Instructions to use ProbeX/Model-J__ResNet__model_idx_0966 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_0966 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_0966") 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_0966") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0966", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96b13f3a62c0bf7c60d20dab7bf024ea6c3886a45e714f7d010ee5fd4eb29382
- Size of remote file:
- 171 MB
- SHA256:
- c8b10e370761a4f99fc9d3efb1436159a5dde3c311811ca1c7de013f62d6c86d
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