Instructions to use MinhLe999/3class_EfficientNetv2_ForTesting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MinhLe999/3class_EfficientNetv2_ForTesting with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MinhLe999/3class_EfficientNetv2_ForTesting", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("MinhLe999/3class_EfficientNetv2_ForTesting", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9593f27a17659f3f99669018aa4ea89edfc92ed7cbc5d059e2be64042ca02d9
- Size of remote file:
- 5.27 kB
- SHA256:
- 77972b5c880320b1702ee898d60172d236e01e307d4510872190de49dcb33822
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