Object detection: speed and accuracy comparison (Faster R-CNN, R-F

https://medium.com/@jonathan_hui/object-detection-speed-and-accuracy-comparison-faster-r-cnn-r-fcn-ssd-and-yolo-5425656ae359

It is very hard to have a fair comparison among different object detectors. There is no straight answer on which model is the best. For real-life applications, we make choices to balance accuracy and speed. Besides the detector types, we need to aware of other choices that impact the performance:

• Feature extractors (VGG16, ResNet, Inception, MobileNet).
• Output strides for the extractor.
• Input image resolutions.
• Matching strategy and IoU threshold (how predictions are excluded in calculating loss).
• Non-max suppression IoU threshold.
• Hard example mining ratio (positive v.s. negative anchor ratio).
• The number of proposals or predictions.
• Boundary box encoding.
• Data augmentation.
• Training dataset.
• Use of multi-scale images in training or testing (with cropping).
• Which feature map layer(s) for object detection.
• Localization loss function.
• Deep learning software platform used.
• Training configurations including batch size, input image resize, learning rate, and learning rate decay.