Instructions to use spicy03/vit-base-oxford-iiit-pets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spicy03/vit-base-oxford-iiit-pets with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="spicy03/vit-base-oxford-iiit-pets") 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("spicy03/vit-base-oxford-iiit-pets") model = AutoModelForImageClassification.from_pretrained("spicy03/vit-base-oxford-iiit-pets", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files
config.json
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.53.0"
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}
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model.safetensors
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runs/Jul03_05-58-13_1f389e3b739f/events.out.tfevents.1751522301.1f389e3b739f.1382.0
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training_args.bin
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size 5368
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