Instructions to use ProbeX/Model-J__ResNet__model_idx_0966 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_0966 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_0966") 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_0966") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0966") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 028b9f9f7a13ae2858998b6c428da3067c38826f9d46d12cef93e06fe48ebdb0
- Size of remote file:
- 5.37 kB
- SHA256:
- eafc6fb7de0b99c7588bb63ea8d51e3fedd9bc791640bbffd075e85e768209d2
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