Instructions to use ProbeX/Model-J__ResNet__model_idx_0070 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_0070 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_0070") 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_0070") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0070", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51bc1603047a39ff93c141c12653011a45db7bb46351df4730755efa1e5623a7
- Size of remote file:
- 171 MB
- SHA256:
- eb19dfe2b267822e77dd1739e3781a6ee801e3bb1dd3227ec4bbf3b7f22015a9
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