Instructions to use ProbeX/Model-J__ResNet__model_idx_0377 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_0377 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_0377") 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_0377") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0377") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eecfaf53945b3bc66e98bc90136d298403c505c4acb7813cc458e521756772e0
- Size of remote file:
- 171 MB
- SHA256:
- b3dcd0cbd749880ac2ab9bfa4f7d40c61c1b904302bd6d53348c0e4ffa5a394f
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