Delete generator/notebooks/_build.py

.gitignore

@@ -67,3 +67,6 @@ generator/outputs/samples/*
 .venv/
 .ipynb_checkpoints/
 __pycache__/
+
+#Presentation
+presentation_inputs.zip

README.md

@@ -1,125 +1,264 @@
-# DRL_PROJ — DeepFake Detection
+# Deep learning face project
 
-Deep learning project for binary deepfake detection on the DeepFakeFace dataset.
+This repository contains a two-part deep learning project on the
+DeepFakeFace (DFF) dataset:
 
-## Project structure
+1. **Classifier:** detect whether a face image is real or fake.
+2. **Generator:** train generative models that produce new fake face images.
 
-```
+The project is written as an experimental report. The notebooks are the main
+deliverable: they show the pipeline, the intermediate failures, the ablations,
+the decisions, and the final models. Read them in order.
+
+## Project story
+
+The work follows the same principle in both parts: start with a simple
+baseline, inspect what fails, change one important factor at a time, and keep
+the evidence tied to saved logs and saved artifacts.
+
+For the **classifier**, the story moves from dataset understanding to
+preprocessing, baseline models, controlled ablations, Grad-CAM inspection,
+stronger model families, and data scaling. The final practical classifier is a
+ResNet50-style pipeline using face crops, 224×224 inputs, ImageNet/default
+normalization, and no stochastic augmentation at validation/test time.
+
+For the **generator**, the story starts with raw baseline failures, then locks
+the data pipeline before comparing three parallel model-family branches:
+GAN, VAE, and DDPM. The final comparison keeps quality versus speed central:
+DDPM gives the best saved FID and visual quality, GAN is the best
+quality-speed compromise, and VAE is the fastest but smoothest option.
+
+## How to read the project
+
+Start with the classifier notebooks, then read the generator notebooks. The
+generator has one linear setup stage followed by three parallel branches:
+GAN, VAE, and DDPM. Those branches are numbered in reading order, but they are
+conceptually parallel experiments after the pipeline is selected.
+
+### Classifier notebooks
+
+Read these first:
+
+1. `classifier/notebooks/01_eda.ipynb`  
+   Dataset composition, real/fake source mapping, image statistics, and
+   shortcut risks.
+2. `classifier/notebooks/02_preprocessing.ipynb`  
+   Deterministic preprocessing, train-only augmentation, face crops, and
+   normalization.
+3. `classifier/notebooks/03_phase1_analysis.ipynb`  
+   SimpleCNN and ResNet18 controlled baselines.
+4. `classifier/notebooks/04_phase2_analysis.ipynb`  
+   Resolution, normalization, source holdouts, facecrop, and augmentation
+   ablations.
+5. `classifier/notebooks/05_gradcam_analysis.ipynb`  
+   Qualitative localization analysis across the classifier pipeline.
+6. `classifier/notebooks/06_phase3_model_family_analysis.ipynb`  
+   Stronger pretrained model families and the ResNet50 practical choice.
+7. `classifier/notebooks/07_phase4_data_scaling_analysis.ipynb`  
+   Data scaling for strong backbones and the final classifier decision.
+
+### Generator notebooks
+
+Read these after the classifier:
+
+1. `generator/notebooks/01_baseline_sanity_check.ipynb`  
+   Raw baseline failures and why the data pipeline must be fixed first.
+2. `generator/notebooks/02_pipeline_selection.ipynb`  
+   Controlled pipeline ablations: resolution, alignment, augmentation, and
+   raw/aligned mixing.
+3. `generator/notebooks/03_gan_stability_progression.ipynb`  
+   GAN branch: DCGAN → WGAN-GP → spectral normalization + GroupNorm +
+   self-attention → 128×128 check.
+4. `generator/notebooks/04_vae_loss_progression.ipynb`  
+   VAE branch: MSE + KL → perceptual loss → PatchGAN adversarial loss.
+5. `generator/notebooks/05_ddpm_recipe_progression.ipynb`  
+   DDPM branch: linear schedule → cosine schedule → v-prediction → wider
+   backbone.
