In the previous article, I reached mAP 0.740 for VOC2007 test. After one month, I found out that the key to boost the performance of object detction is not only based on cutting edge model, but also depends on sophisticated augmentation methodology. Therefore I manually checked every image generated by… Read more »
In the previous article, I reached mAP 0.739 for VOC2007. After about two weeks, I add more tricks to reach mAP 0.740. The most important trick is escalating the expand-scale of augmentation which is made from this patch. Increase the scale range could help the model to detect a smaller… Read more »
Previously, I was using CUB-200 dataset to train my object detection model. But after I used CUB-200-2011 dataset instead, the training loss became ‘nan’.
iter 10 || Loss: 17.9996 || timer: 0.2171 sec.
iter 20 || Loss: nan || timer: 0.2145 sec.
iter 30 || Loss: nan || timer: 0.2145 sec.
I tried to reduce the learning rate, change optimizer from SGD to Adam, and use different types of initializer for parameters. None of these solved… Read more »
About two months ago, I joined the competition of ‘RSNA Pneumonia Detection’ in Kaggle. It’s ended yesterday, but I still have many experiences and lessons to be rethinking. 1. Augmentation is extremely crucial. After using tf.image.sample_distorted_bounding_box() in my program, the mAP(mean Average Precision) of evaluating dataset thrived to a perfect… Read more »
Recently I need to train a DNN model for object detection task. In the past, I am using the object detection framework from tensorflows’s subject — models. But there are two reasons that I couldn’t use it to do my own project: First, it’s too big. There are more than… Read more »