08_2008.jpg",
      "basename": "779067_1968-10-08_2008.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.06221456080675125,
      "logit": -2.7129321098327637,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\31\\2346431_1980-06-11_2007.jpg",
      "basename": "2346431_1980-06-11_2007.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.10906809568405151,
      "logit": -2.1002955436706543,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\06\\25476706_1975-03-27_2009.jpg",
      "basename": "25476706_1975-03-27_2009.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9499694108963013,
      "logit": 2.9437952041625977,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\04\\34379104_1950-12-07_2013.jpg",
      "basename": "34379104_1950-12-07_2013.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9545132517814636,
      "logit": 3.0437798500061035,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\89\\9507989_1976-07-19_2007.jpg",
      "basename": "9507989_1976-07-19_2007.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9725574851036072,
      "logit": 3.567837715148926,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\52\\9143052_1931-01-21_1991.jpg",
      "basename": "9143052_1931-01-21_1991.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.8278583884239197,
      "logit": 1.5705249309539795,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\06\\25476706_1975-03-27_2009.jpg",
      "basename": "25476706_1975-03-27_2009.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.8256536722183228,
      "logit": 1.555131196975708,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\25\\1932725_1949-01-25_1979.jpg",
      "basename": "1932725_1949-01-25_1979.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.1434662640094757,
      "logit": -1.786793828010559,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\41\\37585341_1994-09-01_2015.jpg",
      "basename": "37585341_1994-09-01_2015.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.1799710988998413,
      "logit": -1.5165432691574097,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\74\\3290874_1984-06-18_2004.jpg",
      "basename": "3290874_1984-06-18_2004.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.4988362491130829,
      "logit": -0.004655048251152039,
      "selection": "borderline"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_simplecnn_baseline\\01_confident_true_wiki_wiki_779067_1968-10-08_2008.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_simplecnn_baseline\\02_confident_true_wiki_wiki_2346431_1980-06-11_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_simplecnn_baseline\\03_confident_true_inpainting_inpainting_25476706_1975-03-27_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_simplecnn_baseline\\04_confident_true_insight_insight_34379104_1950-12-07_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_simplecnn_baseline\\05_confident_true_text2img_text2img_9507989_1976-07-19_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_simplecnn_baseline\\06_strong_false_positive_wiki_9143052_1931-01-21_1991.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_simplecnn_baseline\\07_strong_false_positive_wiki_25476706_1975-03-27_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_simplecnn_baseline\\08_strong_false_negative_text2img_1932725_1949-01-25_1979.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_simplecnn_baseline\\09_strong_false_negative_inpainting_37585341_1994-09-01_2015.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_simplecnn_baseline\\10_borderline_inpainting_3290874_1984-06-18_2004.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p1_simplecnn_baseline\\panel.png",
  "expanded_panel_path": null,
  "fine_panel_path": null
 }
@@ -0,0 +1,138 @@
 {
  "run": "p2a_t1_original",
  "fold": 0,
  "n_candidates": 192,
  "candidate_metrics": {
    "accuracy": 0.9895833333333334,
    "auc_roc": 0.9984085648148149,
    "f1": 0.993006993006993,
    "confusion_matrix": [
      [
        48,
        0
      ],
      [
        2,
        142
      ]
    ]
  },
  "heldout_source": null,
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\74\\3290874_1984-06-18_2004.jpg",
      "basename": "3290874_1984-06-18_2004.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 3.208382226560502e-09,
      "logit": -19.557498931884766,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\29\\19558629_1985-02-28_2007.jpg",
      "basename": "19558629_1985-02-28_2007.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 6.348270176204096e-08,
      "logit": -16.572498321533203,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\06\\25476706_1975-03-27_2009.jpg",
      "basename": "25476706_1975-03-27_2009.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 17.80807876586914,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\81\\3028481_1951-07-05_1977.jpg",
      "basename": "3028481_1951-07-05_1977.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 19.85223960876465,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\10\\726410_1912-05-31_1997.jpg",
      "basename": "726410_1912-05-31_1997.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 17.96767807006836,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\33\\12318533_1982-06-16_2007.jpg",
      "basename": "12318533_1982-06-16_2007.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.00015543345944024622,
      "logit": -8.769137382507324,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\10\\726410_1912-05-31_1997.jpg",
      "basename": "726410_1912-05-31_1997.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.0615716315805912,
      "logit": -2.7240052223205566,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\57\\31543457_1910-02-06_1970.jpg",
      "basename": "31543457_1910-02-06_1970.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.5928639769554138,
      "logit": 0.3758176565170288,
      "selection": "borderline"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\44\\3720944_1920-12-28_1963.jpg",
      "basename": "3720944_1920-12-28_1963.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.7217857837677002,
      "logit": 0.9533369541168213,
      "selection": "borderline"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\04\\34379104_1950-12-07_2013.jpg",
      "basename": "34379104_1950-12-07_2013.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.897236168384552,
      "logit": 2.1668851375579834,
      "selection": "expanded_context"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t1_original\\01_confident_true_wiki_wiki_3290874_1984-06-18_2004.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t1_original\\02_confident_true_wiki_wiki_19558629_1985-02-28_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t1_original\\03_confident_true_inpainting_inpainting_25476706_1975-03-27_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t1_original\\04_confident_true_insight_insight_3028481_1951-07-05_1977.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t1_original\\05_confident_true_text2img_text2img_726410_1912-05-31_1997.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t1_original\\06_strong_false_negative_insight_12318533_1982-06-16_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t1_original\\07_strong_false_negative_inpainting_726410_1912-05-31_1997.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t1_original\\08_borderline_inpainting_31543457_1910-02-06_1970.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t1_original\\09_borderline_insight_3720944_1920-12-28_1963.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t1_original\\10_expanded_context_insight_34379104_1950-12-07_2013.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t1_original\\panel.png",
  "expanded_panel_path": null,
  "fine_panel_path": null
 }
@@ -0,0 +1,138 @@
 {
  "run": "p2a_t2_real_norm",
  "fold": 0,
  "n_candidates": 192,
  "candidate_metrics": {
    "accuracy": 0.9791666666666666,
    "auc_roc": 0.9939236111111112,
    "f1": 0.9861111111111112,
    "confusion_matrix": [
      [
        46,
        2
      ],
      [
        2,
        142
      ]
    ]
  },
