GithubHelp home page GithubHelp logo

yolov3multi's Introduction

Contents

YOLOv3-DarkNet53 Description

You only look once (YOLO) is a state-of-the-art, real-time object detection system. YOLOv3 is extremely fast and accurate.

Prior detection systems repurpose classifiers or localizers to perform detection. They apply the model to an image at multiple locations and scales. High scoring regions of the image are considered detections. YOLOv3 use a totally different approach. It apply a single neural network to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities.

YOLOv3 uses a few tricks to improve training and increase performance, including: multi-scale predictions, a better backbone classifier, and more. The full details are in the paper!

Paper: YOLOv3: An Incremental Improvement. Joseph Redmon, Ali Farhadi, University of Washington

Model Architecture

YOLOv3 use DarkNet53 for performing feature extraction, which is a hybrid approach between the network used in YOLOv2, Darknet-19, and that newfangled residual network stuff. DarkNet53 uses successive 3 × 3 and 1 × 1 convolutional layers and has some shortcut connections as well and is significantly larger. It has 53 convolutional layers.

Dataset

Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.

Dataset used: COCO2014

  • Dataset size: 19G, 123,287 images, 80 object categories.

    • Train：13G, 82,783 images
    • Val：6G, 40,504 images
    • Annotations: 241M, Train/Val annotations

  • The directory structure is as follows.

        ├── dataset
        ├── coco2014
            ├── annotations
            │   ├─ train.json
            │   └─ val.json
            ├─ train
            │   ├─picture1.jpg
            │   ├─ ...
            │   └─picturen.jpg
            └─ val
                ├─picture1.jpg
                ├─ ...
                └─picturen.jpg

  • If the user uses his own data set, the data set format needs to be converted to coco data format, and the data in the JSON file should correspond to the image data one by one. After accessing user data, because the size and quantity of image data are different, lr, anchor_scale and training_shape may need to be adjusted appropriately.

Environment Requirements

Quick Start

  • After installing MindSpore via the official website, you can start training and evaluation in as follows. If running on GPU, please add --device_target=GPU in the python command or use the "_gpu" shell script ("xxx_gpu.sh").

  • Prepare the backbone_darknet53.ckpt and hccl_8p.json files, before run network.

    • Pretrained_backbone can use convert_weight.py, convert darknet53.conv.74 to mindspore ckpt.

      python convert_weight.py --input_file ./darknet53.conv.74

      darknet53.conv.74 can get from download . you can use command in linux os.

      wget https://pjreddie.com/media/files/darknet53.conv.74

    • Genatating hccl_8p.json, Run the script of utils/hccl_tools/hccl_tools.py.(Only useful for Ascend device) The following parameter "[0-8)" indicates that the hccl_8p.json file of cards 0 to 7 is generated.

      • The name of json file generated by this command is hccl_8p_01234567_{host_ip}.json. For convenience of expression, use hccl_8p.json represents the json file.
      python hccl_tools.py --device_num "[0,8)"

  • Train on local

    # The parameter of training_shape define image shape for network, default is "".
# It means use 10 kinds of shape as input shape, or it can be set some kind of shape.
# run training example(1p) by python command.
python train.py \
    --data_dir=./dataset/coco2014 \
    --pretrained_backbone=backbone_darknet53.ckpt \
    --is_distributed=0 \
    --lr=0.001 \
    --loss_scale=1024 \
    --weight_decay=0.016 \
    --T_max=320 \
    --max_epoch=320 \
    --warmup_epochs=4 \
    --training_shape=416 \
    --lr_scheduler=cosine_annealing > log.txt 2>&1 &

# For Ascend device, standalone training example(1p) by shell script
bash run_standalone_train.sh dataset/coco2014 backbone_darknet53.ckpt

# For GPU device, standalone training example(1p) by shell script
bash run_standalone_train_gpu.sh dataset/coco2014 backbone_darknet53.ckpt

