Instructions to use ProbeX/Model-J__ResNet__model_idx_0744 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_0744 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_0744") 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_0744") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0744", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e69ae58f6f5e6a58c0e9c7931f3571841fbe433b4ff661d664d46b55a20014f
- Size of remote file:
- 5.37 kB
- SHA256:
- 7d7c41b7a20d92447f40fb641c24868b0a4b62b1f075f01ddbf5c5069b74f12d
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