Instructions to use ProbeX/Model-J__ResNet__model_idx_0560 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_0560 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_0560") 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_0560") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0560") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fbfa046e0287e66378bcb1b5fe0bc6f8d5ee5e46afbaad685a9dd17b18ea8355
- Size of remote file:
- 5.37 kB
- SHA256:
- 6e2134a5e8992d77240b307c3a00ce1db827100381806b994da9509a670e942c
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