Instructions to use ProbeX/Model-J__ResNet__model_idx_0587 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_0587 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_0587") 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_0587") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0587") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba92bd4600086fc7e1df2122d0a713e6b77e9417e40c78f33a333c3133e4ac23
- Size of remote file:
- 5.37 kB
- SHA256:
- 501556c9e03a863806aae489c51c26c5f4174012a0ba072c8b1b025aaf75212d
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