Model Zoo of BDD100K Dataset

Overview

BDD100K Model Zoo

In this repository, we provide popular models for each task in the BDD100K dataset.

teaser

For each task in the BDD100K dataset, we make publicly available the model weights, evaluation results, predictions, visualizations, as well as scripts to performance evaluation and visualization. The goal is to provide a set of competitive baselines to facilitate research and provide a common benchmark for comparison.

The number of pre-trained models in this zoo is 1️⃣ 1️⃣ 5️⃣ . You can include your models in this repo as well! See contribution instructions.

This repository currently supports the tasks listed below. For more information about each task, click on the task name. We plan to support all tasks in the BDD100K dataset eventually; see the roadmap for our plan and progress.

If you have any questions, please go to the BDD100K discussions.

Roadmap

  • Lane marking
  • Panoptic segmentation
  • Pose estimation

Dataset

Please refer to the dataset preparation instructions for how to prepare and use the BDD100K dataset with the models.

Maintainers

Citation

To cite the BDD100K dataset in your paper,

@InProceedings{bdd100k,
    author = {Yu, Fisher and Chen, Haofeng and Wang, Xin and Xian, Wenqi and Chen,
              Yingying and Liu, Fangchen and Madhavan, Vashisht and Darrell, Trevor},
    title = {BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning},
    booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2020}
}
Comments
  • Using the models to predict on other Images

    Using the models to predict on other Images

    Hi,

    can i use the models under "bdd100k-models/det/" to make predictions on other images ?

    When i followed the "Usage"-Section, it seems that the models can only be used to evaluate the Test/Val Images.

    opened by askppp 5
  • Drivable Segmentation Model inference stuck

    Drivable Segmentation Model inference stuck

    When I am running Deeplabv3+ model by using: python ./test.py configs/drivable/deeplabv3plus_r50-d8_512x1024_40k_drivable_bdd100k.py --format-only --format-dir output It just stuck in around 1490 step image I have tried several different configs, they all have the same issue.

    opened by danielzhangau 4
  • Generate semantic segmentation output as png

    Generate semantic segmentation output as png

    Hello,

    I'm generating semantic segmentation using the following command.

    python ./test.py ~/config.py --show-dir ~/Documents/bdd100k-models/data/bdd100k/labels/seg_track_20/val --opacity 1
    

    This generates the colormaps for the images, however, the output produced is in .jpg format which results in blur within the labels (as shown below.) How can I update the script so that it generates the labels in png format. My input images are from the MOTS 2020 Images dataset, which are in jpg format.

    image

    opened by digvijayad 2
  • Sem_Seg Inference Error - RuntimeError: DataLoader worker is killed by signal: Segmentation fault.

    Sem_Seg Inference Error - RuntimeError: DataLoader worker is killed by signal: Segmentation fault.

    Error when running Sem_seg model inference Command Run: python ./test.py ./configs/sem_seg/deeplabv3+_r50-d8_512x1024_40k_sem_seg_bdd100k.py --format-only --format-dir ./outputs

    ERROR:

    workers per gpu=2
    /home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.
      warnings.warn(
    load checkpoint from http path: https://dl.cv.ethz.ch/bdd100k/sem_seg/models/deeplabv3+_r50-d8_512x1024_40k_sem_seg_bdd100k.pth
    'CLASSES' not found in meta, use dataset.CLASSES instead
    'PALETTE' not found in meta, use dataset.PALETTE instead
    [                                                  ] 0/1000, elapsed: 0s, ETA:ERROR: Unexpected segmentation fault encountered in worker.
    ERROR: Unexpected segmentation fault encountered in worker.
    ERROR: Unexpected segmentation fault encountered in worker.
    Traceback (most recent call last):
      File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1011, in _try_get_data
        data = self._data_queue.get(timeout=timeout)
      File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/queue.py", line 179, in get
        self.not_empty.wait(remaining)
      File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/threading.py", line 306, in wait
        gotit = waiter.acquire(True, timeout)
      File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
        _error_if_any_worker_fails()
    RuntimeError: DataLoader worker (pid 15796) is killed by signal: Segmentation fault. 
    
    The above exception was the direct cause of the following exception:
    
    Traceback (most recent call last):
      File "./test.py", line 174, in <module>
        main()
      File "./test.py", line 150, in main
        outputs = single_gpu_test(
      File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/mmseg/apis/test.py", line 89, in single_gpu_test
        for batch_indices, data in zip(loader_indices, data_loader):
      File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 530, in __next__
        data = self._next_data()
      File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1207, in _next_data
        idx, data = self._get_data()
      File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1163, in _get_data
        success, data = self._try_get_data()
      File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1024, in _try_get_data
        raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) from e
    RuntimeError: DataLoader worker (pid(s) 15796) exited unexpectedly
    
    opened by digvijayad 2
  • red traffic lights

    red traffic lights

    Hello, thanks for your marvelous contribution.I would like to know that the category of red traffic lights is not available on bdd, have you re-labeled it on the bdd dataset?

