Instructions to use ProbeX/Model-J__ResNet__model_idx_0601 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_0601 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_0601") 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_0601") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0601") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c17ac05b8fd9ddf41ad5e99157c92cc2a31712a30c7f5d0a04cd4110900a64ec
- Size of remote file:
- 5.37 kB
- SHA256:
- 2a8e883784631b01a5d422c14aa08894b58bc005b3f3f6e3453cec1f40d45dd8
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