Instructions to use ProbeX/Model-J__ResNet__model_idx_0768 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_0768 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_0768") 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_0768") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0768", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03103f2a2c13519f9b9f7595459f36cfbccebd2fab7b282180b5c9b089c10c81
- Size of remote file:
- 5.37 kB
- SHA256:
- 59a08389c4db67f8d59ad45d5076bfdd657a8da17afe58210deed93b71d1756b
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