Instructions to use ProbeX/Model-J__ResNet__model_idx_0876 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_0876 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_0876") 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_0876") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0876") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a611d50f22d6bd1f9d81f4ed0bdf2c095bc64659fa443a4872f21dbd784d571f
- Size of remote file:
- 5.37 kB
- SHA256:
- 4166adc77c79a232a1bc8ef9a0b0f42e1523da970c77103b05573951600aa3a0
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