DetectiveSAM

DetectiveSAM is an inference-only image forgery localization bundle built around SAM2. This release includes bundled checkpoints and a small set of ready-to-run examples for demos.

What is bundled

• Inference checkpoints under checkpoints/
• SAM2 config and weights 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 aliases:

• detective_sam
• detective_sam_sota

Setup

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

Hugging Face Usage

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

3. Bundled SOTA examples

Flux example:

python -m detectivesam_inference.predict \
 --checkpoint detective_sam_sota \
 --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_sota \
 --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 \
 --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 the YAML sidecars in checkpoints/; you only need the alias or checkpoint path.
• This repo does not include training code or training-only dependencies.
• License metadata is currently marked other: the bundled SAM2 components are Apache-2.0, while DetectiveSAM release terms should be finalized before broader redistribution.