Instructions to use chandra1976/vit-facial-expression-fatigue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chandra1976/vit-facial-expression-fatigue with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="chandra1976/vit-facial-expression-fatigue") 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("chandra1976/vit-facial-expression-fatigue") model = AutoModelForImageClassification.from_pretrained("chandra1976/vit-facial-expression-fatigue", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- config.json +1 -2
- model.safetensors +1 -1
- training_args.bin +2 -2
config.json
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"pooler_output_size": 768,
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"transformers_version": "
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"use_cache": false
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}
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"pooler_output_size": 768,
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"transformers_version": "4.57.6"
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}
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model.safetensors
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training_args.bin
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