Instructions to use hamzenium/ViT-Deepfake-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hamzenium/ViT-Deepfake-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hamzenium/ViT-Deepfake-Classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hamzenium/ViT-Deepfake-Classifier") model = AutoModelForImageClassification.from_pretrained("hamzenium/ViT-Deepfake-Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| VENV_NAME = venv | |
| PYTHON = $(VENV_NAME)/bin/python | |
| build: setup install | |
| setup: | |
| python3 -m venv $(VENV_NAME) | |
| install: | |
| $(PYTHON) -m pip install --upgrade pip | |
| $(PYTHON) -m pip install -r requirements.txt | |
| train: | |
| $(PYTHON) train.py | |
| test: | |
| $(PYTHON) test.py | |
| infer: | |
| uvicorn main:app --reload | |
| clean: | |
| rm -rf $(VENV_NAME) | |
| rm -rf __pycache__ | |