Final polish
This commit is contained in:
Johnny Fernandes
@@ -1,4 +1,4 @@
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# Deep Learning Face Project
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# Deep learning face project
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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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@@ -10,7 +10,7 @@ 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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## 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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@@ -19,7 +19,7 @@ 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, 224x224 inputs, ImageNet/default
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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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@@ -28,14 +28,14 @@ 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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## 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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### Classifier notebooks
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Read these first:
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@@ -57,7 +57,7 @@ Read these first:
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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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### Generator Notebooks
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### Generator notebooks
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Read these after the classifier:
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@@ -67,12 +67,12 @@ Read these after the classifier:
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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 -> 128x128 check.
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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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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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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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@@ -81,7 +81,7 @@ Read these after the classifier:
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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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## 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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@@ -97,7 +97,7 @@ When a notebook shows a plot or image grid, the surrounding markdown explains:
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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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## Repository layout
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```text
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DRL_PROJ/
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@@ -106,6 +106,7 @@ DRL_PROJ/
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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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@@ -113,6 +114,7 @@ DRL_PROJ/
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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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@@ -121,11 +123,11 @@ DRL_PROJ/
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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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## Rebuilding the generator notebooks
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The generator notebooks are generated from a single source file:
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```powershell
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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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@@ -133,24 +135,120 @@ python _build.py
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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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## Running The Code
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## Setup
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Create an environment and install the project requirements:
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Create a conda environment and install the project requirements:
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```powershell
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python -m venv .venv
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.\.venv\Scripts\python.exe -m pip install --upgrade pip setuptools wheel
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.\.venv\Scripts\python.exe -m pip install -r requirements.txt
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```bash
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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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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.
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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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Execution entry points exist in `classifier/run.py` and `generator/run.py` for
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reproducibility, but the report notebooks should be read as analysis over
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already saved results.
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```bash
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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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## Final Takeaway
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Expected layout under the data root: `wiki/<identity>/*.jpg`,
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`inpainting/...`, `text2img/...`, `insight/...`.
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## Classifier — training
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From the repository root:
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```bash
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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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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`.
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**Primary delivery model** (best Phase 4 detector): config
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`classifier/configs/phase4/p4_convnext_tiny_100pct.json` with per-fold
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weights `classifier/outputs/models/p4_convnext_tiny_100pct_fold*_best.pt`.
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## Classifier — inference
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Classify a single image as real or fake:
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```bash
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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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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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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
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`{run_name}_fold{k}_final.pt`. To use a specific fold:
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```bash
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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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## Generator — training
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From the repository root:
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```bash
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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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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
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`{run_name}_best_ema.pt` (lowest-FID snapshot).
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## Generator — inference (sampling)
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Generate 4×4 sample grids from Phase 5 EMA checkpoints:
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```bash
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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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Options:
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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).
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- `--output-dir` — where to write the PNGs (default
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`generator/outputs/samples/final_comparison/`).
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- `--truncation` — optional latent truncation for the GAN (lower = less
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diversity but sharper).
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- `--device` — `cuda` or `cpu` (default: auto-detect).
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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.
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## Final takeaway
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The project is best understood as a sequence of controlled decisions:
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@@ -0,0 +1,18 @@
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{
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"extends": "../shared.json",
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"run_name": "smoke",
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"backbone": "simple_cnn",
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"cnn_preset": "micro",
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"dropout": 0.0,
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"epochs": 1,
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"cv_folds": 2,
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"image_size": 64,
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"batch_size": 8,
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"num_workers": 0,
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"early_stopping_patience": 0,
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"subsample": 1.0,
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"augment": false,
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"lr": 0.001,
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"T_max": 1,
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"data_dir": "data"
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}
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+912
-924
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -0,0 +1,90 @@
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"""
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End-to-end smoke test: synthetic DFF layout -> short CV train -> inference.
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Run from the classifier package root:
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cd classifier && conda activate drl && python -m unittest tests.smoke_test -v
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"""
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import io
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import subprocess
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import sys
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import unittest
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from pathlib import Path
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from PIL import Image
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_CLASSIFIER_ROOT = Path(__file__).resolve().parents[1]
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_SMOKE_CFG = _CLASSIFIER_ROOT / "configs" / "smoke" / "smoke.json"
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def _write_synthetic_dff(data_root: Path, *, n_identities: int = 16) -> None:
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"""Minimal DeepFakeFace-style tree: wiki + three fake sources, shared basenames per identity."""
