Instructions to use ProbeX/Model-J__ResNet__model_idx_0499 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_0499 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_0499") 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_0499") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0499") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fcfc37a96f73d67bb09a0f96af18b746aca76fb1f51bd01e1790fa523cf1f1b9
- Size of remote file:
- 5.37 kB
- SHA256:
- 3f9aeb0f91a4b99f830d41814d289ba258a45a56d862e298e5099c148c983770
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