Instructions to use dpaul93/cifake_vit_generated-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dpaul93/cifake_vit_generated-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dpaul93/cifake_vit_generated-v2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dpaul93/cifake_vit_generated-v2") model = AutoModelForImageClassification.from_pretrained("dpaul93/cifake_vit_generated-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dpaul93/cifake_vit_generated-v2: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/dpaul93/cifake_vit_generated-v2/resolve/main/training_args.bin
- Command line
-
hf download hf://dpaul93/cifake_vit_generated-v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dpaul93/cifake_vit_generated-v2/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- cf954ab2584b12d1e884c96422b0ab159190426481dd6649f9796f93e9345f9d
- Size of remote file:
- 5.05 kB
- SHA256:
- a1b06ebee886160f65b8f81e9e5bf1fbc75cb8b141e84eb69e3c0692d5802552
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