# DetectiveSAMv2

DetectiveSAMv2 is an inference-only image forgery localization bundle built around SAM2. This repo is v2-only: the code path, config sidecar, tests, and demo commands all target the `detective_sam_v2` checkpoint.

The full runnable bundle with model weights is hosted on Hugging Face:

- https://huggingface.co/Gertlek/DetectiveSAMv2

## What is bundled

- Inference checkpoint configs under `checkpoints/`
- SAM2 config under `sam2configs/`
- Poster demo pairs under `demo/cocoglide/`, `demo/flux_test/`, and `demo/qwen_test/`
- A drop-in single-image slot at `demo/user_image/demo_input.png`

Built-in checkpoint alias:

- `detective_sam_v2`

The `detective_sam_v2` alias expects `checkpoints/detective_sam_v2.pth` and the bundled YAML sidecar.

## DetectiveSAMv2 Release

This release packages the v2 architecture and benchmarked weights without the old DetectiveSAMv1 compatibility branches.

Main changes:

- Evidence features built from unadapted target features, raw perturbation-stream features, and adapted target deltas.
- Spatial cross-attention feature adapters for flexible information sharing across target and perturbation streams.
- Transformer evidence mask adapter for direct prompt-mask prediction.
- V2-only adapter implementation in `detectivesam_inference/models/adapters.py`.
- Three perturbation streams by default: Gaussian blur, JPEG compression, and Gaussian noise.
- Training scaled beyond the original SIDA/MagicBrush mix with PicoBanana 10k clean, UltraEdit 10k, and AutoEditForge-generated QWEN/FLUX train data designed to promote generalization to new editors.
- Validation expanded across trained-on and held-out modern edit benchmarks, including QWEN-Bench, FLUX-Bench, CoCoGLIDE, AutoSplice, and NanoBanana.
- JSON and YAML checkpoint sidecar support, including training checkpoint containers with `model` or `ema` keys.

### Benchmark Scores

Scores below are IoU / F1 in percent for the 10-epoch v2 checkpoint `dsamv2_best6info_10ep_20260623_060047/best_model.pth`. Previous Detective SAM values are measured on the same validation setup. The MagicBrush row uses the MagicBrush test split, not the higher `full_magicbrush_val` artifact.

| Dataset | DetectiveSAMv2 | Previous Detective SAM | Delta |
| --- | ---: | ---: | ---: |
| MagicBrush test | 48.83 / 61.01 | 43.98 / 56.46 | +4.85 / +4.55 |
| SIDA test | 54.95 / 65.24 | 50.22 / 60.49 | +4.73 / +4.76 |
| FLUX-Bench | 44.26 / 56.17 | 18.61 / 25.77 | +25.65 / +30.39 |
| QWEN-Bench | 45.40 / 56.95 | 19.62 / 27.15 | +25.78 / +29.80 |
| CoCoGLIDE | 46.63 / 58.33 | 42.79 / 53.43 | +3.84 / +4.90 |
| AutoSplice | 57.49 / 70.53 | 47.85 / 60.32 | +9.64 / +10.21 |
| NanoBanana | 33.04 / 45.97 | 25.89 / 35.74 | +7.15 / +10.23 |
| Overall | 49.55 / 61.39 | 38.77 / 49.14 | +10.78 / +12.26 |

QWEN-Bench and FLUX-Bench are included because the training mix includes corresponding AutoEditForge-generated QWEN and FLUX datasets.

## Setup

```bash
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```

Then download the large weights from Hugging Face:

```bash
pip install -U huggingface_hub
hf download Gertlek/DetectiveSAMv2 \
  checkpoints/detective_sam_v2.pth \
  sam2configs/sam2.1_hiera_base_plus.pt \
  --local-dir .
```

The expected checkpoint paths are also documented in `checkpoints/README.md` and `sam2configs/README.md`.

## Hugging Face Usage

For the simplest setup, clone the Hugging Face repo directly:

```bash
git lfs install
git clone https://huggingface.co/Gertlek/DetectiveSAMv2
cd DetectiveSAMv2
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python -m detectivesam_inference.predict \
  --output-dir outputs/poster_baseline
```

## Poster Demo Flows

### 1. Live single-image demo

Place your image at `demo/user_image/demo_input.png`, then run:

```bash
python -m detectivesam_inference.predict \
  --output-dir outputs/poster_user_image
```

In this mode the CLI reuses the target image as its own source reference so the demo stays runnable with a single image.

### 2. Bundled CocoGLIDE example

If `demo/user_image/demo_input.png` is absent, the default `predict` command falls back to the bundled CocoGlide sample `banana_28809`.

```bash
python -m detectivesam_inference.predict \
  --output-dir outputs/poster_baseline
```

### 3. Bundled modern-edit examples

Flux example:

```bash
python -m detectivesam_inference.predict \
  --checkpoint detective_sam_v2 \
  --source demo/flux_test/source/548.png \
  --target demo/flux_test/target/548.png \
  --mask demo/flux_test/mask/548.png \
  --output-dir outputs/poster_flux
```

Qwen example:

```bash
python -m detectivesam_inference.predict \
  --checkpoint detective_sam_v2 \
  --source demo/qwen_test/source/166.png \
  --target demo/qwen_test/target/166.png \
  --mask demo/qwen_test/mask/166.png \
  --output-dir outputs/poster_qwen
```

### 4. Bundled CocoGlide subset sweep

Use this to evaluate the bundled banana and train CocoGlide demo pairs.

```bash
python -m detectivesam_inference.evaluate \
  --checkpoint detective_sam_v2 \
  --dataset-root demo/cocoglide \
  --output-dir outputs/poster_eval_cocoglide \
  --num-visualizations 2
```

## Outputs

Each `predict` run writes a compact set of visual artifacts plus a JSON summary:

- `<name>_comparison.png`
- `<name>_probability.png`
- `<name>_pred_mask.png`
- `<name>_pred_overlay.png`
- `<name>_summary.json`

If a ground-truth mask is provided, the run also saves:

- `<name>_gt_mask.png`
- `<name>_gt_overlay.png`

The `evaluate` command writes `summary.json` plus a few visualization examples under `visualizations/`.

## Notes

- The runtime selects `cuda` automatically when available and otherwise runs on CPU.
- Checkpoint settings come from YAML or JSON sidecars in `checkpoints/`; you only need the alias or checkpoint path.
- The Hugging Face repo bundles the v2 `.pth` checkpoint and SAM2 `.pt` weight file.
- This repo does not include training code or training-only dependencies.