  "heldout_source": null,
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\21\\24644621_1964-12-15_2010.jpg",
      "basename": "24644621_1964-12-15_2010.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 2.2610427549807355e-05,
      "logit": -10.697076797485352,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\46\\6385546_1980-05-23_2013.jpg",
      "basename": "6385546_1980-05-23_2013.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 8.002227696124464e-05,
      "logit": -9.433125495910645,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\29\\19558629_1985-02-28_2007.jpg",
      "basename": "19558629_1985-02-28_2007.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9999998807907104,
      "logit": 16.21304702758789,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\83\\2667583_1985-05-02_2009.jpg",
      "basename": "2667583_1985-05-02_2009.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9999998807907104,
      "logit": 15.857626914978027,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\33\\12318533_1982-06-16_2007.jpg",
      "basename": "12318533_1982-06-16_2007.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 18.31481170654297,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\02\\10386202_1915-02-10_1949.jpg",
      "basename": "10386202_1915-02-10_1949.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.8437501788139343,
      "logit": 1.686400294303894,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\81\\3028481_1951-07-05_1977.jpg",
      "basename": "3028481_1951-07-05_1977.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.5785955786705017,
      "logit": 0.31701087951660156,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\33\\12318533_1982-06-16_2007.jpg",
      "basename": "12318533_1982-06-16_2007.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.0017513168277218938,
      "logit": -6.345634460449219,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\21\\24644621_1964-12-15_2010.jpg",
      "basename": "24644621_1964-12-15_2010.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.48223310708999634,
      "logit": -0.0710974782705307,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\44\\3720944_1920-12-28_1963.jpg",
      "basename": "3720944_1920-12-28_1963.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.4771732687950134,
      "logit": -0.0913703665137291,
      "selection": "borderline"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t2_real_norm\\01_confident_true_wiki_wiki_24644621_1964-12-15_2010.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t2_real_norm\\02_confident_true_wiki_wiki_6385546_1980-05-23_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t2_real_norm\\03_confident_true_inpainting_inpainting_19558629_1985-02-28_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t2_real_norm\\04_confident_true_insight_insight_2667583_1985-05-02_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t2_real_norm\\05_confident_true_text2img_text2img_12318533_1982-06-16_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t2_real_norm\\06_strong_false_positive_wiki_10386202_1915-02-10_1949.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t2_real_norm\\07_strong_false_positive_wiki_3028481_1951-07-05_1977.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t2_real_norm\\08_strong_false_negative_insight_12318533_1982-06-16_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t2_real_norm\\09_strong_false_negative_insight_24644621_1964-12-15_2010.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t2_real_norm\\10_borderline_wiki_3720944_1920-12-28_1963.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t2_real_norm\\panel.png",
  "expanded_panel_path": null,
  "fine_panel_path": null
 }
@@ -0,0 +1,138 @@
 {
  "run": "p2a_t3_holdout_inpainting",
  "fold": 0,
  "n_candidates": 240,
  "candidate_metrics": {
    "accuracy": 0.9333333333333333,
    "auc_roc": 0.9819878472222222,
    "f1": 0.9567567567567568,
    "confusion_matrix": [
      [
        47,
        1
      ],
      [
        15,
        177
      ]
    ]
  },
  "heldout_source": "inpainting",
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\31\\2346431_1980-06-11_2007.jpg",
      "basename": "2346431_1980-06-11_2007.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 8.817832686247584e-09,
      "logit": -18.546489715576172,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\70\\22732870_1995-03-08_2014.jpg",
      "basename": "22732870_1995-03-08_2014.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 8.841730902986455e-09,
      "logit": -18.54378318786621,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\32\\39602032_1988-03-10_2013.jpg",
      "basename": "39602032_1988-03-10_2013.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 16.797229766845703,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\19\\14136419_1997-11-01_2013.jpg",
      "basename": "14136419_1997-11-01_2013.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 17.396644592285156,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\08\\3523408_1963-02-01_2015.jpg",
      "basename": "3523408_1963-02-01_2015.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 21.35198211669922,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\81\\3028481_1951-07-05_1977.jpg",
      "basename": "3028481_1951-07-05_1977.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.9999240636825562,
      "logit": 9.485453605651855,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\92\\3958892_1987-01-22_2013.jpg",
      "basename": "3958892_1987-01-22_2013.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 0,
      "prob_fake": 5.744245572714135e-05,
      "logit": -9.764669418334961,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\33\\12318533_1982-06-16_2007.jpg",
      "basename": "12318533_1982-06-16_2007.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.00028320401906967163,
      "logit": -8.169059753417969,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\02\\10386202_1915-02-10_1949.jpg",
      "basename": "10386202_1915-02-10_1949.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.41957107186317444,
      "logit": -0.32453441619873047,
      "selection": "borderline"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\56\\22613456_1986-02-10_2012.jpg",
      "basename": "22613456_1986-02-10_2012.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.6162223815917969,
      "logit": 0.473544716835022,
      "selection": "borderline"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_inpainting\\01_confident_true_wiki_wiki_2346431_1980-06-11_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_inpainting\\02_confident_true_wiki_wiki_22732870_1995-03-08_2014.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_inpainting\\03_confident_true_inpainting_inpainting_39602032_1988-03-10_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_inpainting\\04_confident_true_insight_insight_14136419_1997-11-01_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_inpainting\\05_confident_true_text2img_text2img_3523408_1963-02-01_2015.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_inpainting\\06_strong_false_positive_wiki_3028481_1951-07-05_1977.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_inpainting\\07_strong_false_negative_inpainting_3958892_1987-01-22_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_inpainting\\08_strong_false_negative_insight_12318533_1982-06-16_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_inpainting\\09_borderline_insight_10386202_1915-02-10_1949.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_inpainting\\10_borderline_inpainting_22613456_1986-02-10_2012.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_inpainting\\panel.png",
  "expanded_panel_path": null,
  "fine_panel_path": null
 }
@@ -0,0 +1,138 @@
 {
  "run": "p2a_t3_holdout_insight",
  "fold": 0,
  "n_candidates": 240,
  "candidate_metrics": {
    "accuracy": 0.7625,
    "auc_roc": 0.9549696180555556,
    "f1": 0.8267477203647416,
    "confusion_matrix": [
      [
        47,
        1
      ],
      [
        56,
        136
      ]