+6. `generator/notebooks/06_final_family_comparison.ipynb`  
+   Final comparison of the selected GAN, VAE, and DDPM recipes under saved
+   Phase 5 conditions.
+7. `generator/notebooks/07_final_sample_showcase.ipynb`  
+   Curated final sample examples from saved outputs. This is qualitative
+   showcase material, not a replacement for FID.
+
+## What the notebooks do
+
+The notebooks are analysis/report chapters. They load existing configs, logs,
+figures, saved sample grids, checkpoints, and prediction summaries. They are
+not intended to launch new training runs.
+
+When a notebook shows a plot or image grid, the surrounding markdown explains:
+
+- what the artifact shows;
+- why it is needed;
+- how it supports the phase decision;
+- what limitation remains.
+
+This is important because the project is evaluated not only by final
+performance, but by the documented evolution of the solution.
+
+## Repository layout
+
+```text
 DRL_PROJ/
-  classifier/       ← discriminative model (real vs. fake classifier)
+  classifier/
-    src/            ← model definitions, training, evaluation, preprocessing
+    configs/       experiment configs by phase
-    configs/        ← experiment configs organised by phase
+    notebooks/     classifier report notebooks
-      phase1/       ← baseline models (SimpleCNN, ResNet18)
+    outputs/       saved logs, figures, Grad-CAM panels, checkpoints
-      phase2/       ← architecture sweep (ResNet variants, face-crop)
+    src/           classifier data, models, training, evaluation
-      phase3/       ← EfficientNet, ViT, frequency-aware training
+    tests/         unit and smoke tests
-      phase4/       ← ensemble strategies
+    tools/         facecrop, Grad-CAM, inference, reevaluation helpers
-    tools/          ← analyse.py, ensemble.py, inference.py, facecrop.py
+
-    notebooks/      ← EDA, preprocessing, evaluation, GradCAM
+  generator/
-    outputs/        ← models, logs, figures (gitignored except .pt/.json)
+    configs/       generator configs by phase/family
-    run.py          ← main training entry point
+    notebooks/     generator report notebooks and notebook builder
-  generator/        ← generative model (GAN / VAE / diffusion) — in progress
+    outputs/       saved logs, sample grids, final showcase artifacts
-  pipeline/         ← Vast.ai ephemeral GPU orchestration
+    src/           generator data, models, training, metrics
-  data/             ← dataset root (gitignored)
+    tests/         unit and smoke tests
-  cropped/          ← MTCNN pre-cropped faces (gitignored)
+    tools/         sampling and utility scripts
-    classifier/     ← bbox crops for the classifier
+
-    generator/      ← landmark-aligned crops for the generator
+  data/            original DFF dataset root, not committed
+  cropped/         preprocessed face crops, not committed
+  docs/            project statement and supporting documents
+  pipeline/        optional remote/GPU orchestration helpers
 ```
 
+## Rebuilding the generator notebooks
+
+The generator notebooks are generated from a single source file:
+
+```bash
+cd generator/notebooks
+python _build.py
+```
+
+That builder writes the numbered generator notebooks listed above. It uses
+existing saved logs and artifacts; it does not train models.
+
 ## Setup
 
-Create a local environment when you want to run the code directly on a machine you control:
+Create a conda environment and install the project requirements:
 
 ```bash
-python3 -m venv .venv
+conda create -n drl python=3.12
-source .venv/bin/activate
+conda activate drl
 python -m pip install --upgrade pip setuptools wheel
 python -m pip install -r requirements.txt
 ```
 
-## Local Training
+Use **Python 3.12**; some dependencies (for example `facenet-pytorch`) are
+unreliable on 3.13+.
