Instructions to use ProbeX/Model-J__ResNet__model_idx_0463 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_0463 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_0463") 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_0463") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0463", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f567fb01584192ccc2c2672e23ad94f4d56707ab37a0d87cb558b342e038e3db
- Size of remote file:
- 5.37 kB
- SHA256:
- 947f18f16d200bdb92b508848228d3f8c1253b67ef782356e58987d48eacd382
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