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 jalf
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 Johnny Fernandes
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Johnny Fernandes
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DiogoCosta18
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DiogoCosta18
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DiogoCosta18
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DiogoCosta18
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DiogoCosta18
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DiogoCosta18
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Johnny Fernandes
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Johnny Fernandes
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@@ -67,3 +67,6 @@ generator/outputs/samples/*
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.venv/
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.ipynb_checkpoints/
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__pycache__/
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#Presentation
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presentation_inputs.zip
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@@ -1,125 +1,264 @@
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# DRL_PROJ — DeepFake Detection
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# Deep learning face project
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Deep learning project for binary deepfake detection on the DeepFakeFace dataset.
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This repository contains a two-part deep learning project on the
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DeepFakeFace (DFF) dataset:
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## Project structure
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1. **Classifier:** detect whether a face image is real or fake.
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2. **Generator:** train generative models that produce new fake face images.
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```
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The project is written as an experimental report. The notebooks are the main
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deliverable: they show the pipeline, the intermediate failures, the ablations,
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the decisions, and the final models. Read them in order.
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## Project story
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The work follows the same principle in both parts: start with a simple
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baseline, inspect what fails, change one important factor at a time, and keep
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the evidence tied to saved logs and saved artifacts.
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For the **classifier**, the story moves from dataset understanding to
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preprocessing, baseline models, controlled ablations, Grad-CAM inspection,
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stronger model families, and data scaling. The final practical classifier is a
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ResNet50-style pipeline using face crops, 224×224 inputs, ImageNet/default
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normalization, and no stochastic augmentation at validation/test time.
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For the **generator**, the story starts with raw baseline failures, then locks
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the data pipeline before comparing three parallel model-family branches:
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GAN, VAE, and DDPM. The final comparison keeps quality versus speed central:
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DDPM gives the best saved FID and visual quality, GAN is the best
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quality-speed compromise, and VAE is the fastest but smoothest option.
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## How to read the project
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Start with the classifier notebooks, then read the generator notebooks. The
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generator has one linear setup stage followed by three parallel branches:
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GAN, VAE, and DDPM. Those branches are numbered in reading order, but they are
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conceptually parallel experiments after the pipeline is selected.
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### Classifier notebooks
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Read these first:
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1. `classifier/notebooks/01_eda.ipynb`
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Dataset composition, real/fake source mapping, image statistics, and
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shortcut risks.
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2. `classifier/notebooks/02_preprocessing.ipynb`
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Deterministic preprocessing, train-only augmentation, face crops, and
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normalization.
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3. `classifier/notebooks/03_phase1_analysis.ipynb`
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SimpleCNN and ResNet18 controlled baselines.
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4. `classifier/notebooks/04_phase2_analysis.ipynb`
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Resolution, normalization, source holdouts, facecrop, and augmentation
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ablations.
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5. `classifier/notebooks/05_gradcam_analysis.ipynb`
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Qualitative localization analysis across the classifier pipeline.
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6. `classifier/notebooks/06_phase3_model_family_analysis.ipynb`
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Stronger pretrained model families and the ResNet50 practical choice.
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7. `classifier/notebooks/07_phase4_data_scaling_analysis.ipynb`
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Data scaling for strong backbones and the final classifier decision.
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|
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### Generator notebooks
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Read these after the classifier:
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1. `generator/notebooks/01_baseline_sanity_check.ipynb`
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Raw baseline failures and why the data pipeline must be fixed first.
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2. `generator/notebooks/02_pipeline_selection.ipynb`
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Controlled pipeline ablations: resolution, alignment, augmentation, and
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raw/aligned mixing.
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3. `generator/notebooks/03_gan_stability_progression.ipynb`
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GAN branch: DCGAN → WGAN-GP → spectral normalization + GroupNorm +
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self-attention → 128×128 check.
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4. `generator/notebooks/04_vae_loss_progression.ipynb`
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VAE branch: MSE + KL → perceptual loss → PatchGAN adversarial loss.
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5. `generator/notebooks/05_ddpm_recipe_progression.ipynb`
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DDPM branch: linear schedule → cosine schedule → v-prediction → wider
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backbone.
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6. `generator/notebooks/06_final_family_comparison.ipynb`
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Final comparison of the selected GAN, VAE, and DDPM recipes under saved
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Phase 5 conditions.
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7. `generator/notebooks/07_final_sample_showcase.ipynb`
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Curated final sample examples from saved outputs. This is qualitative
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showcase material, not a replacement for FID.
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## What the notebooks do
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The notebooks are analysis/report chapters. They load existing configs, logs,
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figures, saved sample grids, checkpoints, and prediction summaries. They are
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not intended to launch new training runs.
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When a notebook shows a plot or image grid, the surrounding markdown explains:
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- what the artifact shows;
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- why it is needed;
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- how it supports the phase decision;
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- what limitation remains.
