Instructions to use ProbeX/Model-J__ResNet__model_idx_0982 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_0982 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_0982") 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_0982") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0982", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2751f65d15bfe9b9db118564b6b2d56ae1e6a537783ea33d8d749fcec8edeb3
- Size of remote file:
- 171 MB
- SHA256:
- 045829d5801fc3e8fc78704b88368b1c78573121f0e2ddad8c88c1e7e89c0814
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