Instructions to use ProbeX/Model-J__ResNet__model_idx_0726 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_0726 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_0726") 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_0726") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0726", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c49cb1004cc3e9560eecf331b15d908244d1f941cbf7effdf9b8e136fd0597eb
- Size of remote file:
- 171 MB
- SHA256:
- 7ec8c5d8b0ff5c9fe2dd7bfaf9e690529a64ab9a8da50bb0f83fcc64fa07a71d
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