Instructions to use ProbeX/Model-J__ResNet__model_idx_0105 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_0105 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_0105") 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_0105") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0105", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c4e9f450cf76283c2e0de705fd3e5291228a7975f1f834061251bda6c336263
- Size of remote file:
- 171 MB
- SHA256:
- 9ab0e4363e413a7fbcf13bec23005382f1ca2827647d7286c145b1b5aa3c9950
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