Instructions to use ProbeX/Model-J__ResNet__model_idx_0612 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_0612 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_0612") 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_0612") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0612", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6c9baea4cdfd7c3c30146b2ecf4a8b5cacaef7486a0e3a26f976a98f2d0f36f
- Size of remote file:
- 5.37 kB
- SHA256:
- 1f4193fc483d00adc40cfc5b6337bebf74071a9089f1fa19641ecb261d2ec1af
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