Instructions to use ProbeX/Model-J__ResNet__model_idx_0571 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_0571 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_0571") 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_0571") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0571") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f96e5d83b59ea3f0d7c0f7f84289cf0f66c098780e206c2e011926608b97c576
- Size of remote file:
- 5.37 kB
- SHA256:
- 30fe5afa6ce2c381076fe73ced60b09d38062a227021e62973a0adcd8142ffbe
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