Instructions to use ProbeX/Model-J__ResNet__model_idx_0504 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_0504 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_0504") 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_0504") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0504", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ce5a6e33c04b53b4cb344a88c097944cf504cba4cd53a06618bfe497e184557
- Size of remote file:
- 5.37 kB
- SHA256:
- 2d8d0771de0ff20e4273c008cdf3809b5f78610ef36c6e7978b0b45fc90ae210
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