Instructions to use ProbeX/Model-J__ResNet__model_idx_0971 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_0971 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_0971") 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_0971") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0971", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fc43caf361e21a36ff09cd662a210fbec71da553e156d5cb1c93e95cd8ed0915
- Size of remote file:
- 5.37 kB
- SHA256:
- 3b1dfc496a74b093e7dcb88b848da112a332b351a755db5a2525305336e02faf
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