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
File size: 390 Bytes
c8c46cf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | VENV_NAME = venv
PYTHON = $(VENV_NAME)/bin/python
.PHONY: all setup install train infer clean
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__
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