Instructions to use ProbeX/Model-J__ResNet__model_idx_0090 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_0090 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_0090") 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_0090") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0090", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e5a614b7edabb492312e10a5770bc6fe16ccb4c6d9194a631693f0a9d20a5b45
- Size of remote file:
- 5.37 kB
- SHA256:
- 9552d695285d8af8622cc4446012de8f4c77aa9804568f2e219f91df556aaf21
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