---
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)

### 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)

### Additional Datasets / Models

- `CCC-Full-42.pt` (618 MB)
- `TampCOCO-Full-42.pt  (618 MB)`

## 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