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SSD Implementation in Pytorch

This repository implements SSD, with training, inference and mAP evaluation in PyTorch. Most of the code is just parts of pytorch ssd implementation and all I have done is gotten rid of abstractions and commented the code.

The repo provides code to train on voc dataset. Specifically I trained on trainval images of VOC 2007 dataset and for testing, I use VOC2007 test set.

SSD Explanation and Implementation Video

SSD Explanation and Implementation

Result by training SSD on VOC 2007 dataset

One should be able to get 71-72% mAP by training on VOC 2007 trainval images(68% reported in paper).

Adding 2012 trainval we should be able to get >77% mAP


Here's an evaluation result that I got after training 100 epochs.

Class Wise Average Precisions
AP for class aeroplane = 0.7552
AP for class bicycle = 0.8384
AP for class bird = 0.7025
AP for class boat = 0.6543
AP for class bottle = 0.3411
AP for class bus = 0.8355
AP for class car = 0.8611
AP for class cat = 0.8682
AP for class chair = 0.4798
AP for class cow = 0.7453
AP for class diningtable = 0.7092
AP for class dog = 0.8582
AP for class horse = 0.8506
AP for class motorbike = 0.8259
AP for class person = 0.7721
AP for class pottedplant = 0.3939
AP for class sheep = 0.7300
AP for class sofa = 0.7626
AP for class train = 0.8615
AP for class tvmonitor = 0.7260
Mean Average Precision : 0.7286

Data preparation

For setting up the VOC 2007 dataset:

  • Create a data directory inside SSD-Pytorch
  • Download VOC 2007 train/val data from http://host.robots.ox.ac.uk/pascal/VOC/voc2007 and copy the VOC2007 directory inside data directory
  • Download VOC 2007 test data from http://host.robots.ox.ac.uk/pascal/VOC/voc2007 and copy the VOC2007 directory and name it as VOC2007-test directory inside data
  • If you want to use 2012 trainval images as well, then download VOC 2012 train/val data from http://host.robots.ox.ac.uk/pascal/VOC/voc2007 and copy the VOC2012 directory inside data
    • Ensure to place all the directories inside the data folder of repo according to below structure 
      SSD-Pytorch
    -> data
        -> VOC2007
            -> JPEGImages
            -> Annotations
            -> ImageSets
        -> VOC2007-test
            -> JPEGImages
            -> Annotations
        -> VOC2012 (if needed)
            -> JPEGImages
            -> Annotations
            -> ImageSets
    -> tools
        -> train.py
        -> infer.py
    -> config
        -> voc.yaml
    -> model
        -> ssd.py 
    -> dataset
        -> voc.py

For training on your own dataset

  • Update the path for train_im_sets, test_im_sets in config
  • If you want to train on 2007+2012 trainval then have train_im_sets as ['data/VOC2007', 'data/VOC2012']
  • Modify dataset file dataset/voc.py to load images and annotations accordingly specifically load_images_and_anns method
  • Update the class list of your dataset in the dataset file.
  • Dataset class should return the following: 
    im_tensor(C x H x W) , 
target{
    'bboxes': Number of Gts x 4 (this is in x1y1x2y2 format normalized from 0-1)
    'labels': Number of Gts,
    'difficult': Number of Gts,
    }
file_path

For modifications

  • In case you have GPU which does not support large batch size, you can use a smaller batch size like 2 and then have acc_steps in config set as 4(to mimic 8 batch size training).
  • For using a different backbone you would have to change the following:
    • Change the backbone, extra conv layers and creation of feature maps in initialization of SSD model
    • Ensure the out_channels is correctly set as the channels in all feature maps to be used for prediction here
    • In the forward method call the backbone and extra conv layers and ensure outputs is correctly set as list of feature maps here

Quickstart

  • Create a new conda environment with python 3.10 then run below commands
  • git clone https://github.com/explainingai-code/SSD-PyTorch.git
  • cd SSD-PyTorch
  • pip install -r requirements.txt
  • For training/inference use the below commands passing the desired configuration file as the config argument in case you want to play with it.
  • python -m tools.train for training SSD on VOC dataset
  • python -m tools.infer --evaluate False --infer_samples True for generating inference predictions
  • python -m tools.infer --evaluate True --infer_samples False for evaluating on test dataset

Configuration

  • config/voc.yaml - Allows you to play with different components of SSD on voc dataset

Output

Outputs will be saved according to the configuration present in yaml files.

For every run a folder of task_name key in config will be created

During training of SSD the following output will be saved

  • Latest Model checkpoint in task_name directory

During inference the following output will be saved

  • Sample prediction outputs for images in task_name/samples

Citations

@article{DBLP:journals/corr/LiuAESR15,
  author       = {Wei Liu and
                  Dragomir Anguelov and
                  Dumitru Erhan and
                  Christian Szegedy and
                  Scott E. Reed and
                  Cheng{-}Yang Fu and
                  Alexander C. Berg},
  title        = {{SSD:} Single Shot MultiBox Detector},
  journal      = {CoRR},
  volume       = {abs/1512.02325},
  year         = {2015},
  url          = {http://arxiv.org/abs/1512.02325},
  eprinttype    = {arXiv},
  eprint       = {1512.02325},
  timestamp    = {Wed, 12 Feb 2020 08:32:49 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/LiuAESR15.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}