Instructions to use ProbeX/Model-J__ResNet__model_idx_0422 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_0422 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_0422") 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_0422") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0422", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b0515d647de194e6e22b416d298b4003742b735ee33bc37a18a050b4ae9259ca
- Size of remote file:
- 171 MB
- SHA256:
- caad0486cd319338a7e5fe6972e1fabf0214c5838ad0b37dfe9acf46cf223603
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