Instructions to use timm/tf_efficientnet_b8.ap_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/tf_efficientnet_b8.ap_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/tf_efficientnet_b8.ap_in1k", pretrained=True) - Transformers
How to use timm/tf_efficientnet_b8.ap_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/tf_efficientnet_b8.ap_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/tf_efficientnet_b8.ap_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54f14a8535dc9d179a8268f223504ccfdb72fbf8b08c2b236afe852ad0f9dae9
- Size of remote file:
- 352 MB
- SHA256:
- c970cfb408fe3f066b6e475fe2a20872b47e36d61431b8172e54944faf1beb7a
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