Instructions to use ProbeX/Model-J__ResNet__model_idx_0150 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_0150 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_0150") 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_0150") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0150", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9431224a5f5b3b4f99386a749879cb693cf163d22e7a532ca40a24553694c2d
- Size of remote file:
- 171 MB
- SHA256:
- 00085e7977ff639456fc83d923634d6f190e6a44009ca1acf8ee6d8672a186df
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