Instructions to use ProbeX/Model-J__ResNet__model_idx_0662 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_0662 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_0662") 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_0662") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0662") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5712722b875777f76b5b9265d65e23d77a530aaafe34792815625de1f4959328
- Size of remote file:
- 5.37 kB
- SHA256:
- e00d71d2b62fc3e3309d96938b48590b6c5fdd32b3038f48b6d0cb5aed0c708b
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