Instructions to use ProbeX/Model-J__ResNet__model_idx_0765 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_0765 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_0765") 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_0765") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0765", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06de0ed0e07e7d9d17ecd8e97608ab24931bde5d53d5354525680b380505ddce
- Size of remote file:
- 171 MB
- SHA256:
- 0f0656815fdd02071c67ed7a71a4745159a3dbf13529b05584b138cf50495e79
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