    ]
  },
  "heldout_source": "insight",
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\74\\3290874_1984-06-18_2004.jpg",
      "basename": "3290874_1984-06-18_2004.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 5.1686233418224425e-12,
      "logit": -25.988414764404297,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\31\\2346431_1980-06-11_2007.jpg",
      "basename": "2346431_1980-06-11_2007.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 9.782129967161879e-12,
      "logit": -25.3504638671875,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\15\\16719015_1940-10-10_1964.jpg",
      "basename": "16719015_1940-10-10_1964.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 20.643247604370117,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\62\\38862_1966-09-09_2005.jpg",
      "basename": "38862_1966-09-09_2005.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.999998927116394,
      "logit": 13.801375389099121,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\08\\3523408_1963-02-01_2015.jpg",
      "basename": "3523408_1963-02-01_2015.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 18.072370529174805,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\44\\3720944_1920-12-28_1963.jpg",
      "basename": "3720944_1920-12-28_1963.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.9999977350234985,
      "logit": 13.005270004272461,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\12\\11714712_1959-02-08_2007.jpg",
      "basename": "11714712_1959-02-08_2007.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 1.4099471590256485e-10,
      "logit": -22.68229866027832,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\05\\308405_1966-11-29_2008.jpg",
      "basename": "308405_1966-11-29_2008.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 8.343223001361366e-09,
      "logit": -18.601816177368164,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\07\\30502207_1953-11-09_1992.jpg",
      "basename": "30502207_1953-11-09_1992.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.4951499104499817,
      "logit": -0.019400928169488907,
      "selection": "borderline"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\20\\22164720_1946-06-24_2007.jpg",
      "basename": "22164720_1946-06-24_2007.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.5142862796783447,
      "logit": 0.05716080591082573,
      "selection": "borderline"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_insight\\01_confident_true_wiki_wiki_3290874_1984-06-18_2004.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_insight\\02_confident_true_wiki_wiki_2346431_1980-06-11_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_insight\\03_confident_true_inpainting_inpainting_16719015_1940-10-10_1964.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_insight\\04_confident_true_insight_insight_38862_1966-09-09_2005.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_insight\\05_confident_true_text2img_text2img_3523408_1963-02-01_2015.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_insight\\06_strong_false_positive_wiki_3720944_1920-12-28_1963.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_insight\\07_strong_false_negative_insight_11714712_1959-02-08_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_insight\\08_strong_false_negative_insight_308405_1966-11-29_2008.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_insight\\09_borderline_insight_30502207_1953-11-09_1992.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_insight\\10_borderline_insight_22164720_1946-06-24_2007.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_insight\\panel.png",
  "expanded_panel_path": null,
  "fine_panel_path": null
 }
@@ -0,0 +1,138 @@
 {
  "run": "p2a_t3_holdout_text2img",
  "fold": 0,
  "n_candidates": 240,
  "candidate_metrics": {
    "accuracy": 0.775,
    "auc_roc": 0.9264322916666667,
    "f1": 0.8383233532934131,
    "confusion_matrix": [
      [
        46,
        2
      ],
      [
        52,
        140
      ]
    ]
  },
  "heldout_source": "text2img",
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\23\\35796523_1952-12-01_1992.jpg",
      "basename": "35796523_1952-12-01_1992.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 2.9518876232259572e-08,
      "logit": -17.33823585510254,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\21\\24644621_1964-12-15_2010.jpg",
      "basename": "24644621_1964-12-15_2010.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 5.34162118981385e-08,
      "logit": -16.74515151977539,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\97\\3832797_1984-01-19_2010.jpg",
      "basename": "3832797_1984-01-19_2010.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9999995231628418,
      "logit": 14.63173770904541,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\17\\32590217_1942-09-19_2014.jpg",
      "basename": "32590217_1942-09-19_2014.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9999998807907104,
      "logit": 16.324398040771484,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\50\\31704050_1988-05-11_2013.jpg",
      "basename": "31704050_1988-05-11_2013.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9999866485595703,
      "logit": 11.222184181213379,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\35\\20728335_1988-07-07_2014.jpg",
      "basename": "20728335_1988-07-07_2014.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.9033180475234985,
      "logit": 2.2346484661102295,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\44\\3720944_1920-12-28_1963.jpg",
      "basename": "3720944_1920-12-28_1963.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.8656692504882812,
      "logit": 1.8631978034973145,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\98\\43349098_1994-08-30_2014.jpg",
      "basename": "43349098_1994-08-30_2014.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 0,
      "prob_fake": 7.18730461812811e-07,
      "logit": -14.14577865600586,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\68\\14609668_1956-12-09_2009.jpg",
      "basename": "14609668_1956-12-09_2009.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 0,
      "prob_fake": 9.012396731122863e-07,
      "logit": -13.919493675231934,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\57\\6707957_1985-09-04_2015.jpg",
      "basename": "6707957_1985-09-04_2015.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.4953322410583496,
      "logit": -0.018671687692403793,
      "selection": "borderline"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_text2img\\01_confident_true_wiki_wiki_35796523_1952-12-01_1992.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_text2img\\02_confident_true_wiki_wiki_24644621_1964-12-15_2010.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_text2img\\03_confident_true_inpainting_inpainting_3832797_1984-01-19_2010.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_text2img\\04_confident_true_insight_insight_32590217_1942-09-19_2014.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_text2img\\05_confident_true_text2img_text2img_31704050_1988-05-11_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_text2img\\06_strong_false_positive_wiki_20728335_1988-07-07_2014.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_text2img\\07_strong_false_positive_wiki_3720944_1920-12-28_1963.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_text2img\\08_strong_false_negative_text2img_43349098_1994-08-30_2014.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_text2img\\09_strong_false_negative_text2img_14609668_1956-12-09_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_text2img\\10_borderline_text2img_6707957_1985-09-04_2015.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2a_t3_holdout_text2img\\panel.png",
  "expanded_panel_path": null,
  "fine_panel_path": null
 }
@@ -0,0 +1,138 @@
 {
  "run": "p2b_resnet18_224",
  "fold": 0,
  "n_candidates": 192,
  "candidate_metrics": {
    "accuracy": 0.9895833333333334,
    "auc_roc": 0.9984085648148149,
    "f1": 0.993006993006993,
    "confusion_matrix": [
      [
        48,
