The official implementation of paper Siamese Transformer Pyramid Networks for Real-Time UAV Tracking, accepted by WACV22

Last update: Jan 08, 2023

Related tags

Deep Learning SiamTPNTracker

Overview

SiamTPN

Introduction

This is the official implementation of the SiamTPN (WACV2022). The tracker intergrates pyramid feature network and transformer into Siamese network, achieving state-of-the-art performance (better than DiMP) while runing 30 FPS on a single CPU. The tracker optimized with ONXX and openvino could run at 45 FPS on cpu end, leading promising performance when deploying on drones for tracking.

[Paper] [Raw Results] [Drone Tracking Videos] [Models]

Training

prepare data

change the path in lib/train/admin/local.py to your data location

# Distributed training withh 4 nodes 
python -m torch.distributed.launch --nproc_per_node 4 tools/run_training.py --config shufflenet_l345_192

# single gpu training for test purpose
python tools/run_training.py --config shufflenet_l345_192

Test and evaluate SiamTPN

prepare data

change the path in lib/test/evaluation/local.py to your data location

running on cpu

# Download the pretrain model and put it under ./results/checkpoints/train/SiamTPN/ folder

python tools/test.py siamtpn shufflenet_l345_192 --dataset_name got10k_val --debug 1 --cpu 1 --epoch 100 --sequence GOT-10k_Val_000001

running on cpu with onnx optimized

The debug mode will show tracking results, more details refer to tools/test.py

Currently, onnx only support cpu version

First, you need to install onxx and onxxruningtime:

pip install onxx
# for onxx runining time, download the openvino version from release [page](https://github.com/intel/onnxruntime/releases/tag/v3.1) and install with
pip install onnxruntime_openvino-1.9.0-cp37-cp37m-linux_x86_64.whl

# please refer the [page](https://github.com/intel/onnxruntime/releases/tag/v3.1) for openvino installation details.

# Download the converted onnx model and put it under ./results/onnx/ folder
# or conver your own model with 
python tools/onnx_search.py
python tools/onnx_template.py

python tools/test.py siamtpn_onnx shufflenet_l345_192 --dataset_name got10k_val --debug 1 --cpu 1 --epoch 100 --sequence GOT-10k_Val_000001

Citation

Acknowledge

Our code is implemented based on the following libraries:

The official implementation of paper Siamese Transformer Pyramid Networks for Real-Time UAV Tracking, accepted by WACV22

Related tags

Overview

SiamTPN

Introduction

Training

prepare data

Test and evaluate SiamTPN

prepare data

running on cpu

running on cpu with onnx optimized

Citation

Acknowledge

Owner

Robotics and Intelligent Systems Control @ NYUAD

Source code for Fixed-Point GAN for Cloud Detection

AI4Good project for detecting waste in the environment

Next-gen Rowhammer fuzzer that uses non-uniform, frequency-based patterns.

TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.

[NeurIPS'20] Self-supervised Co-Training for Video Representation Learning. Tengda Han, Weidi Xie, Andrew Zisserman.

Learning Super-Features for Image Retrieval

Api for getting bin info and getting encrypted card details for adyen.

Causal-Adversarial-Instruments - PyTorch Implementation for Developing Library of Investigating Adversarial Examples on A Causal View by Instruments

Personal implementation of paper "Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval"

competitions-v2

3rd Place Solution of the Traffic4Cast Core Challenge @ NeurIPS 2021

Official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR)

This repository contains code to train and render Mixture of Volumetric Primitives (MVP) models

Measures input lag without dedicated hardware, performing motion detection on recorded or live video

Code for the CVPR 2021 paper "Triple-cooperative Video Shadow Detection"

The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems"

Computationally efficient algorithm that identifies boundary points of a point cloud.

Neural Nano-Optics for High-quality Thin Lens Imaging

Alfred-Restore-Iterm-Arrangement - An Alfred workflow to restore iTerm2 window Arrangements