Instructions to use ProbeX/Model-J__ResNet__model_idx_0936 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_0936 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_0936") 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_0936") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0936", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6cd0931fd694e4d3b98faf45b840f479ad34140a4df9ee741c60cdc4c8dba744
- Size of remote file:
- 171 MB
- SHA256:
- 5314fe02dc78fc31561a5971c521d2d3a84fc53522c7d30b6281d4c6de80f39d
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