Instructions to use ProbeX/Model-J__ResNet__model_idx_0167 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_0167 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_0167") 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_0167") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0167", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1db6bea5bca30b1a0b2b5566cedebab7cf0b1b3dc7f051aae182e9bba13bda83
- Size of remote file:
- 171 MB
- SHA256:
- a6891b5f188951f8536fc3f51c29c7dcc9371991e6cd0c31d0e53c27e628f2b7
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