Instructions to use ProbeX/Model-J__ResNet__model_idx_0912 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_0912 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_0912") 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_0912") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0912", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f47eb4b91379aad90a97be038410528c7af668d9880a4abc149dadbdee1b0d7d
- Size of remote file:
- 171 MB
- SHA256:
- 0fd1c62c2d0f6c8ecef71d22f10dab3a2fc7a7040dfdebe78a2f26205041d794
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