+
+The raw dataset should be placed under `data/`. Preprocessed crops are stored
+under `cropped/`. These folders are intentionally not committed. To download
+and extract the dataset:
 
 ```bash
-python3 classifier/run.py classifier/configs/phase2/p2_resnet18_facecrop.json
+python classifier/tools/fetch_ds.py
-python3 classifier/run.py classifier/configs/phase3/p3_efficientnet_b0.json
+python classifier/tools/fetch_ds.py --data-dir /path/to/DFF
 ```
 
-## Ephemeral Vast.ai Pipeline
+Expected layout under the data root: `wiki/<identity>/*.jpg`,
+`inpainting/...`, `text2img/...`, `insight/...`.
 
-The deployment/orchestration path now lives under [`pipeline/`](/run/host/mnt/shared/UP/DRL/DRL_PROJ/pipeline/README.md).
+## Classifier — training
 
-One-time setup:
+From the repository root:
 
 ```bash
-cat > pipeline/.env <<'EOF'
+# CPU (slow but valid)
-VAST_API_KEY=<your-api-key>
+python classifier/run.py classifier/configs/phase4/p4_convnext_tiny_100pct.json
-VAST_SSH_PRIVATE_KEY=/home/your-user/.ssh/id_ed25519
+
-EOF
+# GPU when CUDA is available
+python classifier/run.py classifier/configs/phase4/p4_convnext_tiny_100pct.json --use-gpu
 ```
 
-End-to-end ephemeral run:
+Training uses 5-fold stratified group cross-validation. Per-fold checkpoints
+are saved as `classifier/outputs/models/{run_name}_fold{k}_best.pt` (and
+`_final.pt`). Override data or output locations with `--data-dir` and
+`--output-root`.
+
+**Primary delivery model** (best Phase 4 detector): config
+`classifier/configs/phase4/p4_convnext_tiny_100pct.json` with per-fold
+weights `classifier/outputs/models/p4_convnext_tiny_100pct_fold*_best.pt`.
+
+## Classifier — inference
+
+Classify a single image as real or fake:
 
 ```bash
-python3 -m pipeline run classifier/configs/phase2/p2_resnet18_facecrop.json --upload-data
+python classifier/tools/inference.py image.jpg classifier/configs/phase4/p4_convnext_tiny_100pct.json
 ```
 
-Interactive offer selection:
+This loads the config and the matching checkpoint, runs the image through the
+model, and prints a result like:
+
+```
+Image : image.jpg
+Model : p4_convnext_tiny_100pct (convnext_tiny)
+Device: cuda
+Result: FAKE  (confidence: 74.7%)
+P(fake): 0.7466   P(real): 0.2534
+```
+
+If you omit `--checkpoint`, the tool automatically looks for a saved
+checkpoint under `classifier/outputs/models/` — first the single-run
+`{run_name}_best.pt`, then CV fold files `{run_name}_fold{k}_best.pt`, then
+`{run_name}_fold{k}_final.pt`. To use a specific fold:
 
 ```bash
-python3 -m pipeline offers --select-offer
+python classifier/tools/inference.py image.jpg classifier/configs/phase4/p4_convnext_tiny_100pct.json \
+  --checkpoint classifier/outputs/models/p4_convnext_tiny_100pct_fold0_best.pt
 ```
 
-You can override the ranking mode per run:
+## Generator — training
+
+From the repository root:
 
 ```bash
-python3 -m pipeline offers --sort price
+python generator/run.py generator/configs/phase0/p0_vae.json
-python3 -m pipeline offers --sort performance
+python generator/run.py generator/configs/phase0/p0_ddpm.json
-python3 -m pipeline offers --sort performance --price 0.14
 ```
 
-You can also filter by region:
+Generator training expects real-face images (default source is `wiki`); use
+`--data-dir` to point at your dataset tree. Checkpoints are saved under
+`generator/outputs/models/{run_name}_final_ema.pt` (EMA shadow) and
+`{run_name}_best_ema.pt` (lowest-FID snapshot).
