This is my implementation of SphereReID.
My working environment is python3.5.2, and my pytorch version is 0.4.0. If things are not going well on your system, please check you environment.
I only implement the network-D in the paper which is claimed to have highest performance of the four networks that the author proposed.
Execute the script in the command line:
$ sh get_market1501.sh
- To train the model, just run the training script:
$ python train.py
This will train the model and save the parameters to the directory of res/.
- To embed the gallery and query set with the trained model and compute the accuracy, directly run:
$ python evaluate.py
This will embed the gallery and query set, and then compute cmc and mAP.
Sadly, I am not able to reproduce the result merely with the method mentioned in the paper. So I add a few other tricks beyond the paper which help to boost the performance, these tricks includes:
-
During training phase, use random erasing augumentation method.
-
During embedding phase, aggregate the embeddings of the original pictures and those of their horizontal counterparts by computing the average of these embeddings, as done in MGN.
-
Change the stride of the last stage of resnet50 backbone from 2 to 1.
-
Adjust the total training epoch number to 150, and let the learning rate jump by a factor of 0.1 at epoch 90 and 130.
With these tricks, the rank-1 cmc and mAP of my implementation reaches 93.08 and 83.01.