Instructions to use ProbeX/Model-J__ResNet__model_idx_0640 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_0640 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_0640") 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_0640") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0640", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c737c3463f3b8accd3e072abf9016a8cb6761ad4f674b59a72b8b7ba3da53283
- Size of remote file:
- 171 MB
- SHA256:
- 114e2bd3b09e258e8d6fc5cfd9bb599662a9092bcfe7c29c221fe632f84c830c
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