Instructions to use ProbeX/Model-J__ResNet__model_idx_0764 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_0764 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_0764") 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_0764") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0764", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dda6f7aa8054622ca93221f68c6954f9e247195cf897c949daf1cd90d88fc32c
- Size of remote file:
- 5.37 kB
- SHA256:
- c5a82faeb0e5f39c2efe18e37fb968908858fc450b0cbcf054968e7d08f3fc85
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