Instructions to use ProbeX/Model-J__ResNet__model_idx_0821 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_0821 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_0821") 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_0821") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0821") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24a01ccfd33acc73217c666fe6785195aca738c5f5b09302ac3306427a57b2da
- Size of remote file:
- 171 MB
- SHA256:
- 63353adce8f214c4c0ed98c233cfade18ed84721e564367a830f7203ed9bdb2d
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