Feature Extraction
Transformers
Safetensors
PyTorch
phorensics
computer-vision
media-forensics
deepfake-detection
physics-based-vision
signal-processing
custom_code
Instructions to use Anuran66/Phorensics-Engine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Anuran66/Phorensics-Engine with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Anuran66/Phorensics-Engine", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Anuran66/Phorensics-Engine", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Anuran66/Phorensics-Engine: direct link, hf CLI and curl.
- Browser
- Download file 337 Bytes
-
https://huggingface.co/Anuran66/Phorensics-Engine/resolve/main/config.json
- Command line
-
hf download hf://Anuran66/Phorensics-Engine/config.json
-
curl -L -o config.json https://huggingface.co/Anuran66/Phorensics-Engine/resolve/main/config.json
337 Bytes
| { | |
| "architectures": [ | |
| "PhorensicsModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "phorensics_model.PhorensicsConfig", | |
| "AutoModel": "phorensics_model.PhorensicsModel" | |
| }, | |
| "dtype": "float32", | |
| "max_dim": 600, | |
| "model_type": "phorensics", | |
| "patch_size": 128, | |
| "stride": 64, | |
| "transformers_version": "5.18.0" | |
| } | |