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sources = ("wiki", "inpainting", "text2img", "insight")
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for i in range(n_identities):
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stem = f"id{i:03d}"
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for src in sources:
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d = data_root / src / f"person_{i:03d}"
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d.mkdir(parents=True, exist_ok=True)
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buf = io.BytesIO()
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Image.new(
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"RGB",
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(96, 96),
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color=(min(20 + i * 11, 255), 80, 120 if src == "wiki" else 40),
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).save(buf, format="JPEG")
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# Same filename stem across all sources → one CV group per identity (matches DFF).
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(d / f"{stem}.jpg").write_bytes(buf.getvalue())
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class SmokeTrainInferTests(unittest.TestCase):
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def test_local_smoke_train_then_inference(self):
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import tempfile
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with tempfile.TemporaryDirectory() as td:
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tmp = Path(td)
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data_dir = tmp / "data"
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out_root = tmp / "outputs"
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_write_synthetic_dff(data_dir)
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sys.path.insert(0, str(_CLASSIFIER_ROOT))
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import run as train_run
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train_run.main(
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str(_SMOKE_CFG),
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data_dir_override=str(data_dir),
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output_root=str(out_root),
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use_gpu=False,
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)
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models_dir = out_root / "models"
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ck_fold0 = models_dir / "smoke_fold0_best.pt"
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if not ck_fold0.is_file():
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ck_fold0 = models_dir / "smoke_fold0_final.pt"
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self.assertTrue(
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ck_fold0.is_file(),
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f"Expected fold-0 checkpoint under {models_dir}",
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)
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probe = tmp / "probe.jpg"
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probe.write_bytes((data_dir / "wiki" / "person_000" / "id000.jpg").read_bytes())
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cmd = [
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sys.executable,
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str(_CLASSIFIER_ROOT / "tools" / "inference.py"),
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str(probe),
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str(_SMOKE_CFG),
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"--checkpoint",
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str(ck_fold0),
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]
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proc = subprocess.run(
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cmd,
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cwd=str(_CLASSIFIER_ROOT),
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capture_output=True,
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text=True,
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timeout=300,
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)
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self.assertEqual(proc.returncode, 0, proc.stderr + proc.stdout)
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self.assertIn("P(fake)", proc.stdout)
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if __name__ == "__main__":
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unittest.main()
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@@ -18,8 +18,9 @@ def parse_args():
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def iter_config_paths(config_root: Path):
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for sub in ("phase1", "phase2"):
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yield from sorted((config_root / sub).glob("*.json"))
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for sub in sorted(config_root.iterdir()):
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if sub.is_dir() and sub.name not in ("smoke",):
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yield from sorted(sub.glob("*.json"))
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def main():
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@@ -2,8 +2,8 @@
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Download the DeepFakeFace dataset from HuggingFace and extract it.
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Usage:
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python tools/download_data.py
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python tools/download_data.py --data-dir /mnt/data/DFF
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python tools/fetch_ds.py
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python tools/fetch_ds.py --data-dir /mnt/data/DFF
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"""
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import argparse
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import zipfile
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@@ -19,9 +19,27 @@ from PIL import Image
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from src.models import get_model, load_checkpoint
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from src.preprocessing import get_transforms
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from src.utils import load_config
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# Defaults checkpoint to outputs/models/{run_name}_best.pt when not supplied
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def _default_checkpoint(cfg: dict, checkpoint_path: Path | None) -> Path:
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"""Resolve checkpoint: explicit path, single-fold `*_best.pt`, or CV `*_fold{k}_best.pt` / `*_final.pt`."""
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run_name = cfg["run_name"]
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models_dir = ROOT / "outputs" / "models"
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if checkpoint_path is not None:
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return Path(checkpoint_path)
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candidates: list[Path] = [models_dir / f"{run_name}_best.pt"]
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for k in range(32):
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candidates.append(models_dir / f"{run_name}_fold{k}_best.pt")
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for k in range(32):
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candidates.append(models_dir / f"{run_name}_fold{k}_final.pt")
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for p in candidates:
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if p.is_file():
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return p
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return models_dir / f"{run_name}_best.pt"
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# Defaults checkpoint under outputs/models/ (single-run or CV best/final).
