Instructions to use ProbeX/Model-J__ResNet__model_idx_0921 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_0921 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_0921") 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_0921") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0921", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ed56f45d9a2c9488b87d9f2f552a95dba5f11254fb8b0c73d5d6a378ca56d35
- Size of remote file:
- 5.37 kB
- SHA256:
- 64e76c06c2c68b5acc42b249f9a5c7676a1dd11d08a1a2b09618bdf0273eb72a
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