Update README.md

README.md

@@ -14,26 +14,37 @@ tags:
 - swin
 - localization
 ---
+
 # NGIML Model Card
 
 ## Inference
 
 NGIML performs single-image forgery localization from a pretrained checkpoint and an input RGB image.
 
+---
+
 ## Checkpoints
 
 Pretrained checkpoints are hosted on Hugging Face:
 
 [juhenes/ngiml](https://huggingface.co/juhenes/ngiml)
 
-Available checkpoints:
+### CASIA2 / Extended Models
+
+- `CASIA2-EffNet-42.pt` (110 MB)
+- `CASIA2-EffNet+Noise-42.pt` (172 MB)
+- `CASIA2-EffNet+Swin-42.pt` (593 MB)
+- `CASIA2-Full-42.pt` (618 MB)
+- `CASIA2-Full-4.pt` (618 MB)
+- `CASIA2-Full-420.pt` (618 MB)
+- `CASIA2-Full(mbconv)-42.pt` (611 MB)
+- `CASIA2-Swin-42.pt` (495 MB)
+- `CASIA2-Swin+Noise-42.pt` (556 MB)
 
-- `casia-effnet.pt`
-- `casia-effnet+noise.pt`
-- `casia-effnet+swin.pt`
-- `casia-full.pt`
-- `casia-swin.pt`
-- `casia-swin+noise.pt`
+### Additional Datasets / Models
+
+- `CCC-Full-42.pt` (618 MB)
+- `TampCOCO-Full-42.pt  (618 MB)`
 
 ## Run Inference
 
@@ -45,6 +56,8 @@ The easiest way to test the model is through Colab:
 
 This is the recommended path for quick testing because the notebook is already set up for checkpoint-based inference.
 
+---
+
 ### Local CLI
 
 If you want to run the project locally, use the repository files here:
@@ -54,76 +67,4 @@ If you want to run the project locally, use the repository files here:
 Install the dependencies:
 
 ```bash
-pip install -r requirements.txt
-```
-
-Run the CLI:
-
-```bash
-python predict.py --checkpoint /path/to/checkpoint.pt --image /path/to/image.png
-```
-
-Example:
-
-```bash
-python predict.py --checkpoint checkpoints_cache/casia-full.pt --image /path/to/image.png
-```
-
-If `--output-dir` is omitted, outputs are saved under `outputs/<image-stem>/`.
-
-## Optional Arguments
-
-- `--output-dir` to choose where outputs are saved
-- `--threshold` to override the default binary threshold
-- `--normalization-mode` to set `imagenet` or `zero_one`
-- `--resize-max-side` to resize large images before preprocessing
-- `--crop-size` to override the inference crop size
-- `--device` to choose a device such as `cpu` or `cuda:0`
-
-## Output Files
-
-When an output directory is used, the runtime saves:
-
-- `input_rgb.png`
-- `preview_input_rgb.png`
-- `preview_probability_map.png`
-- `preview_binary_mask.png`
-- `preview_overlay.png`
-- `probability_map.png`
-- `binary_mask.png`
-- `overlay.png`
-- `prediction.json`
-
-`prediction.json` includes summary metadata such as the checkpoint path, threshold, normalization mode, device, and basic prediction statistics.
-
-## References
-
-1. Dong, J., Wang, W., and Tan, T. "CASIA Image Tampering Detection Evaluation Database." 2013 IEEE China Summit and International Conference on Signal and Information Processing, 2013. [DOI](https://doi.org/10.1109/chinasip.2013.6625374)
-
-```bibtex
-@inproceedings{Dong2013,
-  doi = {10.1109/chinasip.2013.6625374},
-  url = {https://doi.org/10.1109/chinasip.2013.6625374},
-  year = {2013},
-  month = jul,
-  publisher = {{IEEE}},
-  author = {Jing Dong and Wei Wang and Tieniu Tan},
-  title = {{CASIA} Image Tampering Detection Evaluation Database},
-  booktitle = {2013 {IEEE} China Summit and International Conference on Signal and Information Processing}
-}
-```
-
-2. Pham, N. T., Lee, J.-W., Kwon, G.-R., and Park, C.-S. "Hybrid Image-Retrieval Method for Image-Splicing Validation." Symmetry, 11(1), 83, 2019.
-
-```bibtex
-@article{pham2019hybrid,
-  title = {Hybrid Image-Retrieval Method for Image-Splicing Validation},
-  author = {Pham, Nam Thanh and Lee, Jong-Weon and Kwon, Goo-Rak and Park, Chun-Su},
-  journal = {Symmetry},
-  volume = {11},
-  number = {1},
-  pages = {83},
-  year = {2019},
-  publisher = {Multidisciplinary Digital Publishing Institute}
-}
-```
+pip install -r requirements.txt