        0
      ],
      [
        2,
        142
      ]
    ]
  },
  "heldout_source": null,
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\74\\3290874_1984-06-18_2004.jpg",
      "basename": "3290874_1984-06-18_2004.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 3.208382226560502e-09,
      "logit": -19.557498931884766,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\29\\19558629_1985-02-28_2007.jpg",
      "basename": "19558629_1985-02-28_2007.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 6.348270176204096e-08,
      "logit": -16.572498321533203,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\06\\25476706_1975-03-27_2009.jpg",
      "basename": "25476706_1975-03-27_2009.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 17.80807876586914,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\81\\3028481_1951-07-05_1977.jpg",
      "basename": "3028481_1951-07-05_1977.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 19.85223960876465,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\10\\726410_1912-05-31_1997.jpg",
      "basename": "726410_1912-05-31_1997.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 17.96767807006836,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\33\\12318533_1982-06-16_2007.jpg",
      "basename": "12318533_1982-06-16_2007.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.00015543345944024622,
      "logit": -8.769137382507324,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\10\\726410_1912-05-31_1997.jpg",
      "basename": "726410_1912-05-31_1997.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.0615716315805912,
      "logit": -2.7240052223205566,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\57\\31543457_1910-02-06_1970.jpg",
      "basename": "31543457_1910-02-06_1970.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.5928639769554138,
      "logit": 0.3758176565170288,
      "selection": "borderline"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\44\\3720944_1920-12-28_1963.jpg",
      "basename": "3720944_1920-12-28_1963.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.7217857837677002,
      "logit": 0.9533369541168213,
      "selection": "borderline"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\04\\34379104_1950-12-07_2013.jpg",
      "basename": "34379104_1950-12-07_2013.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.897236168384552,
      "logit": 2.1668851375579834,
      "selection": "expanded_context"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_resnet18_224\\01_confident_true_wiki_wiki_3290874_1984-06-18_2004.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_resnet18_224\\02_confident_true_wiki_wiki_19558629_1985-02-28_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_resnet18_224\\03_confident_true_inpainting_inpainting_25476706_1975-03-27_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_resnet18_224\\04_confident_true_insight_insight_3028481_1951-07-05_1977.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_resnet18_224\\05_confident_true_text2img_text2img_726410_1912-05-31_1997.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_resnet18_224\\06_strong_false_negative_insight_12318533_1982-06-16_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_resnet18_224\\07_strong_false_negative_inpainting_726410_1912-05-31_1997.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_resnet18_224\\08_borderline_inpainting_31543457_1910-02-06_1970.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_resnet18_224\\09_borderline_insight_3720944_1920-12-28_1963.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_resnet18_224\\10_expanded_context_insight_34379104_1950-12-07_2013.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_resnet18_224\\panel.png",
  "expanded_panel_path": null,
  "fine_panel_path": null
 }
@@ -0,0 +1,138 @@
 {
  "run": "p2b_simplecnn_224",
  "fold": 0,
  "n_candidates": 192,
  "candidate_metrics": {
    "accuracy": 0.7447916666666666,
    "auc_roc": 0.8207465277777778,
    "f1": 0.8122605363984674,
    "confusion_matrix": [
      [
        37,
        11
      ],
      [
        38,
        106
      ]
    ]
  },
  "heldout_source": null,
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\31\\2346431_1980-06-11_2007.jpg",
      "basename": "2346431_1980-06-11_2007.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.09357129782438278,
      "logit": -2.2707886695861816,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\67\\779067_1968-10-08_2008.jpg",
      "basename": "779067_1968-10-08_2008.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.12308616936206818,
      "logit": -1.9635241031646729,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\06\\25476706_1975-03-27_2009.jpg",
      "basename": "25476706_1975-03-27_2009.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9532476663589478,
      "logit": 3.0150113105773926,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\04\\34379104_1950-12-07_2013.jpg",
      "basename": "34379104_1950-12-07_2013.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9883313775062561,
      "logit": 4.439114093780518,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\46\\6385546_1980-05-23_2013.jpg",
      "basename": "6385546_1980-05-23_2013.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9493488669395447,
      "logit": 2.9308156967163086,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\06\\25476706_1975-03-27_2009.jpg",
      "basename": "25476706_1975-03-27_2009.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.868328869342804,
      "logit": 1.8862627744674683,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\52\\9143052_1931-01-21_1991.jpg",
      "basename": "9143052_1931-01-21_1991.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.8662444949150085,
      "logit": 1.8681539297103882,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\35\\20728335_1988-07-07_2014.jpg",
      "basename": "20728335_1988-07-07_2014.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.21595068275928497,
      "logit": -1.289421796798706,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\27\\2103427_1986-06-19_2012.jpg",
      "basename": "2103427_1986-06-19_2012.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.21677419543266296,
      "logit": -1.2845648527145386,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\52\\9143052_1931-01-21_1991.jpg",
      "basename": "9143052_1931-01-21_1991.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.4993416368961334,
      "logit": -0.002633415162563324,
      "selection": "borderline"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_simplecnn_224\\01_confident_true_wiki_wiki_2346431_1980-06-11_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_simplecnn_224\\02_confident_true_wiki_wiki_779067_1968-10-08_2008.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_simplecnn_224\\03_confident_true_inpainting_inpainting_25476706_1975-03-27_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_simplecnn_224\\04_confident_true_insight_insight_34379104_1950-12-07_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_simplecnn_224\\05_confident_true_text2img_text2img_6385546_1980-05-23_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_simplecnn_224\\06_strong_false_positive_wiki_25476706_1975-03-27_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_simplecnn_224\\07_strong_false_positive_wiki_9143052_1931-01-21_1991.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_simplecnn_224\\08_strong_false_negative_text2img_20728335_1988-07-07_2014.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_simplecnn_224\\09_strong_false_negative_insight_2103427_1986-06-19_2012.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_simplecnn_224\\10_borderline_insight_9143052_1931-01-21_1991.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2b_simplecnn_224\\panel.png",
  "expanded_panel_path": null,
  "fine_panel_path": null
 }
@@ -0,0 +1,204 @@
 {
  "run": "p2c_resnet18_facecrop",
  "fold": 0,
  "n_candidates": 192,
  "candidate_metrics": {
    "accuracy": 0.890625,
    "auc_roc": 0.9911747685185185,