    opened by liluxing153 1
  • tagging:finetune possibilities

    tagging:finetune possibilities

    hi hi, thanks for your marvelous contribution. I am very impressed. Now I want to apply this pretrain model(tagging road type and weather) on my own dataset, do you have any codebase for finetuning?

    opened by anran1231 1
  • Semantic segmetation ;common settings  MMSegmentation link not working

    Semantic segmetation ;common settings MMSegmentation link not working

    https://github.com/open-mmlab/mmsegmentation/blob/master/docs/model_zoo.md#common-settings The above link is not working

    I would like to know the settings under which the segmentation models are trained , so that i can replicate the results . thank you.

    opened by 100daggers 1
  • Issue in converting the instance segmentation mask encoding from bdd100k to coco

    Issue in converting the instance segmentation mask encoding from bdd100k to coco

    Hello,

    I am trying to convert the bdd100k instance segmentation using this command: python3 -m bdd100k.label.to_coco -m ins_seg --only-mask -i ./bdd100k/labels/ins_seg/bitmasks/val -o ./ins_seg_val_cocofmt_v2.json

    Also, tried this: python3 -m bdd100k.label.to_coco -m ins_seg -i ./bdd100k/labels/ins_seg/polygons/ins_seg_val.json -o ./ins_seg_val_cocofmt_v3.json -mb ./bdd100k/labels/ins_seg/bitmasks/val

    The conversion is successful in both cases and the annotation looks like this

    Screen Shot 2022-01-07 at 11 46 36 AM ** that's not how coco annotations are.

    Now, if you see the segmentation field above there's string encoding of the masks. Now, I am unsure if that's expected or not.

    Further, assuming it's correct, I tried to load the annotations using loader from DETR https://github.com/facebookresearch/detr/blob/091a817eca74b8b97e35e4531c1c39f89fbe38eb/datasets/coco.py#L36

    The line I have mentioned above is supposed to do the conversion but I am getting an error from the pycocotools that it's not expecting a string in the mask. Screen Shot 2022-01-07 at 11 53 51 AM

    So, I am unsure where the problem is? Is the conversion correct to coco then the loader should work? Note: I tried to convert the detections and they worked fine.

    Thank you for any help you can provide.

    opened by sfarkya04 1
  • How to train on my own gpu?

    How to train on my own gpu?

    Hello! thank you for your work~~but i wonder if i could train these models on my own gpu? i wonder if there are som instructions or usages? plz ,thank u!

    opened by StefanYz 1
Releases(v1.1.0)
  • v1.1.0(Dec 2, 2021)

    BDD100K Models 1.1.0 Release

    teaser

    • Highlights
    • New Task: Pose Estimation
    • New Models

    Highlights

    In this release, we provide over 20 pre-trained models for the new pose estimation task in BDD100K, along with evaluation and visualization tools. We also provide over 30 new models for object detection, instance segmentation, semantic segmentation, and drivable area.

    New Task: Pose Estimation

    With the release of 2D human pose estimation data in BDD100K, we provide pre-trained models in this repo.

    • Pose estimation
      • ResNet, MobileNetV2, HRNet, and more.

    New Models

    We provide additional models for previous tasks

    • Object detection
      • Libra R-CNN, HRNet.
    • Instance segmentation
      • GCNet, HRNet.
    • Semantic segmentation / drivable area
      • NLNet, PointRend.
    Source code(tar.gz)
    Source code(zip)
  • v1.0.0(Oct 29, 2021)

    BDD100K Models 1.0.0 Release

    teaser

    • Highlights
    • Tasks
    • Models
    • Contribution

    Highlights

    The model zoo for BDD100K, the largest driving video dataset, is open for business! It contains more than 100 pre-trained models for 7 tasks. Each model also comes with results and visualization on val and test sets. We also provide documentation for community contribution so that everyone can include their models in this repo.

    Tasks

    We currently support 7 tasks

    • Image Tagging
    • Object Detection
    • Instance Segmentation
    • Semantic Segmentation
    • Drivable Area
    • Multiple Object Tracking (MOT)
    • Multiple Object Tracking and Segmentation (MOTS)

    Each task includes

    • Official evaluation results, model weights, predictions, and visualizations.
    • Detailed instructions for evaluation and visualization.

    Models

    We include popular network models for each task

    • Image tagging
      • VGG, ResNet, and DLA.
    • Object detection
      • Cascade R-CNN, Sparse R-CNN, Deformable ConvNets v2, and more.
    • Instance segmentation
      • Mask R-CNN, Cascade Mask R-CNN, HRNet, and more.
    • Semantic segmentation / drivable area
      • Deeplabv3+, CCNet, DNLNet, and more.
    • Multiple object tracking (MOT)
      • QDTrack.
    • Multiple object tracking and segmentation (MOTS)
      • PCAN.

    Contribution

    We encourage the BDD100K dataset users to contribute their models to this repo, so that all the info can be used for further result reproduction and analysis. The detailed instruction and model submission template are at the contribution page.

