Instructions to use ProbeX/Model-J__ResNet__model_idx_0730 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_0730 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_0730") 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_0730") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0730", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12e946c3cdd572c97a8f0fce0a3e668ffbc55dd655e40d9bebaa9fcf10867b91
- Size of remote file:
- 5.37 kB
- SHA256:
- dd7f4568b095836ce261ae0d9a82630be8ec5124f403b2accbca9fd42b00a5e6
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