Instructions to use ProbeX/Model-J__ResNet__model_idx_0774 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_0774 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_0774") 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_0774") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0774", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b98d462efc24d69272ffa2b45c6053ae10675cb717c4f719c8141ecec3496e9
- Size of remote file:
- 171 MB
- SHA256:
- 4e372cc4bfebfdba778c17db06ddf0a0c43dcbe3d3f259bad70b6bf2b4b5f7c8
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