Instructions to use whj363636/SPTNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whj363636/SPTNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="whj363636/SPTNet") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("whj363636/SPTNet", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload pretrained model
Browse files
aircraft/dinoB16_best_trainul.pt
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cifar10/dinoB16_best_trainul.pt
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cifar100/dinoB16_best_trainul.pt
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oid sha256:508f656f60a1a3125b6c84f0d6d826d9dbbc8322c8e0d94a11fa26f646fcc7b9
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cub/dinoB16_best_trainul.pt
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oid sha256:469590bc3018c1dc6c9038b66edf2d0abdbe45507f45454cdf997e779286553e
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size 369933051
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herb19/dinoB16_best_trainul.pt
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imagenet100/dinoB16_best_trainul.pt
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oid sha256:a4596d9f527a1ae38265dd3490c6e19414e92fb75537ed8ba53beb4b645444c5
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scars/dinoB16_best_trainul.pt
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oid sha256:fd3cd408a4ddc5042a274edafb56855d33b4d1b5a3995d6c2dd6923520562dc8
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size 369920763
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