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meet4150/
50_epoch_aide

Name: 50_epoch_aide
Availability: InStock

ai-generated-image-detection

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50 Epoch AIDE

This repository packages the 50-epoch AIDE checkpoint from:

/home/meet/Aivsre_001/AIDE/output_pico_balanced_49k_run1/checkpoint-50.pth

The model is exported as:

model.safetensors for safer deployment
checkpoint-50.pth as the original PyTorch training snapshot

Model

This is the same hybrid AIDE architecture used in the original repository:

fixed 30-filter SRM high-pass module
dual ResNet-50-style forensic encoders
frozen OpenCLIP ConvNeXt-XXL visual trunk
final MLP fusion head for binary classification

The model predicts:

0 -> real
1 -> fake

Input Preparation

Inference follows the original AIDE pipeline:

convert image to RGB
convert to tensor in [0, 1]
generate four DCT-based reconstructed views with DCT_base_Rec_Module
normalize all five views with ImageNet mean/std
stack in this order:

[x_minmin, x_maxmax, x_minmin1, x_maxmax1, x_0]

The provided inference.py keeps the exact same preparation logic.

Training Run

This packaged checkpoint comes from the local run:

data_path=/home/meet/Aivsre_001/aide_data/train_pico_balanced_49k_v1
eval_data_path=/home/meet/Aivsre_001/aide_data/eval_pico_balanced_49k_v1
batch_size=8
blr=1e-4
weight_decay=0.0
epochs=52
checkpoint exported here: epoch 50

Metrics

From output_pico_balanced_49k_run1/log.txt:

epoch 49 validation/top-1 accuracy: 99.2076
epoch 50 validation/top-1 accuracy: 99.1061
epoch 50 validation loss: 0.0730
epoch 51 validation/top-1 accuracy: 99.1264

This repository specifically publishes checkpoint-50 because that is the requested 50-epoch model snapshot.

Files In This Repo

model.safetensors
checkpoint-50.pth
config.json
model.json
preprocessor_config.json
inference.py
models/
data/
requirements.txt
LICENSE

Usage

Install requirements:

pip install -r requirements.txt

Run local inference:

python inference.py --repo_dir . --image /path/to/image.jpg

Or use it programmatically:

python

from PIL import Image

from inference import load_model, predict_pil_images

model = load_model(".")
image = Image.open("example.jpg").convert("RGB")
result = predict_pil_images(model, [image])[0]
print(result)

Notes

This repository is for weight hosting and local inference
the architecture is custom and not a native transformers AutoModel
use the provided code files with the exported weights

Credits

Derived from the original AIDE implementation:

Project: https://github.com/shilinyan99/AIDE
Paper: https://arxiv.org/abs/2406.19435

meet4150/50_epoch_aide