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def predict(image_path, config_path, checkpoint_path=None):
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image_path = Path(image_path)
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config_path = Path(config_path)
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@@ -35,10 +53,9 @@ def predict(image_path, config_path, checkpoint_path=None):
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sys.exit(1)
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try:
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with open(config_path) as f:
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cfg = json.load(f)
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except json.JSONDecodeError as e:
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print(f"Error: Invalid JSON in config: {e}")
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cfg = load_config(str(config_path))
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except (json.JSONDecodeError, OSError, ValueError) as e:
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print(f"Error: Failed to load config: {e}")
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sys.exit(1)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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@@ -50,10 +67,7 @@ def predict(image_path, config_path, checkpoint_path=None):
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print(f"Error: Failed to build model: {e}")
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sys.exit(1)
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if checkpoint_path is None:
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checkpoint_path = ROOT / "outputs" / "models" / f"{cfg['run_name']}_best.pt"
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else:
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checkpoint_path = Path(checkpoint_path)
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checkpoint_path = _default_checkpoint(cfg, Path(checkpoint_path) if checkpoint_path else None)
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if not checkpoint_path.exists():
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print(f"Error: Checkpoint not found: {checkpoint_path}")
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Binary file not shown.
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@@ -0,0 +1,19 @@
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{
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"extends": "../phase0/_base_phase0.json",
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"run_name": "smoke",
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"model": "vae",
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"latent_dim": 32,
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"ngf": 16,
|
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"epochs": 1,
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"batch_size": 8,
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||||
"lr": 0.001,
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"beta_kl": 0.1,
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||||
"lambda_perceptual": 0,
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"lambda_adversarial": 0,
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"subsample": 1.0,
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"sample_interval": 1,
|
||||
"fid_interval": 999,
|
||||
"fid_n_real": 64,
|
||||
"num_workers": 0,
|
||||
"ema_decay": 0.9
|
||||
}
|
||||
@@ -0,0 +1,78 @@
|
||||
"""
|
||||
Smoke test: tiny wiki-only dataset -> 1 VAE epoch -> decode / sample (inference).
|
||||
|
||||
Run from the generator package root:
|
||||
|
||||
cd generator && conda activate drl && python -m unittest tests.smoke_test -v
|
||||
"""
|
||||
import io
|
||||
import sys
|
||||
import unittest
|
||||
from pathlib import Path
|
||||
from unittest.mock import patch
|
||||
|
||||
import torch
|
||||
from PIL import Image
|
||||
|
||||
_GEN_ROOT = Path(__file__).resolve().parents[1]
|
||||
_SMOKE_CFG = _GEN_ROOT / "configs" / "smoke" / "smoke.json"
|
||||
|
||||
|
||||
def _write_wiki_only(data_root: Path, *, n_folders: int = 12) -> None:
|
||||
"""Minimal layout for GeneratorDataset: data/wiki/<id>/*.jpg"""
|
||||
for i in range(n_folders):
|
||||
d = data_root / "wiki" / f"person_{i:03d}"
|
||||
d.mkdir(parents=True, exist_ok=True)
|
||||
buf = io.BytesIO()
|
||||
Image.new("RGB", (64, 64), color=(30 + i * 15, 100, 180)).save(buf, format="JPEG")
|
||||
(d / "face.jpg").write_bytes(buf.getvalue())
|
||||
|
||||
|
||||
class SmokeGeneratorTrainSampleTests(unittest.TestCase):
|
||||
def test_vae_one_epoch_then_sample(self):
|
||||
import tempfile
|
||||
|
||||
if not _SMOKE_CFG.is_file():
|
||||
self.skipTest("smoke config missing")
|
||||
|
||||
with tempfile.TemporaryDirectory() as td:
|
||||
tmp = Path(td)
|
||||
data_dir = tmp / "data"
|
||||
out_root = tmp / "outputs"
|
||||
_write_wiki_only(data_dir)
|
||||
|
||||
sys.path.insert(0, str(_GEN_ROOT))
