Instructions to use ProbeX/Model-J__ResNet__model_idx_0404 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_0404 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_0404") 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_0404") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0404", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b267f3da3a09db3a0234e70d11ee8a3f73b2d553ef90e4468ab1ad5ef280d52f
- Size of remote file:
- 171 MB
- SHA256:
- 95a38f4a317face85b45c9c79fee87da47c69ff6f8c984cfd98b92bc40637cd5
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