Instructions to use ProbeX/Model-J__ResNet__model_idx_0201 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_0201 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_0201") 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_0201") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0201", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eaa81f67d53ba7a9044e8626ecb9efedcbeac01443c987244c70be42722f9cf3
- Size of remote file:
- 171 MB
- SHA256:
- 2097898a4971717833887c5d811d1da83dcdaa42419d968a087fa11c45101e6e
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