    "f1": 0.9219330855018587,
    "confusion_matrix": [
      [
        47,
        1
      ],
      [
        20,
        124
      ]
    ]
  },
  "heldout_source": null,
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\wiki\\74\\3290874_1984-06-18_2004.jpg",
      "basename": "3290874_1984-06-18_2004.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 2.0144028667345992e-08,
      "logit": -17.72035789489746,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\wiki\\29\\19558629_1985-02-28_2007.jpg",
      "basename": "19558629_1985-02-28_2007.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 2.1860007137775028e-08,
      "logit": -17.638607025146484,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\inpainting\\97\\3832797_1984-01-19_2010.jpg",
      "basename": "3832797_1984-01-19_2010.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9999995231628418,
      "logit": 14.502941131591797,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\insight\\83\\2667583_1985-05-02_2009.jpg",
      "basename": "2667583_1985-05-02_2009.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 17.030344009399414,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\text2img\\79\\25469179_1988-04-23_2008.jpg",
      "basename": "25469179_1988-04-23_2008.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9999992847442627,
      "logit": 14.198725700378418,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\wiki\\57\\31543457_1910-02-06_1970.jpg",
      "basename": "31543457_1910-02-06_1970.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.6529371738433838,
      "logit": 0.6319749355316162,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\text2img\\49\\25392649_1991-05-29_2013.jpg",
      "basename": "25392649_1991-05-29_2013.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.004319223575294018,
      "logit": -5.4403510093688965,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\inpainting\\15\\16719015_1940-10-10_1964.jpg",
      "basename": "16719015_1940-10-10_1964.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.007250246591866016,
      "logit": -4.919443130493164,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\insight\\23\\35796523_1952-12-01_1992.jpg",
      "basename": "35796523_1952-12-01_1992.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.5090859532356262,
      "logit": 0.03634767234325409,
      "selection": "borderline"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\text2img\\02\\10386202_1915-02-10_1949.jpg",
      "basename": "10386202_1915-02-10_1949.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.5188130736351013,
      "logit": 0.07528790831565857,
      "selection": "borderline"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\text2img\\08\\3523408_1963-02-01_2015.jpg",
      "basename": "3523408_1963-02-01_2015.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.46507686376571655,
      "logit": -0.1399204581975937,
      "selection": "expanded_context"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\insight\\61\\9200161_1991-02-11_2014.jpg",
      "basename": "9200161_1991-02-11_2014.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.4395407438278198,
      "logit": -0.24302612245082855,
      "selection": "expanded_context"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\text2img\\39\\451239_1954-05-11_2007.jpg",
      "basename": "451239_1954-05-11_2007.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.39794081449508667,
      "logit": -0.4140525758266449,
      "selection": "expanded_context"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\insight\\00\\12611800_1950-08-17_2011.jpg",
      "basename": "12611800_1950-08-17_2011.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.6231862902641296,
      "logit": 0.5030946731567383,
      "selection": "expanded_context"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\inpainting\\41\\37585341_1994-09-01_2015.jpg",
      "basename": "37585341_1994-09-01_2015.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.34164801239967346,
      "logit": -0.6559587717056274,
      "selection": "expanded_context"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\insight\\25\\1932725_1949-01-25_1979.jpg",
      "basename": "1932725_1949-01-25_1979.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.3312528729438782,
      "logit": -0.7025240063667297,
      "selection": "expanded_context"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\01_confident_true_wiki_wiki_3290874_1984-06-18_2004.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\02_confident_true_wiki_wiki_19558629_1985-02-28_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\03_confident_true_inpainting_inpainting_3832797_1984-01-19_2010.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\04_confident_true_insight_insight_2667583_1985-05-02_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\05_confident_true_text2img_text2img_25469179_1988-04-23_2008.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\06_strong_false_positive_wiki_31543457_1910-02-06_1970.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\07_strong_false_negative_text2img_25392649_1991-05-29_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\08_strong_false_negative_inpainting_16719015_1940-10-10_1964.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\09_borderline_insight_35796523_1952-12-01_1992.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\10_borderline_text2img_10386202_1915-02-10_1949.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\11_expanded_context_text2img_3523408_1963-02-01_2015.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\12_expanded_context_insight_9200161_1991-02-11_2014.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\13_expanded_context_text2img_451239_1954-05-11_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\14_expanded_context_insight_12611800_1950-08-17_2011.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\15_expanded_context_inpainting_37585341_1994-09-01_2015.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\16_expanded_context_insight_1932725_1949-01-25_1979.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\panel.png",
  "expanded_panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\panel_expanded.png",
  "fine_panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_resnet18_facecrop\\panel_fine_layer3.png"
 }
@@ -0,0 +1,138 @@
 {
  "run": "p2c_simplecnn_facecrop",
  "fold": 0,
  "n_candidates": 192,
  "candidate_metrics": {
    "accuracy": 0.7552083333333334,
    "auc_roc": 0.8058449074074073,
    "f1": 0.8226415094339623,
    "confusion_matrix": [
      [
        36,
        12
      ],
      [
        35,
        109
      ]
    ]
  },
  "heldout_source": null,
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\wiki\\93\\28699293_1957-04-26_2009.jpg",
      "basename": "28699293_1957-04-26_2009.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.09115341305732727,
      "logit": -2.2996323108673096,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\wiki\\31\\2346431_1980-06-11_2007.jpg",
      "basename": "2346431_1980-06-11_2007.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.10735085606575012,
      "logit": -2.118091106414795,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\inpainting\\52\\9143052_1931-01-21_1991.jpg",
      "basename": "9143052_1931-01-21_1991.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9165919423103333,
      "logit": 2.3969175815582275,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\insight\\17\\32590217_1942-09-19_2014.jpg",
      "basename": "32590217_1942-09-19_2014.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.7911618947982788,
      "logit": 1.331943154335022,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\text2img\\99\\7319399_1981-08-23_2010.jpg",
      "basename": "7319399_1981-08-23_2010.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9600318074226379,
      "logit": 3.1788814067840576,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\wiki\\57\\31543457_1910-02-06_1970.jpg",
      "basename": "31543457_1910-02-06_1970.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.9373878836631775,
