Instructions to use ProbeX/Model-J__ResNet__model_idx_0976 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_0976 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_0976") 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_0976") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0976", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98fc1816a6e49661a6a2785f61ac5b0a83c1e8852faf88421adb8adc277ab151
- Size of remote file:
- 171 MB
- SHA256:
- cfb2662748e86c4c1273e12bed1cac68e669b55ad917f7df6e807b8118ecc616
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