Instructions to use timm/mobilenetv4_conv_medium.e500_r256_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/mobilenetv4_conv_medium.e500_r256_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/mobilenetv4_conv_medium.e500_r256_in1k", pretrained=True) - Transformers
How to use timm/mobilenetv4_conv_medium.e500_r256_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/mobilenetv4_conv_medium.e500_r256_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/mobilenetv4_conv_medium.e500_r256_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ac16f935bb42593d27a975e18a7276079eaead94c719c930b745d06c251d0f4
- Size of remote file:
- 39.3 MB
- SHA256:
- 6bc757dd471e255d240fe9cb17d9171f86b62a3717e2ae1f748b9ab0f0fc1267
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