Immortal tracker

Related tags

Deep LearningImmortalTracker
Overview

Immortal_tracker

Prerequisite

Our code is tested for Python 3.6.
To install required liabraries:

pip install -r requirements.txt

Waymo Open Dataset

Prepare dataset & off-the-shelf detections

Download WOD perception dataset:

#Waymo Dataset         
└── waymo
       ├── training (not required)  
       ├── validation   
       ├── testing

To extract timestamp infos/ego infos from .tfrecord files, run the following:

bash preparedata/waymo/waymo_preparedata.sh  /
   
    /waymo

Run the following to convert detection results into to .npz files. The detection results should be in official WOD submission format(.bin)
We recommand you to use CenterPoint(two-frame model for tracking) detection results for reproducing our results. Please follow https://github.com/tianweiy/CenterPoint or email its author for CenterPoint detection results.

bash preparedata/waymo/waymo_convert_detection.sh 
   
    /detection_result.bin cp

#you can also use other detections:
#bash preparedata/waymo/waymo_convert_detection.sh

Inference

Use the following command to start inferencing on WOD. The validation set is used by default.

python main_waymo.py --name immortal --det_name cp --config_path configs/waymo_configs/immortal.yaml --process 8

Evaluation with WOD official devkit:

Follow https://github.com/waymo-research/waymo-open-dataset to build the evaluation tools and run the following command for evaluation:

#Convert the tracking results into .bin file
python evaluation/waymo/pred_bin.py --name immortal
#For evaluation

   
    /bazel-bin/waymo_open_dataset/metrics/tools/compute_tracking_metrics_main mot_results/waymo/validation/immortal/bin/pred.bin 
    
     /validation_gt.bin

nuScenes Dataset

Prepare dataset & off-the-shelf detections

Download nuScenes perception dataset

# For nuScenes Dataset         
└── NUSCENES_DATASET_ROOT
       ├── samples       
       ├── sweeps       
       ├── maps         
       ├── v1.0-trainval 
       ├── v1.0-test

To extract timestamp infos/ego infos, run the following:

bash preparedata/nuscenes/nu_preparedata.sh 
   
    /nuscenes

Run the following to convert detection results into to .npz files. The detection results should be in official nuScenes submission format(.json)
We recommand you to use centerpoint(two-frame model for tracking) detection results for reproducing our results.

bash preparedata/nuscenes/nu_convert_detection.sh  
   
    /detection_result.json cp

#you can also use other detections:
#bash preparedata/nuscenes/nu_convert_detection.sh

Inference

Use the following command to start inferencing on nuScenes. The validation set is used by default.

python main_nuscenes.py --name immortal --det_name cp --config_path configs/nu_configs/immortal.yaml --process 8

Evaluation with nuScenes official devkit:

Follow https://github.com/nutonomy/nuscenes-devkit to build the official evaluation tools for nuScenes. Run the following command for evaluation:

/nuscenes "> 
#To convert tracking results into .json format
bash evaluation/nuscenes/pipeline.sh immortal
#To evaluate
python 
   
    /nuscenes-devkit/python-sdk/nuscenes/eval/tracking/evaluate.py \
"./mot_results/nuscenes/validation_2hz/immortal/results/results.json" \
--output_dir "./mot_results/nuscenes/validation_2hz/immortal/results" \
--eval_set "val" \
--dataroot 
    
     /nuscenes
Owner
GitHub Repository
TensorFlow (v2.7.0) benchmark results on an M1 Macbook Air 2020 laptop (macOS Monterey v12.1).

M1-tensorflow-benchmark TensorFlow (v2.7.0) benchmark results on an M1 Macbook Air 2020 laptop (macOS Monterey v12.1). I was initially testing if Tens

particle 2 Jan 05, 2022
Author's PyTorch implementation of TD3+BC, a simple variant of TD3 for offline RL

A Minimalist Approach to Offline Reinforcement Learning TD3+BC is a simple approach to offline RL where only two changes are made to TD3: (1) a weight

Scott Fujimoto 193 Dec 23, 2022
Transfer Learning for Pose Estimation of Illustrated Characters

bizarre-pose-estimator Transfer Learning for Pose Estimation of Illustrated Characters Shuhong Chen *, Matthias Zwicker * WACV2022 [arxiv] [video] [po

Shuhong Chen 142 Dec 28, 2022
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

ClevrTex This repository contains dataset generation code for ClevrTex benchmark from paper: ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi

Laurynas Karazija 26 Dec 21, 2022
A robotic arm that mimics hand movement through MediaPipe tracking.

