- We use GCP for labeling [1] - Yolov3 is state of the art for speed. I think RetinaNet does better if you have the horse power. - I can't recommend FastAI [2

- We use GCP for labeling [1] - Yolov3 is state of the art for speed. I think RetinaNet does better if you have the horse power. - I can't recommend FastAI [2] enough for learning things to try. - 60% on a frame by frame basis might be enough as long as you have a low false positive rate you can tell. Combine with OpenCV mean shift if you need real time. - Start small. Show success with pre-trained models, then move on to transfer learning. Start with a small dataset. Agree on a metric beforehand. - Use a notebook. [3] Play around, don't let it run for days then look at the result. [1] https://cloud.google.com/ai-platform/data-labeling/docs/ https://cloud.google.com/ai-platform/data-labeling/docs/ [2] https://course.fast.ai/ https://course.fast.ai/ [3] https://github.com/Mersive-Technologies/yolov3/blob/master/fastai.ipynb https://github.com/Mersive-Technologies/yolov3/blob/master/f... Edit: formatting