+
+## Generator — inference (sampling)
+
+Generate 4×4 sample grids from Phase 5 EMA checkpoints:
 
 ```bash
-python3 -m pipeline offers --select-offer --region europe
+python generator/tools/sampling.py --models p5_gan p5_vae p5_ddpm --samples 10
-python3 -m pipeline offers --select-offer --region Portugal
-python3 -m pipeline offers --select-offer --region US
-python3 -m pipeline offers --select-offer --region europe --price 0.14
 ```
 
-To inspect which region strings are currently available from the search results:
+Options:
 
-```bash
+- `--models` — which models to sample from (`p5_gan`, `p5_vae`, `p5_ddpm`;
-python3 -m pipeline offers --list-regions
+  defaults to all three).
-```
+- `--samples` — number of grids per model (default 10).
+- `--output-dir` — where to write the PNGs (default
+  `generator/outputs/samples/final_comparison/`).
+- `--truncation` — optional latent truncation for the GAN (lower = less
+  diversity but sharper).
+- `--device` — `cuda` or `cpu` (default: auto-detect).
 
-That command:
+Each grid is a 4×4 PNG of 16 images sampled from the model's EMA weights.
-- ensures your SSH public key is registered with Vast.ai
+GAN samples are drawn from random latent vectors, VAE samples decode from the
-- searches offers using the filters in `pipeline/defaults/vast.json`
+learned prior, and DDPM samples use 50-step DDIM.
-- creates an instance
-- waits for SSH readiness
-- syncs the repo
-- uploads `data/` when `--upload-data` is set
-- runs `python3 classifier/run.py ...`
-- downloads `classifier/outputs/`
-- for generator runs, rsyncs `generator/outputs/` back every 25 epochs and again at completion
-- destroys the instance automatically unless `--keep-on-failure` is set
 
-Useful commands:
+## Final takeaway
 
-```bash
+The project is best understood as a sequence of controlled decisions:
-python3 -m pipeline up
-python3 -m pipeline status <instance_id>
-python3 -m pipeline down <instance_id>
-```
 
-To override the default Vast search/runtime settings, copy `pipeline/defaults/vast.json`, edit it, and pass:
+1. cleanly define the data and preprocessing;
+2. establish simple baselines;
+3. improve one factor at a time;
+4. compare model families using saved evidence;
+5. report both performance and limitations.
 
-```bash
+The classifier becomes reliable through source-aware preprocessing, stronger
-python3 -m pipeline run classifier/configs/phase3/p3_efficientnet_b0.json --pipeline-config /path/to/vast.override.json
+pretrained backbones, and scaling. The generator improves by first locking the
-```
+face-aligned pipeline and then selecting the best recipe inside each model
-
+family before the final GAN/VAE/DDPM comparison.
-The default policy in `pipeline/defaults/vast.json` now targets:
-- `1x` GPU
-- `RTX 3090` or `RTX 3090 Ti`
-- `<= $0.20/hour`
-- sorted by `dlperf` descending
-- uses `vastai/pytorch:latest` as the default image

classifier/configs/smoke/smoke.json

@@ -0,0 +1,18 @@
+{
+  "extends": "../shared.json",
+  "run_name": "smoke",
+  "backbone": "simple_cnn",
+  "cnn_preset": "micro",
+  "dropout": 0.0,
+  "epochs": 1,
+  "cv_folds": 2,
+  "image_size": 64,
+  "batch_size": 8,
+  "num_workers": 0,
+  "early_stopping_patience": 0,
+  "subsample": 1.0,
+  "augment": false,
+  "lr": 0.001,
+  "T_max": 1,
+  "data_dir": "data"
+}

classifier/outputs/analysis/gradcam/gradcam_artifact_summary.csv

@@ -1,16 +0,0 @@
-run,n_candidates,n_images,heldout_source,candidate_auc,candidate_acc,panel_path,expanded_panel_path
-p1_simplecnn_baseline,192,10,,0.8378182870370371,0.7395833333333334,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p1_simplecnn_baseline\panel.png,
-p1_resnet18_baseline,192,10,,0.9769965277777778,0.9270833333333334,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p1_resnet18_baseline\panel.png,
-p2a_t1_original,192,10,,0.9984085648148149,0.9895833333333334,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p2a_t1_original\panel.png,
-p2a_t2_real_norm,192,10,,0.9939236111111112,0.9791666666666666,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p2a_t2_real_norm\panel.png,
-p2a_t3_holdout_text2img,240,10,text2img,0.9264322916666667,0.775,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p2a_t3_holdout_text2img\panel.png,
-p2a_t3_holdout_inpainting,240,10,inpainting,0.9819878472222222,0.9333333333333333,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p2a_t3_holdout_inpainting\panel.png,
-p2a_t3_holdout_insight,240,10,insight,0.9549696180555556,0.7625,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p2a_t3_holdout_insight\panel.png,
-p2b_simplecnn_224,192,10,,0.8207465277777778,0.7447916666666666,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p2b_simplecnn_224\panel.png,
