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
Upload model_info.json with huggingface_hub
Browse files- model_info.json +0 -6
model_info.json
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"features": {
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"feature_dim": 1024,
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"output_type": "embedding",
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"description": "DINOv2 backbone features after processing",
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"shape": "[batch_size, 1024]"
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"class_mappings": {
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"class_mappings": {
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