Instructions to use ProbeX/Model-J__ResNet__model_idx_0474 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_0474 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_0474") 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_0474") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0474", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4968479a0b9ec644e769786cd67a7e839344859974423a66e2aaecbef1e8123c
- Size of remote file:
- 5.37 kB
- SHA256:
- 3b969ddb0614869c393494cbf37163a433ae5b76f2ed82fa2b0dea48bb23d9ee
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