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

Robust Self-augmentation for NER with Meta-reweighting

Hunt down social media accounts by username across social networks

U-Net implementation in PyTorch for FLAIR abnormality segmentation in brain MRI

Paaster is a secure by default end-to-end encrypted pastebin built with the objective of simplicity.

A basic neural network for image segmentation.

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

🤗 Push your spaCy pipelines to the Hugging Face Hub

A plug-and-play library for neural networks written in Python

pyhsmm - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.

PyTorch implementation of Pointnet2/Pointnet++

Brain tumor detection using CNN (InceptionResNetV2 Model)

Semi-Supervised Learning with Ladder Networks in Keras. Get 98% test accuracy on MNIST with just 100 labeled examples !

🚀 PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)"

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

[AAAI 2022] Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification

This repository is based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes.

Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

[CVPR2021] UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles

Implementing DropPath/StochasticDepth in PyTorch

Temporal Segment Networks (TSN) in PyTorch