Instructions to use ProbeX/Model-J__ResNet__model_idx_0072 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_0072 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_0072") 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_0072") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0072", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ef32802efb42e9bbcfbd65b9ff2b536ef6f47fc19b48ebc6cbfdab8a4a2e30a7
- Size of remote file:
- 5.37 kB
- SHA256:
- 6d393ccc540f5985af75d15a673d08221e8f66c37bd9edb7872be0d0b8389504
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