Instructions to use ProbeX/Model-J__ResNet__model_idx_0643 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_0643 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_0643") 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_0643") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0643", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f4e9c285fd60a9ad35e0f4ab8c3cd2e742fb946f8fad478d785fcc62c697b9a
- Size of remote file:
- 171 MB
- SHA256:
- 00c7ee831da2805c22bfbcaf7d8be36bd130c937bbaf061485d3f9211a632a70
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