-p2b_resnet18_224,192,10,,0.9984085648148149,0.9895833333333334,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p2b_resnet18_224\panel.png,
-p2c_simplecnn_facecrop,192,10,,0.8058449074074073,0.7552083333333334,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p2c_simplecnn_facecrop\panel.png,
-p2c_resnet18_facecrop,192,16,,0.9911747685185185,0.890625,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p2c_resnet18_facecrop\panel.png,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p2c_resnet18_facecrop\panel_expanded.png
-p2d_simplecnn_aug,192,10,,0.7565104166666666,0.65625,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p2d_simplecnn_aug\panel.png,
-p2d_resnet18_aug,192,10,,0.9733796296296297,0.9270833333333334,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p2d_resnet18_aug\panel.png,
-p2e_simplecnn_facecrop_aug,192,10,,0.7358217592592592,0.6510416666666666,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p2e_simplecnn_facecrop_aug\panel.png,
-p2e_resnet18_facecrop_aug,192,10,,0.9910300925925927,0.9322916666666666,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\p2e_resnet18_facecrop_aug\panel.png,

classifier/outputs/analysis/gradcam/gradcam_evolution_panels.csv

@@ -1,4 +0,0 @@
-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

classifier/outputs/analysis/gradcam/p1_resnet18_baseline_summary.json

@@ -1,137 +0,0 @@
-{
-  "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"
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classifier/outputs/analysis/gradcam/p1_simplecnn_baseline_summary.json

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classifier/outputs/analysis/gradcam/p2a_t1_original_summary.json

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classifier/outputs/analysis/gradcam/p2a_t2_real_norm_summary.json

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classifier/outputs/analysis/gradcam/p2a_t3_holdout_inpainting_summary.json

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classifier/outputs/analysis/gradcam/p2a_t3_holdout_insight_summary.json

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-{
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classifier/outputs/analysis/gradcam/p2a_t3_holdout_text2img_summary.json

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-{
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classifier/outputs/analysis/gradcam/p2b_resnet18_224_summary.json

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classifier/outputs/analysis/gradcam/p2b_simplecnn_224_summary.json

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classifier/outputs/analysis/gradcam/p2c_resnet18_facecrop_summary.json

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classifier/outputs/analysis/gradcam/p2c_simplecnn_facecrop_summary.json

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-{
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classifier/outputs/analysis/gradcam/p2d_resnet18_aug_summary.json

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classifier/outputs/analysis/gradcam/p2d_simplecnn_aug_summary.json

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classifier/outputs/analysis/gradcam/p2e_resnet18_facecrop_aug_summary.json

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-      "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
-}

classifier/outputs/analysis/gradcam/p2e_simplecnn_facecrop_aug_summary.json

@@ -1,137 +0,0 @@
-{
-  "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
-}

classifier/outputs/analysis/phase2_story_summary.json

@@ -1,38 +0,0 @@
-{
-  "phase": "phase2",
-  "best_existing_run": "p2c_resnet18_facecrop",
-  "best_existing_auc": 0.9755,
-  "decisions": [
-    {
-      "choice": "input size",
-      "decision": "224x224",
-      "evidence": "ResNet18 improves from 0.9366 to 0.9660 AUC.",
-      "confidence": "high"
-    },
-    {
-      "choice": "face crop",
-      "decision": "enable",
-      "evidence": "Best 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 is 0.9737, below facecrop-only 0.9755; SimpleCNN drops sharply.",
-      "confidence": "low"
-    },
-    {
-      "choice": "normalization",
-      "decision": "ImageNet/default",
-      "evidence": "real_norm is only +0.0018 and is less aligned with pretrained weights.",
-      "confidence": "medium"
-    },
-    {
-      "choice": "source generalization",
-      "decision": "report as limitation and diagnostic target",
-      "evidence": "Holdout text2img and insight pairwise AUC drop to 0.7595 and 0.8421.",
-      "confidence": "high"
-    }
-  ],
-  "note": "Generated by 04_phase2_analysis.ipynb when this cell is executed."
