Instructions to use ProbeX/Model-J__ResNet__model_idx_0593 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_0593 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_0593") 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_0593") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0593") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6175a004bd76b55245f566cd625f617b4c78cf57267f5f7eba7e70042dbeb2d1
- Size of remote file:
- 5.37 kB
- SHA256:
- eeb0d128b431eb0b9aa7018e82bccb27d85d373811849bfbf41efb897b5b0b8d
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