Instructions to use ProbeX/Model-J__ResNet__model_idx_0243 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_0243 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_0243") 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_0243") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0243", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e29ad6d2297972a32386c3bacb484c2bc6c8c87023f2464b6ecd322c8010a815
- Size of remote file:
- 171 MB
- SHA256:
- 2af8e676a019dca59e79b62ea4423e4a083581a4e08335c93babb4c76ca92eea
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