-}

classifier/tests/smoke_test.py

@@ -0,0 +1,90 @@
+"""
+End-to-end smoke test: synthetic DFF layout -> short CV train -> inference.
+
+Run from the classifier package root:
+
+    cd classifier && conda activate drl && python -m unittest tests.smoke_test -v
+"""
+import io
+import subprocess
+import sys
+import unittest
+from pathlib import Path
+
+from PIL import Image
+
+_CLASSIFIER_ROOT = Path(__file__).resolve().parents[1]
+_SMOKE_CFG = _CLASSIFIER_ROOT / "configs" / "smoke" / "smoke.json"
+
+
+def _write_synthetic_dff(data_root: Path, *, n_identities: int = 16) -> None:
+    """Minimal DeepFakeFace-style tree: wiki + three fake sources, shared basenames per identity."""
+    sources = ("wiki", "inpainting", "text2img", "insight")
+    for i in range(n_identities):
+        stem = f"id{i:03d}"
+        for src in sources:
+            d = data_root / src / f"person_{i:03d}"
+            d.mkdir(parents=True, exist_ok=True)
+            buf = io.BytesIO()
+            Image.new(
+                "RGB",
+                (96, 96),
+                color=(min(20 + i * 11, 255), 80, 120 if src == "wiki" else 40),
+            ).save(buf, format="JPEG")
+            # Same filename stem across all sources → one CV group per identity (matches DFF).
+            (d / f"{stem}.jpg").write_bytes(buf.getvalue())
+
+
+class SmokeTrainInferTests(unittest.TestCase):
+    def test_local_smoke_train_then_inference(self):
+        import tempfile
+
+        with tempfile.TemporaryDirectory() as td:
+            tmp = Path(td)
+            data_dir = tmp / "data"
+            out_root = tmp / "outputs"
+            _write_synthetic_dff(data_dir)
+
+            sys.path.insert(0, str(_CLASSIFIER_ROOT))
+            import run as train_run
+
+            train_run.main(
+                str(_SMOKE_CFG),
+                data_dir_override=str(data_dir),
+                output_root=str(out_root),
+                use_gpu=False,
+            )
+
+            models_dir = out_root / "models"
+            ck_fold0 = models_dir / "smoke_fold0_best.pt"
+            if not ck_fold0.is_file():
+                ck_fold0 = models_dir / "smoke_fold0_final.pt"
+            self.assertTrue(
+                ck_fold0.is_file(),
+                f"Expected fold-0 checkpoint under {models_dir}",
+            )
+
+            probe = tmp / "probe.jpg"
+            probe.write_bytes((data_dir / "wiki" / "person_000" / "id000.jpg").read_bytes())
+
+            cmd = [
+                sys.executable,
+                str(_CLASSIFIER_ROOT / "tools" / "inference.py"),
+                str(probe),
+                str(_SMOKE_CFG),
+                "--checkpoint",
+                str(ck_fold0),
+            ]
+            proc = subprocess.run(
+                cmd,
+                cwd=str(_CLASSIFIER_ROOT),
+                capture_output=True,
+                text=True,
+                timeout=300,
+            )
+            self.assertEqual(proc.returncode, 0, proc.stderr + proc.stdout)
+            self.assertIn("P(fake)", proc.stdout)
+
+
+if __name__ == "__main__":
+    unittest.main()

classifier/tools/artifact_chk.py

@@ -18,8 +18,9 @@ def parse_args():
 
 
 def iter_config_paths(config_root: Path):
-    for sub in ("phase1", "phase2"):
+    for sub in sorted(config_root.iterdir()):
-        yield from sorted((config_root / sub).glob("*.json"))
+        if sub.is_dir() and sub.name not in ("smoke",):
+            yield from sorted(sub.glob("*.json"))
 
 
 def main():

classifier/tools/fetch_ds.py

@@ -2,8 +2,8 @@
 Download the DeepFakeFace dataset from HuggingFace and extract it.
