Instructions to use ProbeX/Model-J__ResNet__model_idx_0220 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0220 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0220") 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("ProbeX/Model-J__ResNet__model_idx_0220") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0220", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98733735d69e8880c3a9fa5e5e71fe532085e4d939a0af2c6fdd976b64f8405e
- Size of remote file:
- 5.37 kB
- SHA256:
- 460d986366e3ff22e68e3cfa633a268d72c70d9c3a639b2420623ffe62e8b560
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