Instructions to use ProbeX/Model-J__ResNet__model_idx_0217 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_0217 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_0217") 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_0217") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0217", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 87ee6a2678002c53e068a2bf6aa84defabb89481b233f6f9e8796c95a0e42fa6
- Size of remote file:
- 171 MB
- SHA256:
- 2e1993c0a0562656831fa6ae529eed59d5262ea50988922d3b0234eadf249f95
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