 
 Usage:
-    python tools/download_data.py
+    python tools/fetch_ds.py
-    python tools/download_data.py --data-dir /mnt/data/DFF
+    python tools/fetch_ds.py --data-dir /mnt/data/DFF
 """
 import argparse
 import zipfile

classifier/tools/inference.py

@@ -19,9 +19,27 @@ from PIL import Image
 
 from src.models import get_model, load_checkpoint
 from src.preprocessing import get_transforms
+from src.utils import load_config
 
 
-# Defaults checkpoint to outputs/models/{run_name}_best.pt when not supplied
+def _default_checkpoint(cfg: dict, checkpoint_path: Path | None) -> Path:
+    """Resolve checkpoint: explicit path, single-fold `*_best.pt`, or CV `*_fold{k}_best.pt` / `*_final.pt`."""
+    run_name = cfg["run_name"]
+    models_dir = ROOT / "outputs" / "models"
+    if checkpoint_path is not None:
+        return Path(checkpoint_path)
+    candidates: list[Path] = [models_dir / f"{run_name}_best.pt"]
+    for k in range(32):
+        candidates.append(models_dir / f"{run_name}_fold{k}_best.pt")
+    for k in range(32):
+        candidates.append(models_dir / f"{run_name}_fold{k}_final.pt")
+    for p in candidates:
+        if p.is_file():
+            return p
+    return models_dir / f"{run_name}_best.pt"
+
+
+# Defaults checkpoint under outputs/models/ (single-run or CV best/final).
 def predict(image_path, config_path, checkpoint_path=None):
     image_path = Path(image_path)
     config_path = Path(config_path)
@@ -35,10 +53,9 @@ def predict(image_path, config_path, checkpoint_path=None):
         sys.exit(1)
 
     try:
-        with open(config_path) as f:
+        cfg = load_config(str(config_path))
-            cfg = json.load(f)
+    except (json.JSONDecodeError, OSError, ValueError) as e:
-    except json.JSONDecodeError as e:
+        print(f"Error: Failed to load config: {e}")
-        print(f"Error: Invalid JSON in config: {e}")
         sys.exit(1)
     
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -50,10 +67,7 @@ def predict(image_path, config_path, checkpoint_path=None):
         print(f"Error: Failed to build model: {e}")
         sys.exit(1)
 
-    if checkpoint_path is None:
+    checkpoint_path = _default_checkpoint(cfg, Path(checkpoint_path) if checkpoint_path else None)
-        checkpoint_path = ROOT / "outputs" / "models" / f"{cfg['run_name']}_best.pt"
-    else:
-        checkpoint_path = Path(checkpoint_path)
     
     if not checkpoint_path.exists():
         print(f"Error: Checkpoint not found: {checkpoint_path}")

docs/classifier_impl.md

@@ -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

docs/classifier_plan.md

@@ -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?

docs/generator_plan.md

@@ -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.

