Instructions to use ProbeX/Model-J__ResNet__model_idx_0334 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_0334 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_0334") 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_0334") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0334", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 217ca47a1c739317841fbb21b53e8e281d88b20d9adc64eeaeeac4a98f7d5a6f
- Size of remote file:
- 5.37 kB
- SHA256:
- 7f328bf4098afaf2d68f6ace862990f2c5e9e3ae15625b47bcc53ac5a3493e85
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