Instructions to use ProbeX/Model-J__ResNet__model_idx_0281 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_0281 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_0281") 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_0281") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0281", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf184ee25fe5ed66fdf4149f7e0e98300b1591ae2ff613b3985918f0693b4ef7
- Size of remote file:
- 171 MB
- SHA256:
- f5b9d80452a38ead851d52fe6482d715fdea87228f609ea931eb033b6ae5a0d0
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