Instructions to use ProbeX/Model-J__ResNet__model_idx_0453 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_0453 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_0453") 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_0453") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0453") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0db9f1fe0156879c8d3c0c8b6ecc9ef56ca977abce55f58449dec7efbfabe6ec
- Size of remote file:
- 5.37 kB
- SHA256:
- 4bbcda72fd4acedd844608b7c5abac4c41ede8373271ecd889efbbaca8c797b5
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