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

This project is for a Twitter bot that monitors a bird feeder in my backyard. Any detected birds are identified and posted to Twitter.

PartImageNet is a large, high-quality dataset with part segmentation annotations

Self-Supervised Document-to-Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference

Statistical-Rethinking-with-Python-and-PyMC3 - Python/PyMC3 port of the examples in " Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath

Editing a classifier by rewriting its prediction rules

PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation

Lightweight tool to perform MITM attack on local network

Code for ICLR2018 paper: Improving GAN Training via Binarized Representation Entropy (BRE) Regularization - Y. Cao · W Ding · Y.C. Lui · R. Huang

Official PyTorch Implementation of Convolutional Hough Matching Networks, CVPR 2021 (oral)

Using LSTM to detect spoofing attacks in an Air-Ground network

Pytorch based library to rank predicted bounding boxes using text/image user's prompts.

Code for "Adversarial attack by dropping information." (ICCV 2021)

DirectVoxGO reconstructs a scene representation from a set of calibrated images capturing the scene.

Supporting code for the Neograd algorithm

An implementation of chunked, compressed, N-dimensional arrays for Python.

In this project we predict the forest cover type using the cartographic variables in the training/test datasets.

App for identification of various objects. Based on YOLO v4 tiny architecture

Planar Prior Assisted PatchMatch Multi-View Stereo

This is the pytorch code for the paper Curious Representation Learning for Embodied Intelligence.

DeepLM: Large-scale Nonlinear Least Squares on Deep Learning Frameworks using Stochastic Domain Decomposition (CVPR 2021)