# For Ascend device, distributed training example(8p) by shell script
bash run_distribute_train.sh dataset/coco2014 backbone_darknet53.ckpt hccl_8p.json

# For GPU device, distributed training example(8p) by shell script
bash run_distribute_train_gpu.sh dataset/coco2014 backbone_darknet53.ckpt

# run evaluation by python command
  - For the standalone training mode, the ckpt file generated by training is stored in train/outputs/{year}-{month}-{day}_time_{hour}_{minute}_{second}/ckpt_0 directory.
  - For distributed training mode, the ckpt file generated by training is stored in train_parallel0/outputs/{year}-{month}-{day}_time_{hour}_{minute}_{second}/ckpt_0 directory.

python eval.py \
    --data_dir=./dataset/coco2014 \
    --pretrained=train_parallel0/outputs/{year}-{month}-{day}_time_{hour}_{minute}_{second}/ckpt_0/0-99_31680.ckpt \
    --testing_shape=416 > log.txt 2>&1 &

# run evaluation by shell script
bash run_eval.sh dataset/coco2014/ train_parallel0/outputs/{year}-{month}-{day}_time_{hour}_{minute}_{second}/ckpt_0/0-99_31680.ckpt

  • Train on ModelArts

    # Train 8p with Ascend
# (1) Perform a or b.
#       a. Set "enable_modelarts=True" on base_config.yaml file.
#          Set "data_dir='/cache/data/coco2014/'" on base_config.yaml file.
#          Set "checkpoint_url='s3://dir_to_your_pretrain/'" on base_config.yaml file.
#          Set "pretrained_backbone='/cache/checkpoint_path/0-148_92000.ckpt'" on base_config.yaml file.
#          Set "weight_decay=0.016" on base_config.yaml file.
#          Set "warmup_epochs=4" on base_config.yaml file.
#          Set "lr_scheduler='cosine_annealing'" on base_config.yaml file.
#          Set other parameters on base_config.yaml file you need.
#       b. Add "enable_modelarts=True" on the website UI interface.
#          Add "data_dir=/cache/data/coco2014/" on the website UI interface.
#          Add "checkpoint_url=s3://dir_to_your_pretrain/" on the website UI interface.
#          Add "pretrained_backbone=/cache/checkpoint_path/0-148_92000.ckpt" on the website UI interface.
#          Add "weight_decay=0.016" on the website UI interface.
#          Add "warmup_epochs=4" on the website UI interface.
#          Add "lr_scheduler=cosine_annealing" on the website UI interface.
#          Add other parameters on the website UI interface.
# (3) Upload or copy your pretrained model to S3 bucket.
# (4) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
# (5) Set the code directory to "/path/yolov3_darknet53" on the website UI interface.
# (6) Set the startup file to "train.py" on the website UI interface.
# (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
# (8) Create your job.
#
# Eval with Ascend
# (1) Perform a or b.
#       a. Set "enable_modelarts=True" on base_config.yaml file.
#          Set "data_dir='/cache/data/coco2014/'" on base_config.yaml file.
#          Set "checkpoint_url='s3://dir_to_your_trained_ckpt/'" on base_config.yaml file.
#          Set "pretrained='/cache/checkpoint_path/0-320_102400.ckpt'" on base_config.yaml file.
#          Set "testing_shape=416" on base_config.yaml file.
#          Set other parameters on base_config.yaml file you need.
#       b. Add "enable_modelarts=True" on the website UI interface.
#          Add "data_dir=/cache/data/coco2014/" on the website UI interface.
#          Add "checkpoint_url=s3://dir_to_your_trained_ckpt/" on the website UI interface.
#          Add "pretrained=/cache/checkpoint_path/0-320_102400.ckpt" on the website UI interface.
#          Add "testing_shape=416" on the website UI interface.
#          Add other parameters on the website UI interface.
# (3) Upload or copy your trained model to S3 bucket.
# (4) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
# (5) Set the code directory to "/path/yolov3_darknet53" on the website UI interface.
# (6) Set the startup file to "eval.py" on the website UI interface.
# (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
# (8) Create your job.

