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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 167ad15c74b3b03a04154b43da9bd84726cbdf821576b1faffd9c3fd780fe322
- Size of remote file:
- 81.4 MB
- SHA256:
- 31cc96e62f66c6950dfcd6b1287b15342588bec3d415df8b52f01a3403844488
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.