Instructions to use ProbeX/Model-J__ResNet__model_idx_0882 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_0882 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_0882") 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_0882") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0882", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ed7c4854078e68ced3b34828afd5a3e33de8a01de8bcb80fd6e8a4440bb4244
- Size of remote file:
- 5.37 kB
- SHA256:
- f49b014ebe6beb5cfefefd6d61dda1b2f9436e8eb44fbdd71313252d438c03c5
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