Instructions to use Foxasdf/EfficientNetV2_Small_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Foxasdf/EfficientNetV2_Small_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Foxasdf/EfficientNetV2_Small_v1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Foxasdf/EfficientNetV2_Small_v1") model = AutoModelForImageClassification.from_pretrained("Foxasdf/EfficientNetV2_Small_v1", device_map="auto") - timm
How to use Foxasdf/EfficientNetV2_Small_v1 with timm:
import timm model = timm.create_model("hf_hub:Foxasdf/EfficientNetV2_Small_v1", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1813d755a298d0ec808c49ce77c6f16fc844a15a37e66cb0dbeb3b3e7c9d932d
- Size of remote file:
- 81.4 MB
- SHA256:
- b3f525be6194b9f353acc6ece96d24321759ec199c5e43fbf10d22f0c03c2821
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