Add phase 3: README.md

README.md

@@ -9,12 +9,13 @@ tags:
   - pytorch
 ---
 
-# TruFor Phase-2 Localization Checkpoints
+# TruFor Phase-2 and Phase-3 Checkpoints
 
-This repository stores phase-2 localization checkpoints trained with the
-official [TruFor](https://github.com/grip-unina/TruFor) PyTorch implementation.
-TruFor detects and localizes manipulated regions in images using RGB and
-Noiseprint++ features.
+This repository stores phase-2 localization and phase-3 detection/confidence
+checkpoints trained with the official
+[TruFor](https://github.com/grip-unina/TruFor) PyTorch implementation. TruFor
+detects and localizes manipulated regions in images using RGB and Noiseprint++
+features.
 
 ## Training summary
 
@@ -34,33 +35,52 @@ Final recorded validation results:
 - Best `avg_p-F1_smooth`: 0.5505
 - Class IoU: `[0.90797067, 0.17650062]`
 
+### Phase 3: detection network and confidence estimator
+
+- Initialization: phase-2 `weights/best.pth.tar`
+- Frozen modules: Noiseprint++, backbone, and localization head
+- Trained modules: confidence head and detection head
+- Training datasets: IMD2020, CASIA 2.0 revised, and CocoGlide
+- Training crop: 512 x 512
+- Epochs: 100
+- Batch size: 4 per GPU
+- Validation maximum crop: 1024 x 1024
+- Final validation loss: 0.538
+- Best `avg_det_bacc`: 0.5981
+- Final class IoU: `[0.90785598, 0.08274118]`
+
 ## Files
 
 - `weights/best.pth.tar`: best checkpoint selected by `avg_p-F1_smooth`
 - `weights/checkpoint.pth.tar`: final epoch-30 resume checkpoint
+- `weights/phase3/best.pth.tar`: best phase-3 detection/confidence checkpoint
+- `weights/phase3/checkpoint.pth.tar`: final epoch-100 phase-3 resume checkpoint
 - `config/trufor_ph2.yaml`: training configuration
+- `config/trufor_ph3_gpu2.yaml`: phase-3 training configuration
 - `logs/trufor_ph2_gpu1.log`: complete training log
+- `logs/trufor_ph3_gpu2.log`: concise phase-3 training log
 - `code/`: the locally patched training/device-placement files and launchers
 - `SHA256SUMS`: checkpoint integrity hashes
 
 ## Loading
 
 These are native TruFor/PyTorch checkpoints, not Transformers checkpoints.
-Use them with the TruFor training/inference code. For phase-2 inference, place
-the selected checkpoint at `weights/trufor_ph2/best.pth.tar`, or pass the path
-through the project's `TEST.MODEL_FILE` configuration option.
+Use them with the TruFor training/inference code. Use
+`weights/phase3/best.pth.tar` for complete localization, confidence, and image
+detection inference by passing its path through the project's
+`TEST.MODEL_FILE` configuration option. Use the top-level
+`weights/best.pth.tar` as phase-2 initialization when retraining phase 3.
 
 ## How to use
 
 To use or modify this work locally, install Git LFS and run
 `git clone https://huggingface.co/benjaik/trufor-ph2`. Use
-`weights/best.pth.tar` for inference or further fine-tuning with the upstream
-TruFor code (copy it to `TruFor_train_test/weights/trufor_ph2/best.pth.tar`, or
-set `TEST.MODEL_FILE` to its downloaded path); use
-`weights/checkpoint.pth.tar` when resuming the completed training run. The
-included configuration, patched files, and training log can be copied and
-adapted for a new dataset or experiment, subject to the included TruFor and CMX
-license terms.
+`weights/phase3/best.pth.tar` for complete inference with the upstream TruFor
+code by setting `TEST.MODEL_FILE` to its downloaded path. Use
+`weights/best.pth.tar` to initialize another phase-3 run, and use the matching
+`checkpoint.pth.tar` file when resuming training. The included configurations,
+patched files, and training logs can be copied and adapted for a new dataset or
+experiment, subject to the included TruFor and CMX license terms.
 
 ## Local compatibility changes