Script Description

Script and Sample Code

.
└─yolov3_darknet53
  ├─README.md
  ├─mindspore_hub_conf.md             # config for mindspore hub
  ├─scripts
    ├─run_standalone_train.sh         # launch standalone training(1p) in ascend
    ├─run_distribute_train.sh         # launch distributed training(8p) in ascend
    ├─run_infer_310.sh                # launch inference in ascend
    └─run_eval.sh                     # launch evaluating in ascend
    ├─run_standalone_train_gpu.sh     # launch standalone training(1p) in gpu
    ├─run_distribute_train_gpu.sh     # launch distributed training(8p) in gpu
    ├─run_eval_gpu.sh                 # launch evaluating in gpu
    └─run_infer_gpu.sh                # launch ONNX inference in gpu
  ├─src
    ├─__init__.py                     # python init file
    ├─config.py                       # parameter configuration
    ├─darknet.py                      # backbone of network
    ├─distributed_sampler.py          # iterator of dataset
    ├─initializer.py                  # initializer of parameters
    ├─logger.py                       # log function
    ├─loss.py                         # loss function
    ├─lr_scheduler.py                 # generate learning rate
    ├─transforms.py                   # Preprocess data
    ├─util.py                         # util function
    ├─yolo.py                         # yolov3 network
    ├─yolo_dataset.py                 # create dataset for YOLOV3
  ├─eval.py                           # eval net
  ├─eval_onnx.py                      # inference net
  └─train.py                          # train net

Script Parameters

Major parameters in train.py as follow.

optional arguments:
  -h, --help            show this help message and exit
  --device_target       device where the code will be implemented: "Ascend" | "GPU", default is "Ascend"
  --data_dir DATA_DIR   Train dataset directory.
  --per_batch_size PER_BATCH_SIZE
                        Batch size for Training. Default: 32.
  --pretrained_backbone PRETRAINED_BACKBONE
                        The ckpt file of DarkNet53. Default: "".
  --resume_yolov3 RESUME_YOLOV3
                        The ckpt file of YOLOv3, which used to fine tune.
                        Default: ""
  --lr_scheduler LR_SCHEDULER
                        Learning rate scheduler, options: exponential,
                        cosine_annealing. Default: exponential
  --lr LR               Learning rate. Default: 0.001
  --lr_epochs LR_EPOCHS
                        Epoch of changing of lr changing, split with ",".
                        Default: 220,250
  --lr_gamma LR_GAMMA   Decrease lr by a factor of exponential lr_scheduler.
                        Default: 0.1
  --eta_min ETA_MIN     Eta_min in cosine_annealing scheduler. Default: 0
  --T_max T_MAX         T-max in cosine_annealing scheduler. Default: 320
  --max_epoch MAX_EPOCH
                        Max epoch num to train the model. Default: 320
  --warmup_epochs WARMUP_EPOCHS
                        Warmup epochs. Default: 0
  --weight_decay WEIGHT_DECAY
                        Weight decay factor. Default: 0.0005
  --momentum MOMENTUM   Momentum. Default: 0.9
  --loss_scale LOSS_SCALE
                        Static loss scale. Default: 1024
  --label_smooth LABEL_SMOOTH
                        Whether to use label smooth in CE. Default:0
  --label_smooth_factor LABEL_SMOOTH_FACTOR
                        Smooth strength of original one-hot. Default: 0.1
  --log_interval LOG_INTERVAL
                        Logging interval steps. Default: 100
  --ckpt_path CKPT_PATH
                        Checkpoint save location. Default: outputs/
  --ckpt_interval CKPT_INTERVAL
                        Save checkpoint interval. Default: None
  --is_save_on_master IS_SAVE_ON_MASTER
                        Save ckpt on master or all rank, 1 for master, 0 for
                        all ranks. Default: 1
  --is_distributed IS_DISTRIBUTED
                        Distribute train or not, 1 for yes, 0 for no. Default:
                        1
  --rank RANK           Local rank of distributed. Default: 0
  --group_size GROUP_SIZE
                        World size of device. Default: 1
  --need_profiler NEED_PROFILER
                        Whether use profiler. 0 for no, 1 for yes. Default: 0
  --training_shape TRAINING_SHAPE
                        Fix training shape. Default: ""
  --resize_rate RESIZE_RATE
                        Resize rate for multi-scale training. Default: None
  --bind_cpu BIND_CPU
                        Whether bind cpu when distributed training. Default: True
  --device_num DEVICE_NUM
                        Device numbers per server. Default: 8

