Instructions to use ProbeX/Model-J__ResNet__model_idx_0586 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_0586 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_0586") 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_0586") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0586") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9f11d4f3a2eff0192fd5c152a24d2a5ae3ef4228de43e5cffbb186e06d1f1b8
- Size of remote file:
- 171 MB
- SHA256:
- d0b8793ec6d95255dcb4e5ed25778e333054e9d1f2faae08b7cd22697ea77373
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