Instructions to use ProbeX/Model-J__ResNet__model_idx_0128 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_0128 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_0128") 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_0128") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4a44f9c9a14d8eb086c82df36a9bf51e7d01971947ddaf7896298dab1d8a253
- Size of remote file:
- 171 MB
- SHA256:
- 37bdc0118e7dd345f0b0a527ef6316516e5fe9787dfe1de40731d1103624197f
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