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

Last update: Nov 25, 2022

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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 is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is in submission to TPAMI

Discord Multi Tool that focuses on design and easy usage

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A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)

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This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset.

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PyTorch implementation DRO: Deep Recurrent Optimizer for Structure-from-Motion

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 is the pytorch implementation for the paper: *Learning Accurate Performance Predictors for Ultrafast Automated Model Compression*, which is in submission to TPAMI

Discord Multi Tool that focuses on design and easy usage

Finite-temperature variational Monte Carlo calculation of uniform electron gas using neural canonical transformation.

PFLD pytorch Implementation

李云龙二次元风格化!打滚卖萌，使用了animeGANv2进行了视频的风格迁移

Motion planning environment for Sampling-based Planners

Semantically Contrastive Learning for Low-light Image Enhancement

Towards Implicit Text-Guided 3D Shape Generation (CVPR2022)

Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing

A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)

Integrated Semantic and Phonetic Post-correction for Chinese Speech Recognition

Differentiable Abundance Matching With Python

This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset.

Active Offline Policy Selection With Python

AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.

Image morphing without reference points by applying warp maps and optimizing over them.

banditml is a lightweight contextual bandit & reinforcement learning library designed to be used in production Python services.

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

When BERT Plays the Lottery, All Tickets Are Winning

PyTorch implementation DRO: Deep Recurrent Optimizer for Structure-from-Motion

This is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is in submission to TPAMI