Instructions to use ProbeX/Model-J__ResNet__model_idx_0732 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_0732 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_0732") 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_0732") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0732", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8dd9bf3396ecb7185cf198bbc67f1ff65ee5a232f0fdf957dfe36d08d2df4cc9
- Size of remote file:
- 171 MB
- SHA256:
- 53eab6073710822dbe25df4e17680259b7eec6a311626520bf26899653055c05
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