Instructions to use ProbeX/Model-J__ResNet__model_idx_0637 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_0637 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_0637") 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_0637") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0637", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c87cbcb7cde745d28280c05c5d04527c6d338e44b940820cb13adde1056b3e92
- Size of remote file:
- 171 MB
- SHA256:
- 556c60392228d4f3e9ce46b54b95f73f0d046140185937e3c9bf7b3a1faa4420
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