README.md

metadata

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.

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Checkpoints

Pretrained checkpoints are hosted on Hugging Face:

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

This is the recommended path for quick testing because the notebook is already set up for checkpoint-based inference.

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Local CLI

If you want to run the project locally, use the repository files here:

• GitHub: juhenes/ngiml-infer

Install the dependencies:

pip install -r requirements.txt