Instructions to use ProbeX/Model-J__ResNet__model_idx_0786 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_0786 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_0786") 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_0786") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0786", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 524b225949269d2d62515f05a88ee8e3a6c4f1a41dcf169453bcd11decd25a76
- Size of remote file:
- 171 MB
- SHA256:
- e264b94317cfc39071a16d02e7afe2e549795f513811faf9153653cfc3512333
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