Instructions to use lysandre/tiny-vit-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lysandre/tiny-vit-random with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="lysandre/tiny-vit-random") 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("lysandre/tiny-vit-random") model = AutoModelForImageClassification.from_pretrained("lysandre/tiny-vit-random") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 1e239f4
Image size 16 > 224
Browse files- config.json +1 -1
- pytorch_model.bin +2 -2
config.json
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"998": "ear, spike, capitulum",
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"999": "toilet tissue, toilet paper, bathroom tissue"
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"image_size":
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"initializer_range": 0.02,
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"intermediate_size": 32,
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"label2id": {
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"998": "ear, spike, capitulum",
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"999": "toilet tissue, toilet paper, bathroom tissue"
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"image_size": 224,
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"initializer_range": 0.02,
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"intermediate_size": 32,
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"label2id": {
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 323543
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