Instructions to use ProbeX/Model-J__ResNet__model_idx_0973 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_0973 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_0973") 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_0973") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0973", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 713b3aa8909c8ec0728b8592d30bec27da58ef035a496a40f7e9a6f7a10c44bf
- Size of remote file:
- 5.37 kB
- SHA256:
- e722b0fcafeebf2f0532e9f1579effcf0bc0debe0c45a1020d767adba426f139
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