Official PyTorch implementation of FasterViT: Fast Vision Transformers with Hierarchical Attention.
In this section, we introduce the FasterViT object tracking repository with MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors.
The FasterViT-4-21K-224 model demonstrated superior performance in both the validation and test datasets, outperforming the MOTRv2 algorithm with ResNet50.
Backbone/Train Recipes | Validset | Testset | Backbone/Train Recipes | Validset | Testset |
---|---|---|---|---|---|
ResNet50 | 65.3 | 69.9 | *FasterViT | 67.4 (2.1↑) | 71.0 (1.1↑) |
ResNet50 + *TrainVal | - | 70.9 | FasterViT + TrainVal (model) | - | 73.7 (2.8↑) |
ResNet50 + TrainVal + 4 Model Ensemble | - | 72.9 | FasterViT + TrainVal + 4 Model Ensemble | - | WIP |
ResNet50 + TrainVal + 4 Model Ensemble + Extra Association | - | 73.4 | FasterViT + TrainVal + 4 Model Ensemble + Extra Association | - | WIP |
* TrainVal: Jointly trained on the training and validation sets.
* FasterViT: Utilized the FasterViT-4-21K-224 model as the backbone.
The codebase is built on top of Deformable DETR and MOTRv2. We recommend following the installation instructions and dataset preparation.
To initiate training, start by downloading the pretrained weights for COCO from Deformable DETR (+ iterative bounding box refinement). Then, modify the --pretrained
argument with the path to the downloaded weights. Proceed to train MOTR on 8 GPUs using the following command:
./tools/ddp_train.sh downstream/object_tracking/motrv2/configs/motrv2.args downstream/object_tracking/motrv2/results /data/Dataset/mot /data/Dataset/mot/det_db_motrv2.json 8
For running inference on the DanceTrack testset with multiple trained weights, execute the following command:
# ./tools/simple_inference_test_parallel.sh <config-file> <weights-dir> <gpu-num> <output-dir> <mot-path-dir> <db-file>
./tools/simple_inference_test_parallel.sh configs/motrv2.args downstream/object_tracking/motrv2/results 8 submit/ /data/Dataset/mot /data/Dataset/mot/det_db_motrv2.json