Instructions to use ProbeX/Model-J__ResNet__model_idx_0797 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_0797 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_0797") 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_0797") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0797", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bae0ac9aee793f1819a570968ffcbb4575187c44483eefe16e288141fa2e98c7
- Size of remote file:
- 171 MB
- SHA256:
- 0ba811b45baacb16670de798e269e078549a182ece44881f1b9c44851465394f
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