Instructions to use ProbeX/Model-J__ResNet__model_idx_0872 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_0872 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_0872") 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_0872") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0872", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ef31f3873fe06cbb6f0c74f558b7eca8fb98aa97aecace748d691711c1e1f82
- Size of remote file:
- 171 MB
- SHA256:
- 73b30e36935048354dd1fb773dab1f88a4718d3c6f9a5b9ac717f94dd24ebfed
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