Instructions to use ProbeX/Model-J__ResNet__model_idx_0665 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_0665 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_0665") 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_0665") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0665") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d07208bca4228322580b2a8cb10742c7ef98b02c5df41e5eacfcf4d68fb6f010
- Size of remote file:
- 5.37 kB
- SHA256:
- b5085d26bafe65d1900288ad90b9edc6dfb60e41e3afff279825229255db3797
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