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Gertlek/
DetectiveSAMv2

Name: DetectiveSAMv2
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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.