Instructions to use ProbeX/Model-J__ResNet__model_idx_0195 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_0195 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_0195") 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_0195") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0195", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 480bcc4eb7ee4a96f2976c613c4a318ebecb283784d069f93e763917f2757a68
- Size of remote file:
- 171 MB
- SHA256:
- 795d2fc55f608cb9629c8a80d3e2e4bb6bac1ff556ba2eb3b7815f1cc48dd174
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