      "logit": 2.7061376571655273,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\wiki\\52\\9143052_1931-01-21_1991.jpg",
      "basename": "9143052_1931-01-21_1991.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.7406934499740601,
      "logit": 1.0495760440826416,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\insight\\91\\42596391_1977-03-09_2009.jpg",
      "basename": "42596391_1977-03-09_2009.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.13060443103313446,
      "logit": -1.895625114440918,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\inpainting\\93\\28699293_1957-04-26_2009.jpg",
      "basename": "28699293_1957-04-26_2009.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.16693732142448425,
      "logit": -1.6074904203414917,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\inpainting\\23\\35796523_1952-12-01_1992.jpg",
      "basename": "35796523_1952-12-01_1992.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.5004183053970337,
      "logit": 0.0016733184456825256,
      "selection": "borderline"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_simplecnn_facecrop\\01_confident_true_wiki_wiki_28699293_1957-04-26_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_simplecnn_facecrop\\02_confident_true_wiki_wiki_2346431_1980-06-11_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_simplecnn_facecrop\\03_confident_true_inpainting_inpainting_9143052_1931-01-21_1991.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_simplecnn_facecrop\\04_confident_true_insight_insight_32590217_1942-09-19_2014.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_simplecnn_facecrop\\05_confident_true_text2img_text2img_7319399_1981-08-23_2010.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_simplecnn_facecrop\\06_strong_false_positive_wiki_31543457_1910-02-06_1970.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_simplecnn_facecrop\\07_strong_false_positive_wiki_9143052_1931-01-21_1991.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_simplecnn_facecrop\\08_strong_false_negative_insight_42596391_1977-03-09_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_simplecnn_facecrop\\09_strong_false_negative_inpainting_28699293_1957-04-26_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_simplecnn_facecrop\\10_borderline_inpainting_35796523_1952-12-01_1992.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2c_simplecnn_facecrop\\panel.png",
  "expanded_panel_path": null,
  "fine_panel_path": null
 }
@@ -0,0 +1,138 @@
 {
  "run": "p2d_resnet18_aug",
  "fold": 0,
  "n_candidates": 192,
  "candidate_metrics": {
    "accuracy": 0.9270833333333334,
    "auc_roc": 0.9733796296296297,
    "f1": 0.948905109489051,
    "confusion_matrix": [
      [
        48,
        0
      ],
      [
        14,
        130
      ]
    ]
  },
  "heldout_source": null,
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\67\\779067_1968-10-08_2008.jpg",
      "basename": "779067_1968-10-08_2008.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.00046918127918615937,
      "logit": -7.6640520095825195,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\08\\3523408_1963-02-01_2015.jpg",
      "basename": "3523408_1963-02-01_2015.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.0006820649723522365,
      "logit": -7.289703369140625,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\97\\3832797_1984-01-19_2010.jpg",
      "basename": "3832797_1984-01-19_2010.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9999998807907104,
      "logit": 15.925747871398926,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\97\\3832797_1984-01-19_2010.jpg",
      "basename": "3832797_1984-01-19_2010.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9999997615814209,
      "logit": 15.087677001953125,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\33\\12318533_1982-06-16_2007.jpg",
      "basename": "12318533_1982-06-16_2007.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9999996423721313,
      "logit": 14.999021530151367,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\51\\18249151_1970-12-16_2004.jpg",
      "basename": "18249151_1970-12-16_2004.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.0321250855922699,
      "logit": -3.405465602874756,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\52\\9143052_1931-01-21_1991.jpg",
      "basename": "9143052_1931-01-21_1991.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.0697367787361145,
      "logit": -2.5907397270202637,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\27\\2103427_1986-06-19_2012.jpg",
      "basename": "2103427_1986-06-19_2012.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.4919249415397644,
      "logit": -0.03230302780866623,
      "selection": "borderline"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\72\\43568472_1968-07-26_2014.jpg",
      "basename": "43568472_1968-07-26_2014.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.5248959064483643,
      "logit": 0.09966614097356796,
      "selection": "borderline"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\04\\34379104_1950-12-07_2013.jpg",
      "basename": "34379104_1950-12-07_2013.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.47507616877555847,
      "logit": -0.09977804869413376,
      "selection": "expanded_context"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_resnet18_aug\\01_confident_true_wiki_wiki_779067_1968-10-08_2008.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_resnet18_aug\\02_confident_true_wiki_wiki_3523408_1963-02-01_2015.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_resnet18_aug\\03_confident_true_inpainting_inpainting_3832797_1984-01-19_2010.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_resnet18_aug\\04_confident_true_insight_insight_3832797_1984-01-19_2010.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_resnet18_aug\\05_confident_true_text2img_text2img_12318533_1982-06-16_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_resnet18_aug\\06_strong_false_negative_insight_18249151_1970-12-16_2004.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_resnet18_aug\\07_strong_false_negative_insight_9143052_1931-01-21_1991.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_resnet18_aug\\08_borderline_text2img_2103427_1986-06-19_2012.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_resnet18_aug\\09_borderline_insight_43568472_1968-07-26_2014.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_resnet18_aug\\10_expanded_context_wiki_34379104_1950-12-07_2013.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_resnet18_aug\\panel.png",
  "expanded_panel_path": null,
  "fine_panel_path": null
 }
@@ -0,0 +1,138 @@
 {
  "run": "p2d_simplecnn_aug",
  "fold": 0,
  "n_candidates": 192,
  "candidate_metrics": {
    "accuracy": 0.65625,
    "auc_roc": 0.7565104166666666,
    "f1": 0.725,
    "confusion_matrix": [
      [
        39,
        9
      ],
      [
        57,
        87
      ]
    ]
  },
  "heldout_source": null,
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\67\\779067_1968-10-08_2008.jpg",
      "basename": "779067_1968-10-08_2008.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.10822787880897522,
      "logit": -2.10897159576416,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\31\\2346431_1980-06-11_2007.jpg",
      "basename": "2346431_1980-06-11_2007.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.16694103181362152,
      "logit": -1.6074637174606323,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\06\\25476706_1975-03-27_2009.jpg",
      "basename": "25476706_1975-03-27_2009.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.8760586977005005,
      "logit": 1.955625057220459,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\04\\34379104_1950-12-07_2013.jpg",
      "basename": "34379104_1950-12-07_2013.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.7972180843353271,
      "logit": 1.36899733543396,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\text2img\\59\\40098159_1902-01-15_1988.jpg",
      "basename": "40098159_1902-01-15_1988.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.8695456981658936,
