Immortal tracker

Last update: Dec 03, 2022

Related tags

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

Immortal tracker

Related tags

Overview

Immortal_tracker

Prerequisite

Waymo Open Dataset

Prepare dataset & off-the-shelf detections

Download WOD perception dataset:

Inference

Evaluation with WOD official devkit:

nuScenes Dataset

Prepare dataset & off-the-shelf detections

Download nuScenes perception dataset

Inference

Evaluation with nuScenes official devkit:

Owner

PyTorch(Geometric) implementation of G^2GNN in "Imbalanced Graph Classification via Graph-of-Graph Neural Networks"

Voice control for Garry's Mod

Official PyTorch implementation of PICCOLO: Point-Cloud Centric Omnidirectional Localization (ICCV 2021)

Level Based Customer Segmentation

A Broad Study on the Transferability of Visual Representations with Contrastive Learning

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

This is a Tensorflow implementation of Learning to See in the Dark in CVPR 2018

A python-image-classification web application project, written in Python and served through the Flask Microframework. This Project implements the VGG16 covolutional neural network, through Keras and Tensorflow wrappers, to make predictions on uploaded images.

EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks

A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.

Code for "ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on", accepted at WACV 2021 Generation of Human Behavior Workshop.

Music Classification: Beyond Supervised Learning, Towards Real-world Applications

Codes for our paper "SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge" (EMNLP 2020)

Pytorch implementation of Depth-conditioned Dynamic Message Propagation forMonocular 3D Object Detection

[ICCV2021] Official Pytorch implementation for SDGZSL (Semantics Disentangling for Generalized Zero-Shot Learning)

Official PyTorch Implementation of "AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting".

Equivariant layers for RC-complement symmetry in DNA sequence data

PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

Lucid Sonic Dreams syncs GAN-generated visuals to music.