|
||||
import run as gen_run
|
||||
|
||||
# Avoid torch.compile in short smoke runs (simpler state_dict / fewer edge cases).
|
||||
with patch("torch.compile", lambda m, **kwargs: m):
|
||||
gen_run.main(
|
||||
str(_SMOKE_CFG),
|
||||
data_dir_override=str(data_dir),
|
||||
output_root=str(out_root),
|
||||
)
|
||||
|
||||
models_dir = out_root / "models"
|
||||
ckpt = models_dir / "smoke_final_ema.pt"
|
||||
self.assertTrue(ckpt.is_file(), f"Expected EMA checkpoint at {ckpt}")
|
||||
|
||||
from src.models import get_model
|
||||
from src.utils import load_config
|
||||
|
||||
cfg = load_config(str(_SMOKE_CFG))
|
||||
vae, kind = get_model(cfg)
|
||||
self.assertEqual(kind, "vae")
|
||||
|
||||
device = torch.device("cpu")
|
||||
state = torch.load(ckpt, map_location=device, weights_only=True)
|
||||
vae.load_state_dict(state, strict=True)
|
||||
vae.eval()
|
||||
|
||||
with torch.no_grad():
|
||||
grid = vae.sample(4, device)
|
||||
self.assertEqual(grid.shape, (4, 3, 64, 64))
|
||||
self.assertTrue(torch.isfinite(grid).all())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@@ -25,8 +25,8 @@ PHASE5_RUNS = {
|
||||
|
||||
|
||||
def _load_cfg(name: str) -> dict:
|
||||
with open(CFG_DIR / PHASE5_RUNS[name]["config"]) as f:
|
||||
return json.load(f)
|
||||
from src.utils import load_config
|
||||
return load_config(str(CFG_DIR / PHASE5_RUNS[name]["config"]))
|
||||
|
||||
|
||||
def _load_model(name: str, cfg: dict, device: torch.device):
|
||||
|
||||
BIN
Binary file not shown.
@@ -1,10 +0,0 @@
|
||||
{
|
||||
"run_name": "p1_resnet18_baseline",
|
||||
"backbone": "resnet18",
|
||||
"pretrained": true,
|
||||
"epochs": 15,
|
||||
"image_size": 128,
|
||||
"subsample": 0.2,
|
||||
"augment": false,
|
||||
"data_dir": "data"
|
||||
}
|
||||
@@ -1,11 +0,0 @@
|
||||
{
|
||||
"run_name": "p1_simplecnn_baseline",
|
||||
"backbone": "simple_cnn",
|
||||
"cnn_preset": "medium",
|
||||
"dropout": 0.0,
|
||||
"epochs": 15,
|
||||
"image_size": 128,
|
||||
"subsample": 0.2,
|
||||
"augment": false,
|
||||
"data_dir": "data"
|
||||
}
|
||||
@@ -1,11 +0,0 @@
|
||||
{
|
||||
"run_name": "p2a_t1_original",
|
||||
"backbone": "resnet18",
|
||||
"pretrained": true,
|
||||
"epochs": 15,
|
||||
"image_size": 224,
|
||||
"subsample": 0.2,
|
||||
"augment": false,
|
||||
"data_dir": "data",
|
||||
"normalization": "imagenet"
|
||||
}
|
||||
@@ -1,5 +0,0 @@
|
||||
{
|
||||
"extends": "p2a_t1_original.json",
|
||||
"run_name": "p2a_t2_real_norm",
|
||||
"normalization": "real_norm"
|
||||
}
|
||||
@@ -1,15 +0,0 @@
|
||||
{
|
||||
"extends": "p2a_t1_original.json",
|
||||
"run_name": "p2a_t3_holdout_inpainting",
|
||||
"train_sources": [
|
||||
"wiki",
|
||||
"text2img",
|
||||
"insight"
|
||||
],
|
||||
"eval_sources": [
|
||||
"wiki",
|
||||
"text2img",
|
||||
"insight",
|
||||
"inpainting"
|
||||
]
|
||||
}
|
||||
@@ -1,15 +0,0 @@
|
||||
{
|
||||
"extends": "p2a_t1_original.json",
|
||||
"run_name": "p2a_t3_holdout_insight",
|
||||
"train_sources": [
|
||||
"wiki",
|
||||
"text2img",
|
||||
"inpainting"
|
||||
],
|
||||
"eval_sources": [
|
||||
"wiki",
|
||||
"text2img",
|
||||
"inpainting",
|
||||
"insight"
|
||||
]
|
||||
}
|
||||
@@ -1,15 +0,0 @@
|
||||
{
|
||||
"extends": "p2a_t1_original.json",
|
||||
"run_name": "p2a_t3_holdout_text2img",
|
||||
"train_sources": [
|
||||
"wiki",
|
||||
"inpainting",
|
||||
"insight"
|
||||
],
|
||||
"eval_sources": [
|
||||
"wiki",
|
||||
