Instructions to use ProbeX/Model-J__ResNet__model_idx_0376 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_0376 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_0376") 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_0376") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0376") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d1c8c2693a8b87b720fa7b8fb4270f6e6a8c52231f445415ce4e7e91345ff41
- Size of remote file:
- 5.37 kB
- SHA256:
- 3289af8682bfe4e982995c858891c53d9ecc3f7ae2340942757e7775e4135591
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