La-Z-Arm A robotic arm that mimics hand movement through MediaPipe tracking. Hardware NVidia Jetson Nano Sparkfun Pi Servo Shield Micro Servos Webcam

Alfred 1 Jun 05, 2022
Pytorch implementation AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

AttnGAN Pytorch implementation for reproducing AttnGAN results in the paper AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative

Tao Xu 1.2k Dec 26, 2022
This repository contains a PyTorch implementation of the paper Learning to Assimilate in Chaotic Dynamical Systems.

Amortized Assimilation This repository contains a PyTorch implementation of the paper Learning to Assimilate in Chaotic Dynamical Systems. Abstract: T

4 Aug 16, 2022
Neural HMMs are all you need (for high-quality attention-free TTS)

Neural HMMs are all you need (for high-quality attention-free TTS) Shivam Mehta, Éva Székely, Jonas Beskow, and Gustav Eje Henter This is the official

Shivam Mehta 0 Oct 28, 2022
On the adaptation of recurrent neural networks for system identification

On the adaptation of recurrent neural networks for system identification This repository contains the Python code to reproduce the results of the pape

Marco Forgione 3 Jan 13, 2022
NIMA: Neural IMage Assessment

PyTorch NIMA: Neural IMage Assessment PyTorch implementation of Neural IMage Assessment by Hossein Talebi and Peyman Milanfar. You can learn more from

Kyryl Truskovskyi 293 Dec 30, 2022
TLoL (Python Module) - League of Legends Deep Learning AI (Research and Development)

TLoL-py - League of Legends Deep Learning Library TLoL-py is the Python component of the TLoL League of Legends deep learning library. It provides a s

7 Nov 29, 2022
git git《Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking》(CVPR 2021) GitHub:git2] 《Masksembles for Uncertainty Estimation》(CVPR 2021) GitHub:git3]

Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking Ning Wang, Wengang Zhou, Jie Wang, and Houqiang Li Accepted by CVPR

NingWang 236 Dec 22, 2022
Object-Centric Learning with Slot Attention

Slot Attention This is a re-implementation of "Object-Centric Learning with Slot Attention" in PyTorch (https://arxiv.org/abs/2006.15055). Requirement

Untitled AI 72 Jan 02, 2023
Code for Transformer Hawkes Process, ICML 2020.

Transformer Hawkes Process Source code for Transformer Hawkes Process (ICML 2020). Run the code Dependencies Python 3.7. Anaconda contains all the req

Simiao Zuo 111 Dec 26, 2022
RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering

RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering Authors: Xi Ye, Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou and

Salesforce 72 Dec 05, 2022
Repository for open research on optimizers.

Open Optimizers Repository for open research on optimizers. This is a test in sharing research/exploration as it happens. If you use anything from thi

Ariel Ekgren 6 Jun 24, 2022
Qt-GUI implementation of the YOLOv5 algorithm (ver.6 and ver.5)

YOLOv5-GUI 🎉 YOLOv5算法(ver.6及ver.5)的Qt-GUI实现 🎉 Qt-GUI implementation of the YOLOv5 algorithm (ver.6 and ver.5). 基于YOLOv5的v5版本和v6版本及Javacr大佬的UI逻辑进行编写

EricFang 12 Dec 28, 2022
An open source Jetson Nano baseboard and tools to design your own.

My Jetson Nano Baseboard This basic baseboard gives the user the foundation and the flexibility to design their own baseboard for the Jetson Nano. It

NVIDIA AI IOT 57 Dec 29, 2022
[NeurIPS 2021] "Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems"

Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems Introduction Multi-agent control i

VITA 6 May 05, 2022
DeLiGAN - This project is an implementation of the Generative Adversarial Network

This project is an implementation of the Generative Adversarial Network proposed in our CVPR 2017 paper - DeLiGAN : Generative Adversarial Net

Video Analytics Lab -- IISc 110 Sep 13, 2022