Update README.md

README.md

@@ -1,3 +1,129 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: image-segmentation
+tags:
+- manipulation
+- forgery
+- image
+- cnn
+- transformer
+- residual-noise
+- efficientnet
+- 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:
+
+- `casia-effnet.pt`
+- `casia-effnet+noise.pt`
+- `casia-effnet+swin.pt`
+- `casia-full.pt`
+- `casia-swin.pt`
+- `casia-swin+noise.pt`
+
+## Run Inference
+
+### Recommended: Google Colab
+
+The easiest way to test the model is through Colab:
+
+- Google Colab: [Open `infer.ipynb` in Colab](https://colab.research.google.com/github/juhenes/ngiml-infer/blob/main/infer.ipynb)
+
+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:
+
+- GitHub: [juhenes/ngiml-infer](https://github.com/juhenes/ngiml-infer)
+
+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}
+}
+```