Instructions to use ProbeX/Model-J__ResNet__model_idx_0887 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_0887 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_0887") 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_0887") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0887") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9909d473f2a8617d317ebad645eb15b4e4d2b12800ad974b1ba144bce12a5655
- Size of remote file:
- 5.37 kB
- SHA256:
- 7adf88f810d622537e45d0e635e40536f00771e46b2203c621424caaa015e547
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