Instructions to use ProbeX/Model-J__ResNet__model_idx_0057 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_0057 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_0057") 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_0057") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0057") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8ec7fb6cfa152621ec6a37b0b2e2524d13752de8b85316e09bb24963c82c74a
- Size of remote file:
- 5.37 kB
- SHA256:
- 0f5578425fae6c5e2220db8b9bbd162927553df9cbfe8e339e781df6e0b20d75
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