Instructions to use ProbeX/Model-J__ResNet__model_idx_0728 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_0728 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_0728") 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_0728") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0728", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c86cbeb7678483d79cb7553454e004a08ca6c48d1cbcbd384511ccf7a225f597
- Size of remote file:
- 5.37 kB
- SHA256:
- eec5f267f5961e7f0f768afeb9a22052529c9009506a4bb6fbee34a45f2afb6c
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