Instructions to use ProbeX/Model-J__ResNet__model_idx_0340 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_0340 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_0340") 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_0340") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0340") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a209cb256827143fa70d10c6b48877230be8f717736f6ef4a57a41e3d6f00f0
- Size of remote file:
- 5.37 kB
- SHA256:
- 579caaba506029c44f0f92454787e1c2e837f153c0bcbdb11c6d28a8ea7a0cd0
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