Instructions to use prithivMLmods/Deepfake-QualityAssess-85M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Deepfake-QualityAssess-85M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Deepfake-QualityAssess-85M") 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("prithivMLmods/Deepfake-QualityAssess-85M") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Deepfake-QualityAssess-85M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f107f606479994108d994ffe0496833de076082044447bc0ecf6c70c2775ad70
- Size of remote file:
- 5.24 kB
- SHA256:
- 486b94e9fe641b88e7cc93143ba88f12ae8a24a19e9cfa852e2451bbf6270762
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.