Instructions to use ProbeX/Model-J__ResNet__model_idx_0895 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_0895 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_0895") 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_0895") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0895", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bfc477b01740d5a8007f4c3133164431831875abab96f79799c8a2ebedd16330
- Size of remote file:
- 5.37 kB
- SHA256:
- 879db2aff33deeb93fe25417802c6cc96071822d4c8e6a0be260c3b56b5539a6
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