Instructions to use ProbeX/Model-J__ResNet__model_idx_0910 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_0910 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_0910") 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_0910") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0910", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 36138860c5c51077a3e21f25fd85677798f7894df00e1b38ab9dd9532b703e62
- Size of remote file:
- 171 MB
- SHA256:
- 0f087f7de0b790e1da7b8689879dda03cc7cb3f41a8aad165be5a8cce319aac2
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