Instructions to use ProbeX/Model-J__ResNet__model_idx_0794 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_0794 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_0794") 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_0794") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0794", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae62666186bee7f18194f540c213e401c6375d8e40bed06dacabebb87d7f4446
- Size of remote file:
- 5.37 kB
- SHA256:
- 8e1530c6c145ee5c70be445637ed77b91d261735254424282106a2fe73ecbaaa
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