Image Classification
Transformers
TensorBoard
Safetensors
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use ricardoSLabs/jaffe_V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ricardoSLabs/jaffe_V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ricardoSLabs/jaffe_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("ricardoSLabs/jaffe_V2") model = AutoModelForImageClassification.from_pretrained("ricardoSLabs/jaffe_V2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ricardoSLabs/jaffe_V2: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/ricardoSLabs/jaffe_V2/resolve/main/training_args.bin
- Command line
-
hf download hf://ricardoSLabs/jaffe_V2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ricardoSLabs/jaffe_V2/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 71f111098e49477824b6461b5bc452b5e23effc415f34548c6660afd263422f3
- Size of remote file:
- 5.3 kB
- SHA256:
- 2d6d31f563fffae6cf85ff0950454aa48ec2fa654a819b39b9e1f164aad804ae
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