Instructions to use ProbeX/Model-J__ResNet__model_idx_0986 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_0986 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_0986") 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_0986") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0986", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ee98c407deb387cacd38f9361e4cda1b42b3069c2ba7b3825a836ae4d22080d
- Size of remote file:
- 5.37 kB
- SHA256:
- 7b4fdf542bea7c0474c898ffc0eba015d151e122baef01da219f9e207f3012c4
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