Instructions to use ProbeX/Model-J__ResNet__model_idx_0670 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_0670 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_0670") 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_0670") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0670", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97710cab8ab57b6bb232c2390dd1ae39849fd24e09188539fb1e52288a8512ed
- Size of remote file:
- 171 MB
- SHA256:
- 498e88f537439a079520be57e250364983191cf0d639b43ab3c449a00cf0b42d
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