      "logit": 1.8969472646713257,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\06\\25476706_1975-03-27_2009.jpg",
      "basename": "25476706_1975-03-27_2009.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.7830404043197632,
      "logit": 1.283473253250122,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\wiki\\52\\9143052_1931-01-21_1991.jpg",
      "basename": "9143052_1931-01-21_1991.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.764281153678894,
      "logit": 1.1762961149215698,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\95\\27859495_1952-08-08_2014.jpg",
      "basename": "27859495_1952-08-08_2014.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.19983962178230286,
      "logit": -1.3872970342636108,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\insight\\56\\14853156_1981-05-28_2009.jpg",
      "basename": "14853156_1981-05-28_2009.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.20330362021923065,
      "logit": -1.3657732009887695,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\data\\inpainting\\51\\18249151_1970-12-16_2004.jpg",
      "basename": "18249151_1970-12-16_2004.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.5001071095466614,
      "logit": 0.00042841583490371704,
      "selection": "borderline"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_simplecnn_aug\\01_confident_true_wiki_wiki_779067_1968-10-08_2008.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_simplecnn_aug\\02_confident_true_wiki_wiki_2346431_1980-06-11_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_simplecnn_aug\\03_confident_true_inpainting_inpainting_25476706_1975-03-27_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_simplecnn_aug\\04_confident_true_insight_insight_34379104_1950-12-07_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_simplecnn_aug\\05_confident_true_text2img_text2img_40098159_1902-01-15_1988.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_simplecnn_aug\\06_strong_false_positive_wiki_25476706_1975-03-27_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_simplecnn_aug\\07_strong_false_positive_wiki_9143052_1931-01-21_1991.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_simplecnn_aug\\08_strong_false_negative_insight_27859495_1952-08-08_2014.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_simplecnn_aug\\09_strong_false_negative_insight_14853156_1981-05-28_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_simplecnn_aug\\10_borderline_inpainting_18249151_1970-12-16_2004.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2d_simplecnn_aug\\panel.png",
  "expanded_panel_path": null,
  "fine_panel_path": null
 }
@@ -0,0 +1,138 @@
 {
  "run": "p2e_resnet18_facecrop_aug",
  "fold": 0,
  "n_candidates": 192,
  "candidate_metrics": {
    "accuracy": 0.9322916666666666,
    "auc_roc": 0.9910300925925927,
    "f1": 0.9530685920577617,
    "confusion_matrix": [
      [
        47,
        1
      ],
      [
        12,
        132
      ]
    ]
  },
  "heldout_source": null,
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\wiki\\13\\25021613_1996-10-24_2012.jpg",
      "basename": "25021613_1996-10-24_2012.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 4.415973648974614e-07,
      "logit": -14.632866859436035,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\wiki\\56\\14853156_1981-05-28_2009.jpg",
      "basename": "14853156_1981-05-28_2009.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 1.1105105386377545e-06,
      "logit": -13.710689544677734,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\inpainting\\44\\3720944_1920-12-28_1963.jpg",
      "basename": "3720944_1920-12-28_1963.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 18.160795211791992,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\insight\\79\\25469179_1988-04-23_2008.jpg",
      "basename": "25469179_1988-04-23_2008.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 24.2982120513916,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\text2img\\15\\16719015_1940-10-10_1964.jpg",
      "basename": "16719015_1940-10-10_1964.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 1.0,
      "logit": 22.898853302001953,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\wiki\\57\\31543457_1910-02-06_1970.jpg",
      "basename": "31543457_1910-02-06_1970.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.6879352927207947,
      "logit": 0.7904843091964722,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\text2img\\37\\3272137_1943-04-22_2010.jpg",
      "basename": "3272137_1943-04-22_2010.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.000333707983372733,
      "logit": -8.004910469055176,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\insight\\46\\6385546_1980-05-23_2013.jpg",
      "basename": "6385546_1980-05-23_2013.jpg",
      "source": "insight",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.010300145484507084,
      "logit": -4.565243721008301,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\text2img\\49\\25392649_1991-05-29_2013.jpg",
      "basename": "25392649_1991-05-29_2013.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.4931047856807709,
      "logit": -0.027582719922065735,
      "selection": "borderline"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\text2img\\39\\451239_1954-05-11_2007.jpg",
      "basename": "451239_1954-05-11_2007.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.5096341967582703,
      "logit": 0.03854157030582428,
      "selection": "borderline"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_resnet18_facecrop_aug\\01_confident_true_wiki_wiki_25021613_1996-10-24_2012.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_resnet18_facecrop_aug\\02_confident_true_wiki_wiki_14853156_1981-05-28_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_resnet18_facecrop_aug\\03_confident_true_inpainting_inpainting_3720944_1920-12-28_1963.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_resnet18_facecrop_aug\\04_confident_true_insight_insight_25469179_1988-04-23_2008.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_resnet18_facecrop_aug\\05_confident_true_text2img_text2img_16719015_1940-10-10_1964.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_resnet18_facecrop_aug\\06_strong_false_positive_wiki_31543457_1910-02-06_1970.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_resnet18_facecrop_aug\\07_strong_false_negative_text2img_3272137_1943-04-22_2010.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_resnet18_facecrop_aug\\08_strong_false_negative_insight_6385546_1980-05-23_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_resnet18_facecrop_aug\\09_borderline_text2img_25392649_1991-05-29_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_resnet18_facecrop_aug\\10_borderline_text2img_451239_1954-05-11_2007.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_resnet18_facecrop_aug\\panel.png",
  "expanded_panel_path": null,
  "fine_panel_path": null
 }
@@ -0,0 +1,138 @@
 {
  "run": "p2e_simplecnn_facecrop_aug",
  "fold": 0,
  "n_candidates": 192,
  "candidate_metrics": {
    "accuracy": 0.6510416666666666,
    "auc_roc": 0.7358217592592592,
    "f1": 0.7196652719665272,
    "confusion_matrix": [
      [
        39,
        9
      ],
      [
        58,
        86
      ]
    ]
  },
  "heldout_source": null,
  "selected": [
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\wiki\\31\\2346431_1980-06-11_2007.jpg",
      "basename": "2346431_1980-06-11_2007.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.1969025731086731,
      "logit": -1.4057669639587402,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\wiki\\93\\28699293_1957-04-26_2009.jpg",
      "basename": "28699293_1957-04-26_2009.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 0,
      "prob_fake": 0.21053381264209747,
      "logit": -1.3217107057571411,
      "selection": "confident_true_wiki"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\inpainting\\52\\9143052_1931-01-21_1991.jpg",
      "basename": "9143052_1931-01-21_1991.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.7768715620040894,
