Instructions to use ProbeX/Model-J__ResNet__model_idx_0839 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_0839 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_0839") 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_0839") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0839", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c302f8cd4d4425148721d62f6b4baa1881b3480e107f1ce401c49b2fb2fc89d9
- Size of remote file:
- 5.37 kB
- SHA256:
- 82a986fba4a96f1d7d5c3f1a3aa81b495fc59cbc9700d38ea9d0b339c8ee6ac2
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