Instructions to use timm/efficientnet_lite0.ra_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/efficientnet_lite0.ra_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/efficientnet_lite0.ra_in1k", pretrained=True) - Transformers
How to use timm/efficientnet_lite0.ra_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/efficientnet_lite0.ra_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/efficientnet_lite0.ra_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 132a26be81dd49a6f86df66b068a23185aafff28b3f3afe2ec1f7f21adb8fcd5
- Size of remote file:
- 18.9 MB
- SHA256:
- 06511aeb2872f1ca56694cb9de90ba937f71f2eecc11e89b3bf62642929897cd
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