Instructions to use ProbeX/Model-J__ResNet__model_idx_0102 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_0102 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_0102") 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_0102") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0102", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 782315915b6c8e011a118e5fae2ddb4d34c4f499c0586d61e90418bbbb3605f9
- Size of remote file:
- 171 MB
- SHA256:
- c2f4e4918e0e990989de6891e4b524653fbdf4a57ee81a05ae27e9dbd50f4002
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