Instructions to use bn22/naflexvit_small_patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use bn22/naflexvit_small_patch16 with timm:
import timm model = timm.create_model("hf_hub:bn22/naflexvit_small_patch16", pretrained=True) - Transformers
How to use bn22/naflexvit_small_patch16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bn22/naflexvit_small_patch16") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bn22/naflexvit_small_patch16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e47a3058b131dcb7744f8ea011c8266aa4f13b5cffa24e0a1263d353440bfe8
- Size of remote file:
- 93.9 MB
- SHA256:
- cf1381adb497c095b2346b6c22cce010039b25d0aa631dc037a6d28fb0cf576f
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