      "logit": 1.2475274801254272,
      "selection": "confident_true_inpainting"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\insight\\04\\34379104_1950-12-07_2013.jpg",
      "basename": "34379104_1950-12-07_2013.jpg",
      "source": "insight",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.7978224158287048,
      "logit": 1.372739553451538,
      "selection": "confident_true_insight"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\text2img\\46\\6385546_1980-05-23_2013.jpg",
      "basename": "6385546_1980-05-23_2013.jpg",
      "source": "text2img",
      "label": 1,
      "pred": 1,
      "prob_fake": 0.9004967212677002,
      "logit": 2.202756643295288,
      "selection": "confident_true_text2img"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\wiki\\13\\25021613_1996-10-24_2012.jpg",
      "basename": "25021613_1996-10-24_2012.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.6920362710952759,
      "logit": 0.8096563220024109,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\wiki\\29\\19558629_1985-02-28_2007.jpg",
      "basename": "19558629_1985-02-28_2007.jpg",
      "source": "wiki",
      "label": 0,
      "pred": 1,
      "prob_fake": 0.6103906035423279,
      "logit": 0.44895440340042114,
      "selection": "strong_false_positive"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\inpainting\\67\\779067_1968-10-08_2008.jpg",
      "basename": "779067_1968-10-08_2008.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.22937096655368805,
      "logit": -1.2118664979934692,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\inpainting\\93\\28699293_1957-04-26_2009.jpg",
      "basename": "28699293_1957-04-26_2009.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.24612900614738464,
      "logit": -1.1193655729293823,
      "selection": "strong_false_negative"
    },
    {
      "path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\cropped\\classifier\\inpainting\\25\\1932725_1949-01-25_1979.jpg",
      "basename": "1932725_1949-01-25_1979.jpg",
      "source": "inpainting",
      "label": 1,
      "pred": 0,
      "prob_fake": 0.4987809658050537,
      "logit": -0.004876129329204559,
      "selection": "borderline"
    }
  ],
  "image_paths": [
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_simplecnn_facecrop_aug\\01_confident_true_wiki_wiki_2346431_1980-06-11_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_simplecnn_facecrop_aug\\02_confident_true_wiki_wiki_28699293_1957-04-26_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_simplecnn_facecrop_aug\\03_confident_true_inpainting_inpainting_9143052_1931-01-21_1991.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_simplecnn_facecrop_aug\\04_confident_true_insight_insight_34379104_1950-12-07_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_simplecnn_facecrop_aug\\05_confident_true_text2img_text2img_6385546_1980-05-23_2013.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_simplecnn_facecrop_aug\\06_strong_false_positive_wiki_25021613_1996-10-24_2012.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_simplecnn_facecrop_aug\\07_strong_false_positive_wiki_19558629_1985-02-28_2007.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_simplecnn_facecrop_aug\\08_strong_false_negative_inpainting_779067_1968-10-08_2008.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_simplecnn_facecrop_aug\\09_strong_false_negative_inpainting_28699293_1957-04-26_2009.png",
    "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_simplecnn_facecrop_aug\\10_borderline_inpainting_1932725_1949-01-25_1979.png"
  ],
  "panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_simplecnn_facecrop_aug\\panel.png",
  "expanded_panel_path": null,
  "fine_panel_path": null
 }
@@ -0,0 +1,37 @@
 {
  "phase": "phase2",
  "best_existing_run": "p2c_resnet18_facecrop",
  "best_existing_auc": 0.9755,
  "decisions": [
    {
      "choice": "input size",
      "decision": "224x224",
      "evidence": "ResNet18 improves from AUC 0.9366 to 0.9660.",
      "confidence": "high"
    },
    {
      "choice": "face crop",
      "decision": "enable",
      "evidence": "Best Phase 2 run is p2c_resnet18_facecrop with AUC 0.9755.",
      "confidence": "medium-high"
    },
    {
      "choice": "augmentation",
      "decision": "disable for current 20% setting",
      "evidence": "p2e_resnet18_facecrop_aug reaches AUC 0.9737, below facecrop-only 0.9755; SimpleCNN drops sharply.",
      "confidence": "low"
    },
    {
      "choice": "normalization",
      "decision": "ImageNet/default",
      "evidence": "real_norm is only +0.0018 AUC and is less aligned with pretrained ImageNet weights.",
      "confidence": "medium"
    },
    {
      "choice": "source generalization",
      "decision": "report as limitation and diagnostic target",
      "evidence": "Holding out text2img and insight drops pairwise AUC to 0.7595 and 0.8421.",
      "confidence": "high"
    }
  ]
 }
@@ -0,0 +1,104 @@
 {
  "phase": "phase4",
  "best_auc_run": "p4_convnext_tiny_100pct",
  "best_auc": 0.9954,
  "selected_detector_run": "p4_convnext_tiny_100pct",
  "selected_detector_reason": "Lowest false positive rate on real images and best balanced accuracy at 100% data.",
  "notes": [
    "20% rows come from the matching Phase 3 runs and act as scaling anchors.",
    "50% and 100% rows come from Phase 4 logs.",
    "ConvNeXt-Tiny is the AUC winner and also the practical detector when false positives on real images matter most."
  ],
  "summary_rows": [
    {
      "model": "ResNet50",
      "scale_pct": 20,
      "run": "p3_resnet50",
      "auc": 0.9857046296296297,
      "accuracy": 0.9532083333333334,
      "f1": 0.9687705256330055,
      "balanced_accuracy": 0.9385277777777778,
      "macro_f1": 0.9377251247311893
    },
    {
      "model": "ResNet50",
      "scale_pct": 50,
      "run": "p4_resnet50_50pct",
      "auc": 0.9921022333333335,
      "accuracy": 0.9607166666666667,
      "f1": 0.973649056957159,
      "balanced_accuracy": 0.9536555555555555,
      "macro_f1": 0.9482411270477236
    },
    {
      "model": "ResNet50",
      "scale_pct": 100,
      "run": "p4_resnet50_100pct",
      "auc": 0.9949671268518518,
      "accuracy": 0.9692166666666667,
      "f1": 0.9793757797895071,
      "balanced_accuracy": 0.9638111111111111,
      "macro_f1": 0.9593474615494658
    },
    {
      "model": "EfficientNet-B0",
      "scale_pct": 20,
      "run": "p3_efficientnet_b0",
      "auc": 0.9844867592592592,
      "accuracy": 0.9450000000000001,
      "f1": 0.9628492625462333,
      "balanced_accuracy": 0.9397222222222222,
      "macro_f1": 0.9284971237471479
    },
    {
      "model": "EfficientNet-B0",
      "scale_pct": 50,
      "run": "p4_efficientnet_b0_50pct",
      "auc": 0.9911186185185186,
      "accuracy": 0.9576499999999999,
      "f1": 0.9714437706354593,
      "balanced_accuracy": 0.9541444444444445,
      "macro_f1": 0.9446884831225546
    },
    {
      "model": "EfficientNet-B0",
      "scale_pct": 100,
      "run": "p4_efficientnet_b0_100pct",
      "auc": 0.9949471324074075,
      "accuracy": 0.96835,
      "f1": 0.9787227370502695,
      "balanced_accuracy": 0.9656777777777779,
      "macro_f1": 0.9584469550394183
    },
    {
      "model": "ConvNeXt-Tiny",
      "scale_pct": 20,
      "run": "p3_convnext_tiny",
      "auc": 0.9867751388888889,
      "accuracy": 0.9473333333333332,
      "f1": 0.9650225390458838,
      "balanced_accuracy": 0.9261666666666667,
      "macro_f1": 0.9292641404681354
    },
    {
      "model": "ConvNeXt-Tiny",
      "scale_pct": 50,
      "run": "p4_convnext_tiny_50pct",
      "auc": 0.9926127555555556,
      "accuracy": 0.96065,
      "f1": 0.9735230102651047,
      "balanced_accuracy": 0.9562111111111111,
      "macro_f1": 0.9484169329127863
    },
    {
      "model": "ConvNeXt-Tiny",
      "scale_pct": 100,
      "run": "p4_convnext_tiny_100pct",
      "auc": 0.9953774120370371,
      "accuracy": 0.9679166666666668,
      "f1": 0.9783980914742021,
      "balanced_accuracy": 0.9662444444444445,
      "macro_f1": 0.9579703600254157
    }
  ]
 }
--- a/Show More
+++ b/Show More