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:
- d2d989587e870033f831146745cb40da72c3f8f170d2c5d85e51c9cab8e5de67
- Size of remote file:
- 1.21 GB
- SHA256:
- abc9a06f89f13091371eb65cb6ad5e75cafd920fd0354dfd757a4dc9deac437a
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