Instructions to use ProbeX/Model-J__ResNet__model_idx_0280 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_0280 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_0280") 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_0280") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0280") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d815b1666a814fd16b085a6a44d2b91ac721aa135202a9efc2387208e32d67ec
- Size of remote file:
- 171 MB
- SHA256:
- 571019d1909beb93687697428e27640c7b3f3337706d8bb024dc037e7952a456
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