"inpainting",
|
||||
"insight",
|
||||
"text2img"
|
||||
]
|
||||
}
|
||||
@@ -1,10 +0,0 @@
|
||||
{
|
||||
"run_name": "p2b_resnet18_224",
|
||||
"backbone": "resnet18",
|
||||
"pretrained": true,
|
||||
"epochs": 15,
|
||||
"image_size": 224,
|
||||
"subsample": 0.2,
|
||||
"augment": false,
|
||||
"data_dir": "data"
|
||||
}
|
||||
@@ -1,11 +0,0 @@
|
||||
{
|
||||
"run_name": "p2b_simplecnn_224",
|
||||
"backbone": "simple_cnn",
|
||||
"cnn_preset": "medium",
|
||||
"dropout": 0.0,
|
||||
"epochs": 15,
|
||||
"image_size": 224,
|
||||
"subsample": 0.2,
|
||||
"augment": false,
|
||||
"data_dir": "data"
|
||||
}
|
||||
@@ -1,10 +0,0 @@
|
||||
{
|
||||
"run_name": "p2c_resnet18_facecrop",
|
||||
"backbone": "resnet18",
|
||||
"pretrained": true,
|
||||
"epochs": 15,
|
||||
"image_size": 224,
|
||||
"subsample": 0.2,
|
||||
"augment": false,
|
||||
"data_dir": "cropped/classifier"
|
||||
}
|
||||
@@ -1,11 +0,0 @@
|
||||
{
|
||||
"run_name": "p2c_simplecnn_facecrop",
|
||||
"backbone": "simple_cnn",
|
||||
"cnn_preset": "medium",
|
||||
"dropout": 0.0,
|
||||
"epochs": 15,
|
||||
"image_size": 224,
|
||||
"subsample": 0.2,
|
||||
"augment": false,
|
||||
"data_dir": "cropped/classifier"
|
||||
}
|
||||
@@ -1,22 +0,0 @@
|
||||
{
|
||||
"run_name": "p2d_resnet18_aug",
|
||||
"backbone": "resnet18",
|
||||
"pretrained": true,
|
||||
"epochs": 15,
|
||||
"image_size": 224,
|
||||
"subsample": 0.2,
|
||||
"data_dir": "data",
|
||||
"augment": {
|
||||
"hflip_p": 0.5,
|
||||
"rotation_degrees": 10,
|
||||
"brightness": 0.2,
|
||||
"contrast": 0.2,
|
||||
"saturation": 0.1,
|
||||
"hue": 0.02,
|
||||
"grayscale_p": 0.1,
|
||||
"blur_p": 0.1,
|
||||
"erase_p": 0.2,
|
||||
"noise_p": 0.3,
|
||||
"noise_std": 0.04
|
||||
}
|
||||
}
|
||||
@@ -1,23 +0,0 @@
|
||||
{
|
||||
"run_name": "p2d_simplecnn_aug",
|
||||
"backbone": "simple_cnn",
|
||||
"cnn_preset": "medium",
|
||||
"dropout": 0.0,
|
||||
"epochs": 15,
|
||||
"image_size": 224,
|
||||
"subsample": 0.2,
|
||||
"data_dir": "data",
|
||||
"augment": {
|
||||
"hflip_p": 0.5,
|
||||
"rotation_degrees": 10,
|
||||
"brightness": 0.2,
|
||||
"contrast": 0.2,
|
||||
"saturation": 0.1,
|
||||
"hue": 0.02,
|
||||
"grayscale_p": 0.1,
|
||||
"blur_p": 0.1,
|
||||
"erase_p": 0.2,
|
||||
"noise_p": 0.3,
|
||||
"noise_std": 0.04
|
||||
}
|
||||
}
|
||||
@@ -1,22 +0,0 @@
|
||||
{
|
||||
"run_name": "p2e_resnet18_facecrop_aug",
|
||||
"backbone": "resnet18",
|
||||
"pretrained": true,
|
||||
"epochs": 15,
|
||||
"image_size": 224,
|
||||
"subsample": 0.2,
|
||||
"data_dir": "cropped/classifier",
|
||||
"augment": {
|
||||
"hflip_p": 0.5,
|
||||
"rotation_degrees": 10,
|
||||
"brightness": 0.2,
|
||||
"contrast": 0.2,
|
||||
"saturation": 0.1,
|
||||
"hue": 0.02,
|
||||
"grayscale_p": 0.1,
|
||||
"blur_p": 0.1,
|
||||
"erase_p": 0.2,
|
||||
"noise_p": 0.3,
|
||||
"noise_std": 0.04
|
||||
}
|
||||
}
|
||||
@@ -1,23 +0,0 @@
|
||||
{
|
||||
"run_name": "p2e_simplecnn_facecrop_aug",
|
||||
"backbone": "simple_cnn",
|
||||
"cnn_preset": "medium",
|
||||
"dropout": 0.0,
|
||||
"epochs": 15,
|
||||
"image_size": 224,
|
||||
"subsample": 0.2,
|
||||
"data_dir": "cropped/classifier",
|
||||
"augment": {
|
||||
"hflip_p": 0.5,
|
||||
"rotation_degrees": 10,
|
||||
"brightness": 0.2,
|
||||
"contrast": 0.2,
|
||||
"saturation": 0.1,
|
||||
"hue": 0.02,
|
||||
"grayscale_p": 0.1,
|
||||
"blur_p": 0.1,
|
||||
"erase_p": 0.2,
|
||||
"noise_p": 0.3,
|
||||
