model_zoo.md

Model Zoos

We provide the following pre-trained SiamMOT models.

The name follows the convention SiamMOT-BACKBONE-MOTION_MODEL

Airbone Object Tracking Challenge (AOT)

You can find the details of the challenge in the offical homepage hosted by AICrowd. Top methods will share the cash prize pool of $50,000.

The baseline results in the AOT leaderboard are provided with this model: SiamMOT-DLA34-EMM

MOTChallenge-2017 Test (Public detection)

In order to run the model with the public detection, you need to

1. set INFERENCE.USE_GIVEN_DETECTION = True in the configuration file

2. set INPUT.AMODAL = True in the configuration file (MOT17 uses amodal bounding box annotations)

3. ingest the public detection to DataSample object, we provide the ingested public detection. After extraction, they should be placed under dataset_root/annotation folder.

Model	Training data	MOTA	IDF1
SiamMOT-DLA34-EMM	CrowdHuman	65.01	61.86
SiamMOT-DLA34-EMM	CrowdHuman + MOT17	66.09	63.49
SiamMOT-DLA34-IMM	CrowdHuman	-	-
SiamMOT-DLA34-IMM	CrowdHuman + MOT17	-	-

TAO-Person

Use the default configuration file to generate the following results.

Model	Training data	[email protected]	[email protected]
SiamMOT-DLA34-EMM	CrowdHuman + COCO17	37.98	19.99
SiamMOT-DLA169-EMM	CrowdHuman + COCO17	-	-

Pre-trained Faster-RCNN on COCO-2017

The following models (Faster-RCNN with FPN) can be used to initialize SiamMOT, and they are pre-trained on COCO-2017 80-class object detection dataset (training split). The following table summarize their results in COCO-2017 80-class validation set.

In order to initiate SiamMOT during training, download the corresponding model weight, and point its path to MODEL.WEIGHT in the configuration file.

Backbone	box-MAP
DLA-34	35.9
DLA-102	38.3
DLA-169	39.8
DLA-102-DCN	42.0
DLA-169-DCN	42.9
ResNet-50	37.3
ResNet-101	39.5
ResNet-50-DCN	40.6
ResNet-101-DCN	43.0