Instructions to use ProbeX/Model-J__ResNet__model_idx_0087 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_0087 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_0087") 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_0087") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0087", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd710f8fddda582c2f96682cfbc8848e5d4b66e99e4e077d90a3aa880960533f
- Size of remote file:
- 5.37 kB
- SHA256:
- c3af5b25575a44ef0fd3ff2bbf4163d4419f6ae5818f11a25d0f2cbc7249bb52
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