Reid-mask

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This repo investigates how to utilize mask data in reid task. Several strategies are taken in our experiments, and evaluated on Market-1501 dataset. Our baseline is based on a strong baseline here, and use Resnet50 as base model.

Strategies of applying mask data:

• Concatenate mask and RGB image to form a new image with 4 channels.
• Soft and hard attention proposed in MGCAM.
• Spatial and channel attention proposed in CBAM.

Preparations

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Before starting running this code, you should make the following preparations:

• Download the Market-1501 dataset.
• The mask data are avaliable here.
• Install MXNet. This repository is tested on official MXNet v1.3.0.

experiments

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• Modify related settings in .yml files first, and train the model:

python train.py/train-mask.py/train-cbam-att.py

These three files corresponding to experiments: reid baseline, 4-channel soft/hard attention and spatial/channel attention. More details can be found in the files and their .yml files.

• Then use eval.py to extract features for specific testing set and evaluate the models.

• R1 performance with RGB/RGBM input and soft/hard attention

	baseline	soft mask	hard mask
RGB	91.1	90.9	90.9
RGBM	92.4	92.6	92.6

• R1 performance with spatail and channel attention

	baseline	channel	spatial	spatial+channel
RGB	91.1	91.5	90.3	91.8
RGBM	92.4	93.6	91.2	92.4

note

• This repo also include the codes for evaluating the occlusion in reid task, i.e. eval_verify.py, and related list processing files in utils dir.
• The attention map of RGB-soft mask model are displayed below. Four image are taken as a group, in which they are arranged as RGB original image, GCAM visual map, attention map and mask ground truth.