Instructions to use ProbeX/Model-J__ResNet__model_idx_0633 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_0633 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_0633") 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_0633") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0633") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6173f37c7e684601921fced2362c34c525ddf6606a566d8698e2bf82fee5f22b
- Size of remote file:
- 5.37 kB
- SHA256:
- c1b0044111c7124724b3336b91c5bd51022b67af5d575d6e0f25df4b6ac1498f
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