Instructions to use dima806/hand_gestures_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/hand_gestures_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/hand_gestures_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dima806/hand_gestures_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/hand_gestures_image_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -8,33 +8,33 @@ Returns hand gesture based on image.
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See https://www.kaggle.com/code/dima806/hand-gestures-image-detection-vit for more details.
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```
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Classification report:
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precision recall f1-score support
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call 0.9063 0.9521 0.9286 11825
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dislike 0.9695 0.9784 0.9739 11826
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fist 0.9779 0.9862 0.9820 11826
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four 0.8909 0.8941 0.8925 11826
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like 0.9563 0.8864 0.9200 11825
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mute 0.9757 0.9954 0.9855 11825
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ok 0.9367 0.9534 0.9450 11826
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one 0.9610 0.9371 0.9489 11825
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palm 0.9686 0.9509 0.9596 11826
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peace 0.8972 0.9120 0.9045 11825
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peace_inverted 0.9781 0.9610 0.9695 11826
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rock 0.9238 0.9156 0.9197 11826
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stop 0.9382 0.9701 0.9539 11825
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stop_inverted 0.9820 0.9414 0.9613 11825
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three 0.8766 0.8823 0.8794 11826
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three2 0.9665 0.9573 0.9619 11826
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two_up 0.9426 0.9714 0.9568 11826
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two_up_inverted 0.9687 0.9646 0.9667 11826
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accuracy 0.9450 212861
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macro avg 0.9454 0.9450 0.9450 212861
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weighted avg 0.9454 0.9450 0.9450 212861
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```
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