    Source code(tar.gz)
    Source code(zip)
Owner
ETH VIS Group
Visual Intelligence and Systems Group at ETH Zürich
ETH VIS Group
A framework for the elicitation, specification, formalization and understanding of requirements.

A framework for the elicitation, specification, formalization and understanding of requirements.

NASA - Software V&V 161 Jan 03, 2023
Neural Point-Based Graphics

Neural Point-Based Graphics Project   Video   Paper Neural Point-Based Graphics Kara-Ali Aliev1 Artem Sevastopolsky1,2 Maria Kolos1,2 Dmitry Ulyanov3

Ali Aliev 252 Dec 13, 2022
TakeInfoatNistforICS - Take Information in NIST NVD for ICS

Take Information in NIST NVD for ICS This project developed with Python. When yo

5 Sep 05, 2022
Node for thenewboston digital currency network.

Project setup For project setup see INSTALL.rst Community Join the community to stay updated on the most recent developments, project roadmaps, and ra

thenewboston 27 Jul 08, 2022
Learning Representations that Support Robust Transfer of Predictors

Transfer Risk Minimization (TRM) Code for Learning Representations that Support Robust Transfer of Predictors Prepare the Datasets Preprocess the Scen

Yilun Xu 15 Dec 07, 2022
Dynamic Token Normalization Improves Vision Transformers

Dynamic Token Normalization Improves Vision Transformers This is the PyTorch implementation of the paper Dynamic Token Normalization Improves Vision T

Wenqi Shao 20 Oct 09, 2022
Prometheus Exporter for data scraped from datenplattform.darmstadt.de

darmstadt-opendata-exporter Scrapes data from https://datenplattform.darmstadt.de and presents it in the Prometheus Exposition format. Pull requests w

Martin Weinelt 2 Apr 12, 2022
The Unsupervised Reinforcement Learning Benchmark (URLB)

The Unsupervised Reinforcement Learning Benchmark (URLB) URLB provides a set of leading algorithms for unsupervised reinforcement learning where agent

259 Dec 26, 2022
The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding.

SuperGen The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding. Requirements Before running, you

Yu Meng 38 Dec 12, 2022
DiscoNet: Learning Distilled Collaboration Graph for Multi-Agent Perception [NeurIPS 2021]

DiscoNet: Learning Distilled Collaboration Graph for Multi-Agent Perception [NeurIPS 2021] Yiming Li, Shunli Ren, Pengxiang Wu, Siheng Chen, Chen Feng

Automation and Intelligence for Civil Engineering (AI4CE) Lab @ NYU 98 Dec 21, 2022
SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers

SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers This repo contains our codes for the paper "No Parameters Left Behind: Sensitivity Gu

Chen Liang 23 Nov 07, 2022
Data Preparation, Processing, and Visualization for MoVi Data

MoVi-Toolbox Data Preparation, Processing, and Visualization for MoVi Data, https://www.biomotionlab.ca/movi/ MoVi is a large multipurpose dataset of

Saeed Ghorbani 51 Nov 27, 2022
tf2-keras implement yolov5

YOLOv5 in tesnorflow2.x-keras yolov5数据增强jupyter示例 Bilibili视频讲解地址: 《yolov5 解读,训练,复现》 Bilibili视频讲解PPT文件: yolov5_bilibili_talk_ppt.pdf Bilibili视频讲解PPT文件:

yangcheng 254 Jan 08, 2023
Where2Act: From Pixels to Actions for Articulated 3D Objects

Where2Act: From Pixels to Actions for Articulated 3D Objects The Proposed Where2Act Task. Given as input an articulated 3D object, we learn to propose

Kaichun Mo 69 Nov 28, 2022
PyTorch implementation of CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition

PyTorch implementation of CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition The unofficial code of CDistNet. Now, we ha

25 Jul 20, 2022
For the paper entitled ''A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining''

Summary This is the source code for the paper "A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining", which was accepted as fu

1 Nov 10, 2021
Pytorch implementation of Decoupled Spatial-Temporal Transformer for Video Inpainting

Decoupled Spatial-Temporal Transformer for Video Inpainting By Rui Liu, Hanming Deng, Yangyi Huang, Xiaoyu Shi, Lewei Lu, Wenxiu Sun, Xiaogang Wang, J

51 Dec 13, 2022
CVPR 2021: "The Spatially-Correlative Loss for Various Image Translation Tasks"

Spatially-Correlative Loss arXiv | website We provide the Pytorch implementation of "The Spatially-Correlative Loss for Various Image Translation Task

Chuanxia Zheng 89 Jan 04, 2023
Implementation of paper "DCS-Net: Deep Complex Subtractive Neural Network for Monaural Speech Enhancement"

DCS-Net This is the implementation of "DCS-Net: Deep Complex Subtractive Neural Network for Monaural Speech Enhancement" Steps to run the model Edit V

Jack Walters 10 Apr 04, 2022
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

yobi byte 29 Oct 09, 2022