"noise_std": 0.04
|
||||
}
|
||||
}
|
||||
@@ -1,8 +0,0 @@
|
||||
{
|
||||
"pretrained": true,
|
||||
"epochs": 15,
|
||||
"image_size": 224,
|
||||
"subsample": 0.2,
|
||||
"augment": false,
|
||||
"data_dir": "cropped/classifier"
|
||||
}
|
||||
@@ -1,5 +0,0 @@
|
||||
{
|
||||
"extends": "_base.json",
|
||||
"run_name": "p3_convnext_tiny",
|
||||
"backbone": "convnext_tiny"
|
||||
}
|
||||
@@ -1,5 +0,0 @@
|
||||
{
|
||||
"extends": "_base.json",
|
||||
"run_name": "p3_efficientnet_b0",
|
||||
"backbone": "efficientnet_b0"
|
||||
}
|
||||
@@ -1,5 +0,0 @@
|
||||
{
|
||||
"extends": "_base.json",
|
||||
"run_name": "p3_mobilenetv3_small",
|
||||
"backbone": "mobilenet_v3_small"
|
||||
}
|
||||
@@ -1,5 +0,0 @@
|
||||
{
|
||||
"extends": "_base.json",
|
||||
"run_name": "p3_resnet34",
|
||||
"backbone": "resnet34"
|
||||
}
|
||||
@@ -1,5 +0,0 @@
|
||||
{
|
||||
"extends": "_base.json",
|
||||
"run_name": "p3_resnet50",
|
||||
"backbone": "resnet50"
|
||||
}
|
||||
@@ -1,7 +0,0 @@
|
||||
{
|
||||
"pretrained": true,
|
||||
"epochs": 15,
|
||||
"image_size": 224,
|
||||
"augment": false,
|
||||
"data_dir": "cropped/classifier"
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
{
|
||||
"extends": "_base.json",
|
||||
"run_name": "p4_convnext_tiny_100pct",
|
||||
"backbone": "convnext_tiny",
|
||||
"subsample": 1.0
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
{
|
||||
"extends": "_base.json",
|
||||
"run_name": "p4_convnext_tiny_50pct",
|
||||
"backbone": "convnext_tiny",
|
||||
"subsample": 0.5
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
{
|
||||
"extends": "_base.json",
|
||||
"run_name": "p4_efficientnet_b0_100pct",
|
||||
"backbone": "efficientnet_b0",
|
||||
"subsample": 1.0
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
{
|
||||
"extends": "_base.json",
|
||||
"run_name": "p4_efficientnet_b0_50pct",
|
||||
"backbone": "efficientnet_b0",
|
||||
"subsample": 0.5
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
{
|
||||
"extends": "_base.json",
|
||||
"run_name": "p4_resnet50_100pct",
|
||||
"backbone": "resnet50",
|
||||
"subsample": 1.0
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
{
|
||||
"extends": "_base.json",
|
||||
"run_name": "p4_resnet50_50pct",
|
||||
"backbone": "resnet50",
|
||||
"subsample": 0.5
|
||||
}
|
||||
@@ -1,13 +0,0 @@
|
||||
{
|
||||
"seed": 42,
|
||||
"cv_folds": 5,
|
||||
"batch_size": 32,
|
||||
"num_workers": 4,
|
||||
"early_stopping_patience": 5,
|
||||
|
||||
"lr": 1e-4,
|
||||
"weight_decay": 1e-4,
|
||||
"T_max": 15,
|
||||
|
||||
"data_dir": "data"
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -1,16 +0,0 @@
|
||||
run,n_candidates,n_images,heldout_source,candidate_auc,candidate_acc,panel,expanded_panel,fine_panel
|
||||
p1_simplecnn_baseline,192,10,,0.8378182870370371,0.7395833333333334,classifier\outputs\gradcam\p1_simplecnn_baseline\panel.png,,
|
||||
p1_resnet18_baseline,192,10,,0.9769965277777778,0.9270833333333334,classifier\outputs\gradcam\p1_resnet18_baseline\panel.png,,
|
||||
p2a_t1_original,192,10,,0.9984085648148149,0.9895833333333334,classifier\outputs\gradcam\p2a_t1_original\panel.png,,
|
||||
p2a_t2_real_norm,192,10,,0.9939236111111112,0.9791666666666666,classifier\outputs\gradcam\p2a_t2_real_norm\panel.png,,
|
||||
p2a_t3_holdout_text2img,240,10,text2img,0.9264322916666667,0.775,classifier\outputs\gradcam\p2a_t3_holdout_text2img\panel.png,,
|
||||
