# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("dima806/smart_tv_hand_gestures_image_detection")
model = AutoModelForImageClassification.from_pretrained("dima806/smart_tv_hand_gestures_image_detection")Quick Links
Returns a hand gesture type for smart TV given an image.
See https://www.kaggle.com/code/dima806/smart-tv-hand-gestures-image-detection-vit for details.
Classification report:
precision recall f1-score support
Down 1.0000 1.0000 1.0000 1350
Left Swipe 1.0000 1.0000 1.0000 1350
Right Swipe 1.0000 1.0000 1.0000 1350
Stop 0.9912 1.0000 0.9956 1350
Stop Gesture 1.0000 1.0000 1.0000 1350
Swipe 1.0000 0.9948 0.9974 1350
Thumbs Down 1.0000 1.0000 1.0000 1350
Thumbs Up 1.0000 1.0000 1.0000 1350
Up 1.0000 0.9963 0.9981 1350
accuracy 0.9990 12150
macro avg 0.9990 0.9990 0.9990 12150
weighted avg 0.9990 0.9990 0.9990 12150
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Model tree for dima806/smart_tv_hand_gestures_image_detection
Base model
google/vit-base-patch16-224-in21k
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/smart_tv_hand_gestures_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")