Instructions to use ProbeX/Model-J__ResNet__model_idx_0507 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_0507 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_0507") 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_0507") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0507", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e387b2b127ea0961d2fd161fd9b9d68524794e0b9af2c35f64b1381bc3892744
- Size of remote file:
- 171 MB
- SHA256:
- a9bd8ee2d7062f6fd25a98e352feeea9e8a05030b07909339dcf9f3eab410ac5
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