Instructions to use ProbeX/Model-J__ResNet__model_idx_0963 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_0963 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_0963") 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_0963") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0963", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c1099c9cf6602fd349016a0741fffea3b30dcd1c033f1d346be0b71755788df1
- Size of remote file:
- 171 MB
- SHA256:
- cb45fb1042a17bbd69e52eaacc8b9fb11fafa67487467316bae2beac2c9206da
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