Instructions to use ProbeX/Model-J__ResNet__model_idx_0960 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_0960 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_0960") 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_0960") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0960", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c43cd5dd363b80b58e3c8f201c138904e01cf73c0777f36933e02e401311813e
- Size of remote file:
- 5.37 kB
- SHA256:
- 06be0ef17907edf72aaa2d5167c38aad4424f924d279c7601d203a9e7dc5af27
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