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This is important because the project is evaluated not only by final
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performance, but by the documented evolution of the solution.
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## Repository layout
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```text
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DRL_PROJ/
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classifier/ ← discriminative model (real vs. fake classifier)
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src/ ← model definitions, training, evaluation, preprocessing
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configs/ ← experiment configs organised by phase
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phase1/ ← baseline models (SimpleCNN, ResNet18)
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phase2/ ← architecture sweep (ResNet variants, face-crop)
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phase3/ ← EfficientNet, ViT, frequency-aware training
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phase4/ ← ensemble strategies
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tools/ ← analyse.py, ensemble.py, inference.py, facecrop.py
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notebooks/ ← EDA, preprocessing, evaluation, GradCAM
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outputs/ ← models, logs, figures (gitignored except .pt/.json)
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run.py ← main training entry point
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generator/ ← generative model (GAN / VAE / diffusion) — in progress
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pipeline/ ← Vast.ai ephemeral GPU orchestration
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data/ ← dataset root (gitignored)
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cropped/ ← MTCNN pre-cropped faces (gitignored)
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classifier/ ← bbox crops for the classifier
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generator/ ← landmark-aligned crops for the generator
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classifier/
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configs/ experiment configs by phase
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notebooks/ classifier report notebooks
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outputs/ saved logs, figures, Grad-CAM panels, checkpoints
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src/ classifier data, models, training, evaluation
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tests/ unit and smoke tests
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tools/ facecrop, Grad-CAM, inference, reevaluation helpers
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generator/
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configs/ generator configs by phase/family
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notebooks/ generator report notebooks and notebook builder
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outputs/ saved logs, sample grids, final showcase artifacts
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src/ generator data, models, training, metrics
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tests/ unit and smoke tests
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tools/ sampling and utility scripts
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data/ original DFF dataset root, not committed
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cropped/ preprocessed face crops, not committed
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docs/ project statement and supporting documents
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pipeline/ optional remote/GPU orchestration helpers
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```
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## Rebuilding the generator notebooks
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The generator notebooks are generated from a single source file:
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```bash
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cd generator/notebooks
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python _build.py
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```
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That builder writes the numbered generator notebooks listed above. It uses
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existing saved logs and artifacts; it does not train models.
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## Setup
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Create a local environment when you want to run the code directly on a machine you control:
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Create a conda environment and install the project requirements:
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```bash
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python3 -m venv .venv
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source .venv/bin/activate
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conda create -n drl python=3.12
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conda activate drl
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python -m pip install --upgrade pip setuptools wheel
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python -m pip install -r requirements.txt
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```
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## Local Training
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Use **Python 3.12**; some dependencies (for example `facenet-pytorch`) are
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unreliable on 3.13+.
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The raw dataset should be placed under `data/`. Preprocessed crops are stored
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under `cropped/`. These folders are intentionally not committed. To download
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and extract the dataset:
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```bash
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python3 classifier/run.py classifier/configs/phase2/p2_resnet18_facecrop.json
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python3 classifier/run.py classifier/configs/phase3/p3_efficientnet_b0.json
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python classifier/tools/fetch_ds.py
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python classifier/tools/fetch_ds.py --data-dir /path/to/DFF
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```
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## Ephemeral Vast.ai Pipeline
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Expected layout under the data root: `wiki/<identity>/*.jpg`,
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`inpainting/...`, `text2img/...`, `insight/...`.
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||||
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The deployment/orchestration path now lives under [`pipeline/`](/run/host/mnt/shared/UP/DRL/DRL_PROJ/pipeline/README.md).
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## Classifier — training
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One-time setup:
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From the repository root:
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```bash
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cat > pipeline/.env <<'EOF'
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VAST_API_KEY=<your-api-key>
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VAST_SSH_PRIVATE_KEY=/home/your-user/.ssh/id_ed25519
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EOF
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# CPU (slow but valid)
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python classifier/run.py classifier/configs/phase4/p4_convnext_tiny_100pct.json
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# GPU when CUDA is available
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python classifier/run.py classifier/configs/phase4/p4_convnext_tiny_100pct.json --use-gpu
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```
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End-to-end ephemeral run:
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||||
Training uses 5-fold stratified group cross-validation. Per-fold checkpoints
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||||
are saved as `classifier/outputs/models/{run_name}_fold{k}_best.pt` (and
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`_final.pt`). Override data or output locations with `--data-dir` and
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`--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`.
