Instructions to use timm/maxvit_tiny_tf_384.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/maxvit_tiny_tf_384.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/maxvit_tiny_tf_384.in1k", pretrained=True) - Transformers
How to use timm/maxvit_tiny_tf_384.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/maxvit_tiny_tf_384.in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/maxvit_tiny_tf_384.in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4dd19ef0c4b9f4d202e33a1868fce0f33091d29c12ba8f93b18d845c6e63d302
- Size of remote file:
- 124 MB
- SHA256:
- 5c3bf4365f456c8c30964dbe951dcd9e16240935b8cbb644b7148be19cc2262d
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