Instructions to use ProbeX/Model-J__ResNet__model_idx_0909 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_0909 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_0909") 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_0909") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0909", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6aa8f045da6b13f4215f2c5718723a215cd9359db68d4f00dc8f6bca4460b315
- Size of remote file:
- 5.37 kB
- SHA256:
- 7f5ca618d8ba8eb2ebd440376428167830ed70fba1d10cbea942e6acb4f367af
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