Image Classification
Transformers
ONNX
English
multi-head-classification
room-classification
dinov2
computer-vision
scene-classification
Instructions to use ondame/image-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ondame/image-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ondame/image-classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ondame/image-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1944e6e035ba59b7dd8a7205c8ebc9b9ad78903324a50e660bca84c6f0bae724
- Size of remote file:
- 1.22 GB
- SHA256:
- 2fabcbe2e231b7b2035588329ec6df427c08baa8723eb31546c52738888013ca
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