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

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

Then download the large weights from Hugging Face:

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:

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:

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.

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

3. Bundled modern-edit examples

Flux example:

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:

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.

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.