Instructions to use ProbeX/Model-J__ResNet__model_idx_0264 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_0264 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_0264") 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_0264") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0264", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6224246950d5d580d21b49c4c5f3fc76c34ad0dbb350e1afe0b0ebe59bedb8f
- Size of remote file:
- 171 MB
- SHA256:
- 1e7f576a659f525c7f5bcfb5251712b1848ef218e8950afa5b2a7d13fa2a6544
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