generator/configs/smoke/smoke.json

@@ -0,0 +1,19 @@
+{
+  "extends": "../phase0/_base_phase0.json",
+  "run_name": "smoke",
+  "model": "vae",
+  "latent_dim": 32,
+  "ngf": 16,
+  "epochs": 1,
+  "batch_size": 8,
+  "lr": 0.001,
+  "beta_kl": 0.1,
+  "lambda_perceptual": 0,
+  "lambda_adversarial": 0,
+  "subsample": 1.0,
+  "sample_interval": 1,
+  "fid_interval": 999,
+  "fid_n_real": 64,
+  "num_workers": 0,
+  "ema_decay": 0.9
+}

generator/outputs/samples/final_showcase/candidate_scores.csv

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generator/outputs/samples/final_showcase/selected_top3.csv

@@ -0,0 +1,10 @@
+run,architecture,grid,grid_index,tile_index,row,col,source_path,score,mean,std,saturation,sharpness,exposure_score,contrast_score,detail_score,color_score,rank,tile_path
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+p5_ddpm,DDPM - cosine v-pred wider,grid_0008.png,8,9,2,0,outputs\samples\final_comparison\p5_ddpm\grid_0008.png,0.9835371665656567,0.483590304851532,0.2652520537376404,0.346821129322052,0.009857337921857834,0.9887802973389626,1.0,1.0,0.9126871824264526,3,outputs\samples\final_showcase\top_tiles\p5_ddpm\rank_03_grid_0008_tile_09.png
+p5_gan,GAN - WGAN-GP + SN + Attn,grid_0012.png,12,6,1,1,outputs\samples\final_comparison\p5_gan\grid_0012.png,0.9931557374587933,0.4784943461418152,0.26595890522003174,0.5266944169998169,0.008175451308488846,0.9952948316931725,1.0,0.9782691518033664,1.0,1,outputs\samples\final_showcase\top_tiles\p5_gan\rank_01_grid_0012_tile_06.png
+p5_gan,GAN - WGAN-GP + SN + Attn,grid_0013.png,13,6,1,1,outputs\samples\final_comparison\p5_gan\grid_0013.png,0.9891186870634555,0.4683932662010193,0.25315365195274353,0.42203789949417114,0.012284314259886742,0.9637289568781853,1.0,1.0,1.0,2,outputs\samples\final_showcase\top_tiles\p5_gan\rank_02_grid_0013_tile_06.png
+p5_gan,GAN - WGAN-GP + SN + Attn,grid_0020.png,20,6,1,1,outputs\samples\final_comparison\p5_gan\grid_0020.png,0.9824905775487424,0.49867671728134155,0.2486007660627365,0.3911525011062622,0.02533857524394989,0.9416352584958076,1.0,1.0,1.0,3,outputs\samples\final_showcase\top_tiles\p5_gan\rank_03_grid_0020_tile_06.png
+p5_vae,VAE - perceptual + PatchGAN,grid_0010.png,10,6,1,1,outputs\samples\final_comparison\p5_vae\grid_0010.png,0.9116364613990301,0.4356265366077423,0.24881285429000854,0.2984734773635864,0.007039450109004974,0.8613329268991947,1.0,0.9416724216903715,0.785456519377859,1,outputs\samples\final_showcase\top_tiles\p5_vae\rank_01_grid_0010_tile_06.png
+p5_vae,VAE - perceptual + PatchGAN,grid_0020.png,20,16,3,3,outputs\samples\final_comparison\p5_vae\grid_0020.png,0.9072376066109756,0.4530244469642639,0.20702789723873138,0.5726377367973328,0.0058115217834711075,0.9157013967633247,0.8626162384947141,0.8949692641342557,1.0,2,outputs\samples\final_showcase\top_tiles\p5_vae\rank_02_grid_0020_tile_16.png
+p5_vae,VAE - perceptual + PatchGAN,grid_0013.png,13,1,0,0,outputs\samples\final_comparison\p5_vae\grid_0013.png,0.901039089333058,0.4923376142978668,0.19744431972503662,0.40606826543807983,0.005098136607557535,0.9614449553191662,0.822684665520986,0.8632008123240492,1.0,3,outputs\samples\final_showcase\top_tiles\p5_vae\rank_03_grid_0013_tile_01.png

generator/outputs/samples/final_showcase/selected_top3.json

@@ -0,0 +1,191 @@
+[
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+]