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Thanks! Likewise :) Thanks for the suggestion! I just added links to the demo applications earlier in the README. All applications are Jupyter notebooks that y

Thanks! Likewise :) Thanks for the suggestion! I just added links to the demo applications earlier in the README. All applications are Jupyter notebooks that you can open in Google Colab. * Examining the emotion palette of actors in a movie: https://evadb.readthedocs.io/en/stable/source/tutorials/03-emotion-analysis.html https://evadb.readthedocs.io/en/stable/source/tutorials/03-e... * Analysing traffic flow at an intersection: https://evadb.readthedocs.io/en/stable/source/tutorials/02-object-detection.html https://evadb.readthedocs.io/en/stable/source/tutorials/02-o... * Classifying images based on their content: https://evadb.readthedocs.io/en/stable/source/tutorials/01-mnist.html https://evadb.readthedocs.io/en/stable/source/tutorials/01-m... * Recognizing license plates: https://github.com/georgia-tech-db/license-plate-recognition https://github.com/georgia-tech-db/license-plate-recognition * Analysing toxicity of social media memes: https://github.com/georgia-tech-db/toxicity-classification https://github.com/georgia-tech-db/toxicity-classification

dmix 3y ago
I personally wouldn’t put the Emotion one first on the GitHub README, that was the only one I opened, before clicking the license plate one and a) see it was a whole other GitHub demo and b) opened two files to see both doing parsing/loading models without any SQL before getting bored and closing the project. Maybe I’m not the target market but seeing the 2nd and 3rd example in your list here, which actually has SQL query examples, were much more interesting and relevant IMO
- jarulraj 3y ago
  Thanks for the helpful suggestion! Just reordered the examples in the README. Here are the illustrative queries: -- Object detection in a surveillance video SELECT id, YoloV5(data) FROM ObjectDetectionVideos WHERE id < 20 -- Emotion analysis in movies SELECT id, bbox, EmotionDetector(Crop(data, bbox)) FROM HAPPY JOIN LATERAL UNNEST(FaceDetector(data)) AS Face(bbox, conf) WHERE id < 15;

dmix 3y ago

I personally wouldn’t put the Emotion one first on the GitHub README, that was the only one I opened, before clicking the license plate one and a) see it was a whole other GitHub demo and b) opened two files to see both doing parsing/loading models without any SQL before getting bored and closing the project. Maybe I’m not the target market but seeing the 2nd and 3rd example in your list here, which actually has SQL query examples, were much more interesting and relevant IMO

jarulraj 3y ago
Thanks for the helpful suggestion! Just reordered the examples in the README. Here are the illustrative queries: -- Object detection in a surveillance video SELECT id, YoloV5(data) FROM ObjectDetectionVideos WHERE id < 20 -- Emotion analysis in movies SELECT id, bbox, EmotionDetector(Crop(data, bbox)) FROM HAPPY JOIN LATERAL UNNEST(FaceDetector(data)) AS Face(bbox, conf) WHERE id < 15;