p2a_t3_holdout_inpainting,240,10,inpainting,0.9819878472222222,0.9333333333333333,classifier\outputs\gradcam\p2a_t3_holdout_inpainting\panel.png,,
|
||||
p2a_t3_holdout_insight,240,10,insight,0.9549696180555556,0.7625,classifier\outputs\gradcam\p2a_t3_holdout_insight\panel.png,,
|
||||
p2b_simplecnn_224,192,10,,0.8207465277777778,0.7447916666666666,classifier\outputs\gradcam\p2b_simplecnn_224\panel.png,,
|
||||
p2b_resnet18_224,192,10,,0.9984085648148149,0.9895833333333334,classifier\outputs\gradcam\p2b_resnet18_224\panel.png,,
|
||||
p2c_simplecnn_facecrop,192,10,,0.8058449074074073,0.7552083333333334,classifier\outputs\gradcam\p2c_simplecnn_facecrop\panel.png,,
|
||||
p2c_resnet18_facecrop,192,16,,0.9911747685185185,0.890625,classifier\outputs\gradcam\p2c_resnet18_facecrop\panel.png,classifier\outputs\gradcam\p2c_resnet18_facecrop\panel_expanded.png,classifier\outputs\gradcam\p2c_resnet18_facecrop\panel_fine_layer3.png
|
||||
p2d_simplecnn_aug,192,10,,0.7565104166666666,0.65625,classifier\outputs\gradcam\p2d_simplecnn_aug\panel.png,,
|
||||
p2d_resnet18_aug,192,10,,0.9733796296296297,0.9270833333333334,classifier\outputs\gradcam\p2d_resnet18_aug\panel.png,,
|
||||
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"c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_simplecnn_facecrop_aug\\09_strong_false_negative_inpainting_28699293_1957-04-26_2009.png",
|
||||
"c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_simplecnn_facecrop_aug\\10_borderline_inpainting_1932725_1949-01-25_1979.png"
|
||||
],
|
||||
"panel_path": "c:\\Users\\diogo\\Documents\\MIA_UP\\2_Semestre\\DRL\\DRL_2\\DRL_PROJ\\classifier\\outputs\\gradcam\\p2e_simplecnn_facecrop_aug\\panel.png",
|
||||
"expanded_panel_path": null,
|
||||
"fine_panel_path": null
|
||||
}
|
||||
@@ -1,37 +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 AUC 0.9366 to 0.9660.",
|
||||
"confidence": "high"
|
||||
},
|
||||
{
|
||||
"choice": "face crop",
|
||||
"decision": "enable",
|
||||
"evidence": "Best Phase 2 run is p2c_resnet18_facecrop with AUC 0.9755.",
|
||||
"confidence": "medium-high"
|
||||
},
|
||||
{
|
||||
"choice": "augmentation",
|
||||
"decision": "disable for current 20% setting",
|
||||
"evidence": "p2e_resnet18_facecrop_aug reaches AUC 0.9737, below facecrop-only 0.9755; SimpleCNN drops sharply.",
|
||||
"confidence": "low"
|
||||
},
|
||||
{
|
||||
"choice": "normalization",
|
||||
"decision": "ImageNet/default",
|
||||
"evidence": "real_norm is only +0.0018 AUC and is less aligned with pretrained ImageNet weights.",
|
||||
"confidence": "medium"
|
||||
},
|
||||
{
|
||||
"choice": "source generalization",
|
||||
"decision": "report as limitation and diagnostic target",
|
||||
"evidence": "Holding out text2img and insight drops pairwise AUC to 0.7595 and 0.8421.",
|
||||
"confidence": "high"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,104 +0,0 @@
|
||||
{
|
||||
"phase": "phase4",
|
||||
"best_auc_run": "p4_convnext_tiny_100pct",
|
||||
"best_auc": 0.9954,
|
||||
"selected_detector_run": "p4_convnext_tiny_100pct",
|
||||
"selected_detector_reason": "Lowest false positive rate on real images and best balanced accuracy at 100% data.",
|
||||
"notes": [
|
||||
"20% rows come from the matching Phase 3 runs and act as scaling anchors.",
|
||||
"50% and 100% rows come from Phase 4 logs.",
|
||||
"ConvNeXt-Tiny is the AUC winner and also the practical detector when false positives on real images matter most."