Training Process

Training

python train.py \
    --data_dir=./dataset/coco2014 \
    --pretrained_backbone=backbone_darknet53.ckpt \
    --is_distributed=0 \
    --lr=0.001 \
    --loss_scale=1024 \
    --weight_decay=0.016 \
    --T_max=320 \
    --max_epoch=320 \
    --warmup_epochs=4 \
    --training_shape=416 \
    --lr_scheduler=cosine_annealing > log.txt 2>&1 &

The python command above will run in the background, you can view the results through the file log.txt. If running on GPU, please add --device_target=GPU in the python command.

After training, you'll get some checkpoint files under the outputs folder by default. The loss value will be achieved as follows:

# grep "loss:" train/log.txt
2020-08-20 14:14:43,640:INFO:epoch[0], iter[0], loss:7809.262695, 0.15 imgs/sec, lr:9.746589057613164e-06
2020-08-20 14:15:05,142:INFO:epoch[0], iter[100], loss:2778.349033, 133.92 imgs/sec, lr:0.0009844054002314806
2020-08-20 14:15:31,796:INFO:epoch[0], iter[200], loss:535.517361, 130.54 imgs/sec, lr:0.0019590642768889666
...

The model checkpoint will be saved in outputs directory.

Distributed Training

For Ascend device, distributed training example(8p) by shell script

bash run_distribute_train.sh dataset/coco2014 backbone_darknet53.ckpt hccl_8p.json

For GPU device, distributed training example(8p) by shell script

bash run_distribute_train_gpu.sh dataset/coco2014 backbone_darknet53.ckpt

The above shell script will run distribute training in the background. You can view the results through the file train_parallel0/log.txt. The loss value will be achieved as follows:

# distribute training result(8p)
epoch[0], iter[0], loss:14623.384766, 1.23 imgs/sec, lr:7.812499825377017e-07
epoch[0], iter[100], loss:746.253051, 22.01 imgs/sec, lr:7.890690624925494e-05
epoch[0], iter[200], loss:101.579535, 344.41 imgs/sec, lr:0.00015703124925494192
epoch[0], iter[300], loss:85.136754, 341.99 imgs/sec, lr:0.00023515624925494185
epoch[1], iter[400], loss:79.429322, 405.14 imgs/sec, lr:0.00031328126788139345
...
epoch[318], iter[102000], loss:30.504046, 458.03 imgs/sec, lr:9.63797575082026e-08
epoch[319], iter[102100], loss:31.599150, 341.08 imgs/sec, lr:2.409552052995423e-08
epoch[319], iter[102200], loss:31.652273, 372.57 imgs/sec, lr:2.409552052995423e-08
epoch[319], iter[102300], loss:31.952403, 496.02 imgs/sec, lr:2.409552052995423e-08
...

Evaluation Process

Evaluation

Before running the command below. If running on GPU, please add --device_target=GPU in the python command or use the "_gpu" shell script ("xxx_gpu.sh").

python eval.py \
    --data_dir=./dataset/coco2014 \
    --pretrained=train_parallel0/outputs/{year}-{month}-{day}_time_{hour}_{minute}_{second}/ckpt_0/0-99_31680.ckpt \
    --testing_shape=416 > log.txt 2>&1 &
OR
bash run_eval.sh dataset/coco2014/ train_parallel0/outputs/{year}-{month}-{day}_time_{hour}_{minute}_{second}/ckpt_0/0-99_31680.ckpt

The above python command will run in the background. You can view the results through the file "log.txt". The mAP of the test dataset will be as follows:

This the standard format from pycocotools, you can refer to cocodataset for more detail.

