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:
- 2c5006eef2cb742cadef77d062d277b1b70ce53ab4a94882fea4675dabd782a1
- Size of remote file:
- 5.2 kB
- SHA256:
- f9117fdd7dfca5577dec1c1e1d1b7f51f374ab196ab2f93b7e8922bdcc002b11
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