Instructions to use henriquequeirozcunha/microvit-s3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henriquequeirozcunha/microvit-s3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="henriquequeirozcunha/microvit-s3", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("henriquequeirozcunha/microvit-s3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload MicroViT-S3 pretrained weights
Browse files- README.md +3 -0
- pretrained_weights.pth +3 -0
README.md
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# MicroViT-S3
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ImageNet-1K pretrained weights. 16.7M params, 77.1% Top-1.
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Source: https://github.com/novendrastywn/MicroViT
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pretrained_weights.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:4f8c444803ced48c122e46efd8ad84c77df0cad03876a7e486b13701cb294e40
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size 57440338
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