# log.txt
=============coco eval reulst=========
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.311
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.528
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.322
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.259
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.398
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.423
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.224
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.442
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551

Export MindIR or ONNX

Currently, batchsize can only set to 1.

python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] --keep_detect [Bool] --device_target=[DEVICE]

The ckpt_file parameter is required, Currently,FILE_FORMAT should be in ["AIR", "ONNX", "MINDIR"] keep_detect keep the detect module or not, default: True device_target should be in ["Ascend", "GPU", "CPU"], default: Ascend

Inference Process

Usage

Before performing inference, the air or onnx file must be exported by export.py. Current batch_Size can only be set to 1. Because the DVPP hardware is used for processing, the picture must comply with the JPEG encoding format, Otherwise, an error will be reported. For example, the COCO_val2014_000000320612.jpg in coco2014 dataset needs to be deleted.

# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID]

DEVICE_ID is optional, default value is 0. DATA_PATH is evaluation data path, ANNO_PATH is annotation file path, json format. e.g., instances_val2014.json.

# onnx inference
bash run_infer_gpu.sh [DATA_PATH] [ONNX_PATH]

DATA_PATH is evaluation data path, include annotation file path, json format. e.g., instances_val2014.json.

result

Inference result in Ascend is saved in current path, you can find result in acc.log file.

# acc.log
=============coco eval reulst=========
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.311
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.528
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.322
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.259
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.398
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.423
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.224
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.442
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551

Inference result in ONNX is saved in current path, you can find result in log.txt file.

# log.txt
=============coco eval reulst=========
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.314
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.532
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.326
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.133
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.328
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.449
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.263
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.405
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.431
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.234
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.453
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.579

Model Description

Performance

Evaluation Performance

Parameters YOLO YOLO
Model Version YOLOv3 YOLOv3
Resource Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 NV SMX2 V100-16G; CPU 2.10GHz, 96cores; Memory, 251G
uploaded Date 09/15/2020 (month/day/year) 09/02/2020 (month/day/year)
MindSpore Version 1.1.1 1.1.1
Dataset COCO2014 COCO2014
Training Parameters epoch=320, batch_size=32, lr=0.001, momentum=0.9 epoch=320, batch_size=32, lr=0.1, momentum=0.9
Optimizer Momentum Momentum
Loss Function Sigmoid Cross Entropy with logits Sigmoid Cross Entropy with logits
outputs boxes and label boxes and label
Loss 34 34
Speed 1pc: 350 ms/step; 1pc: 600 ms/step;
Total time 8pc: 13 hours 8pc: 18 hours(shape=416)
Parameters (M) 62.1 62.1
Checkpoint for Fine tuning 474M (.ckpt file) 474M (.ckpt file)

Inference Performance

Parameters YOLO YOLO
Model Version YOLOv3 YOLOv3
Resource Ascend 910; OS Euler2.8 NV SMX2 V100-16G
Uploaded Date 09/15/2020 (month/day/year) 08/20/2020 (month/day/year)
MindSpore Version 1.1.1 1.1.1
Dataset COCO2014, 40,504 images COCO2014, 40,504 images
batch_size 1 1
outputs mAP mAP
Accuracy 8pcs: 31.1% 8pcs: 29.7%~30.3% (shape=416)
Model for inference 474M (.ckpt file) 474M (.ckpt file)

Description of Random Situation

There are random seeds in distributed_sampler.py, transforms.py, yolo_dataset.py files.

ModelZoo Homepage

Please check the official homepage.

yolov3multi's People

Contributors

likaiucas avatar narcooo avatar

Watchers

avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.