Instructions to use ProbeX/Model-J__ResNet__model_idx_0791 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_0791 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_0791") 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_0791") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0791", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56672a1f9d7fcbacbbc82f7abc754320157c670c6d479cccaf069f1ef57440e9
- Size of remote file:
- 171 MB
- SHA256:
- 57d6e6134569b4752acd3b50994c9975c66d0d7af35c0ba3ccf48491fd1a0b0f
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