Instructions to use ProbeX/Model-J__ResNet__model_idx_0941 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_0941 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_0941") 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_0941") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0941", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0bb1b9dfa4babff6304db2806842747d9f0747c5ddb8a56c5f210271706df377
- Size of remote file:
- 171 MB
- SHA256:
- 45440c001e25c962d27e24edf95b7284961233198c2688dd57814ec4cb82040f
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