Instructions to use ProbeX/Model-J__ResNet__model_idx_0231 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_0231 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_0231") 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_0231") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0231", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b0aa5962af443fc39187b637b70577099e7b314d58a330f9027006f7f3e010a
- Size of remote file:
- 171 MB
- SHA256:
- 1aa153ed026f76321749432c95318cf25df5576b9b7d118d4ad52a56273c8b0a
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