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||||
|
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## Classifier — inference
|
||||
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||||
Classify a single image as real or fake:
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||||
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```bash
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python3 -m pipeline run classifier/configs/phase2/p2_resnet18_facecrop.json --upload-data
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python classifier/tools/inference.py image.jpg classifier/configs/phase4/p4_convnext_tiny_100pct.json
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```
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Interactive offer selection:
|
||||
This loads the config and the matching checkpoint, runs the image through the
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model, and prints a result like:
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|
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```
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Image : image.jpg
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Model : p4_convnext_tiny_100pct (convnext_tiny)
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Device: cuda
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Result: FAKE (confidence: 74.7%)
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P(fake): 0.7466 P(real): 0.2534
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```
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|
||||
If you omit `--checkpoint`, the tool automatically looks for a saved
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checkpoint under `classifier/outputs/models/` — first the single-run
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`{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
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||||
python3 -m pipeline offers --select-offer
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python classifier/tools/inference.py image.jpg classifier/configs/phase4/p4_convnext_tiny_100pct.json \
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--checkpoint classifier/outputs/models/p4_convnext_tiny_100pct_fold0_best.pt
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```
|
||||
|
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You can override the ranking mode per run:
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## Generator — training
|
||||
|
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From the repository root:
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||||
|
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```bash
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python3 -m pipeline offers --sort price
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||||
python3 -m pipeline offers --sort performance
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||||
python3 -m pipeline offers --sort performance --price 0.14
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python generator/run.py generator/configs/phase0/p0_vae.json
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||||
python generator/run.py generator/configs/phase0/p0_ddpm.json
|
||||
```
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|
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You can also filter by region:
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Generator training expects real-face images (default source is `wiki`); use
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`--data-dir` to point at your dataset tree. Checkpoints are saved under
|
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`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
|
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python3 -m pipeline offers --select-offer --region europe
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||||
python3 -m pipeline offers --select-offer --region Portugal
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||||
python3 -m pipeline offers --select-offer --region US
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||||
python3 -m pipeline offers --select-offer --region europe --price 0.14
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python generator/tools/sampling.py --models p5_gan p5_vae p5_ddpm --samples 10
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```
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To inspect which region strings are currently available from the search results:
|
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Options:
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|
||||
```bash
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python3 -m pipeline offers --list-regions
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```
|
||||
- `--models` — which models to sample from (`p5_gan`, `p5_vae`, `p5_ddpm`;
|
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defaults to all three).
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- `--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:
|
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- ensures your SSH public key is registered with Vast.ai
|
||||
- searches offers using the filters in `pipeline/defaults/vast.json`
|
||||
- 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
|
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Each grid is a 4×4 PNG of 16 images sampled from the model's EMA weights.
|
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GAN samples are drawn from random latent vectors, VAE samples decode from the
|
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learned prior, and DDPM samples use 50-step DDIM.
|
||||
|
||||
Useful commands:
|
||||
## Final takeaway
|
||||
|
||||
```bash
|
||||
python3 -m pipeline up
|
||||
python3 -m pipeline status <instance_id>
|
||||
python3 -m pipeline down <instance_id>
|
||||
```
|
||||
The project is best understood as a sequence of controlled decisions:
|
||||
|
||||
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
|
||||
python3 -m pipeline run classifier/configs/phase3/p3_efficientnet_b0.json --pipeline-config /path/to/vast.override.json
|
||||
```
|
||||
|
||||
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
|
||||
The classifier becomes reliable through source-aware preprocessing, stronger
|
||||
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.
|
||||
|
||||
@@ -1,6 +0,0 @@
|
||||
{
|
||||
"extends": "_base.json",
|
||||
"run_name": "p4_convnext_tiny_20pct",
|
||||
"backbone": "convnext_tiny",
|
||||
"subsample": 0.2
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
{
|
||||
"extends": "_base.json",
|
||||
"run_name": "p4_efficientnet_b0_20pct",
|
||||
"backbone": "efficientnet_b0",
|
||||
"subsample": 0.2
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
{
|
||||
"extends": "_base.json",
|
||||
"run_name": "p4_resnet50_20pct",
|
||||
"backbone": "resnet50",
|
||||
"subsample": 0.2
|
||||
}
|
||||
@@ -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"
|
||||
}
|
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run,n_candidates,n_images,heldout_source,candidate_auc,candidate_acc,panel_path,expanded_panel_path
|
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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,
|
||||
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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,
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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,
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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,
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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,
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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
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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,
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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,
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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,
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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,
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@@ -1,4 +0,0 @@
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panel,path
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architecture capacity,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\evolution\05_evolution_architecture_capacity.png
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resnet pipeline,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\evolution\05_evolution_resnet_pipeline.png
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simplecnn pipeline,c:\Users\diogo\Documents\MIA_UP\2_Semestre\DRL\DRL_2\DRL_PROJ\classifier\outputs\gradcam\evolution\05_evolution_simplecnn_pipeline.png
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"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
|
||||
}
|
||||
@@ -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."
|
||||
}
|
||||
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Reference in New Issue
Block a user