|
||||
],
|
||||
"summary_rows": [
|
||||
{
|
||||
"model": "ResNet50",
|
||||
"scale_pct": 20,
|
||||
"run": "p3_resnet50",
|
||||
"auc": 0.9857046296296297,
|
||||
"accuracy": 0.9532083333333334,
|
||||
"f1": 0.9687705256330055,
|
||||
"balanced_accuracy": 0.9385277777777778,
|
||||
"macro_f1": 0.9377251247311893
|
||||
},
|
||||
{
|
||||
"model": "ResNet50",
|
||||
"scale_pct": 50,
|
||||
"run": "p4_resnet50_50pct",
|
||||
"auc": 0.9921022333333335,
|
||||
"accuracy": 0.9607166666666667,
|
||||
"f1": 0.973649056957159,
|
||||
"balanced_accuracy": 0.9536555555555555,
|
||||
"macro_f1": 0.9482411270477236
|
||||
},
|
||||
{
|
||||
"model": "ResNet50",
|
||||
"scale_pct": 100,
|
||||
"run": "p4_resnet50_100pct",
|
||||
"auc": 0.9949671268518518,
|
||||
"accuracy": 0.9692166666666667,
|
||||
"f1": 0.9793757797895071,
|
||||
"balanced_accuracy": 0.9638111111111111,
|
||||
"macro_f1": 0.9593474615494658
|
||||
},
|
||||
{
|
||||
"model": "EfficientNet-B0",
|
||||
"scale_pct": 20,
|
||||
"run": "p3_efficientnet_b0",
|
||||
"auc": 0.9844867592592592,
|
||||
"accuracy": 0.9450000000000001,
|
||||
"f1": 0.9628492625462333,
|
||||
"balanced_accuracy": 0.9397222222222222,
|
||||
"macro_f1": 0.9284971237471479
|
||||
},
|
||||
{
|
||||
"model": "EfficientNet-B0",
|
||||
"scale_pct": 50,
|
||||
"run": "p4_efficientnet_b0_50pct",
|
||||
"auc": 0.9911186185185186,
|
||||
"accuracy": 0.9576499999999999,
|
||||
"f1": 0.9714437706354593,
|
||||
"balanced_accuracy": 0.9541444444444445,
|
||||
"macro_f1": 0.9446884831225546
|
||||
},
|
||||
{
|
||||
"model": "EfficientNet-B0",
|
||||
"scale_pct": 100,
|
||||
"run": "p4_efficientnet_b0_100pct",
|
||||
"auc": 0.9949471324074075,
|
||||
"accuracy": 0.96835,
|
||||
"f1": 0.9787227370502695,
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||||
"balanced_accuracy": 0.9656777777777779,
|
||||
"macro_f1": 0.9584469550394183
|
||||
},
|
||||
{
|
||||
"model": "ConvNeXt-Tiny",
|
||||
"scale_pct": 20,
|
||||
"run": "p3_convnext_tiny",
|
||||
"auc": 0.9867751388888889,
|
||||
"accuracy": 0.9473333333333332,
|
||||
"f1": 0.9650225390458838,
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||||
"balanced_accuracy": 0.9261666666666667,
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||||
"macro_f1": 0.9292641404681354
|
||||
},
|
||||
{
|
||||
"model": "ConvNeXt-Tiny",
|
||||
"scale_pct": 50,
|
||||
"run": "p4_convnext_tiny_50pct",
|
||||
"auc": 0.9926127555555556,
|
||||
"accuracy": 0.96065,
|
||||
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|
||||
"balanced_accuracy": 0.9562111111111111,
|
||||
"macro_f1": 0.9484169329127863
|
||||
},
|
||||
{
|
||||
"model": "ConvNeXt-Tiny",
|
||||
"scale_pct": 100,
|
||||
"run": "p4_convnext_tiny_100pct",
|
||||
"auc": 0.9953774120370371,
|
||||
"accuracy": 0.9679166666666668,
|
||||
"f1": 0.9783980914742021,
|
||||
"balanced_accuracy": 0.9662444444444445,
|
||||
"macro_f1": 0.9579703600254157
|
||||
}
|
||||
]
|
||||
}
|
||||
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Reference in New Issue
Block a user