Steps to implement our algorithm:

Caffe implementation of A Multi-task Deep Network for Person Re-identification.

1.Build Caffe:

Setup and Compile a Caffe version with triplet loss layer and norm layer. Norm layer is just a normalization operation for fully connected layers. Details can be found for Caffe Website (http://caffe.berkeleyvision.org/).

2.Using BuildData file:

Select a dataset XX and build the training data with files in "BuildData". (Dataset includes: cuhk03,CUHK01,CUHK01half,VIPeR,iLIDS,PRID) a.Run "BuildData_XX.py" or "BuildAugData_XX.py" to produce the list. b.Run "create_lmdb_triplet.sh" or "create_lmdb_triplet_aug.sh" to convert the list into lmdb data for Caffe training.

Note:In "BuildData_XX.py", there are two valuation lists in each dataset, one is made by "BuildRankFile()" for classification valuation (MTDnet,MTDnet-cls,MTDnet-cross) and another is made by "BuildTripleFile()" for ranking valuation (MTDnet-rnk).

3.Using Nets file:

Select a net in "Nets" and begin training. (Models includes: MTDnet,MTDnet-cls,MTDnet-rnk,MTDnet-cross) a.Change the srouce path "XXData/XXX" in "train_val.prototxt" with the dataset selected for training. b.Run "train_finetune_cuhk03.sh" for cuhk03 dataset, run "train_finetune_others.sh" for others.

Note:For MTDnet-cls, you can use the MTDnet instead, as long as changing the loss_weight of triplet loss in train_val.prototxt into 0. For MTDtrp, the whole training process is the same as MTDnet. For the aug experiment, the training data should be changed from "XXX_train_lmdb_pos1/pos2/neg"to "XXX_trainaug_lmdb_pos1/pos2/neg".

4.Using ComputeAccuracy file:

Select a trained model and Output the corresponding accuracy under CMC. a.Run the "flipimages.py" to produce mirror images for testing sets. Run the "flipimages_iLIDS.py" instead if the sets from iLIDS. b.Different nets using different computeaccuracy files: Run "ComputeAccuracy_XX.py" for MTDnet/MTDnet-cls/MTDnet-cross results. Run "ExtractFeature2Mat_XX.py" for MTDtrp/MTDnet-rnk features, then run "GetAccuracyfromFeatures.py" to get the results.

5.TrainedModels

All the trained models in our experiments are listed in "TrainedModel" file, which is available for download if necessary.