btdat2506 / mobilenets-ssd-pytorch

MobileNet-SSD, MobileNetV2-SSD/SSDLite on VOC, BDD100K Datasets.

MobileNet-SSD and MobileNetV2-SSD/SSDLite with PyTorch

Object Detection with MobileNet-SSD, MobileNetV2-SSD/SSDLite on VOC, BDD100K Datasets.

Instructions

$ pip install --user kaggle
$ kaggle datasets download ansarisaquib/bdd100

$ mkdir bdd100k
$ mv bdd100.zip bdd100k
$ unzip bdd100k/bdd100.zip
$ cd bdd100k

# Change location of labels which is from "bdd100k/bdd100_labels_release/bdd100k/labels" to "bdd100k/bdd100k/labels".
$ cp -r bdd100k_labels_release/bdd100k/labels/ ./bdd100k/bdd100k/


# Below is the structure of the dataset

|- bdd100k
|- bdd100k_drivable_labels_trainval
|- bdd100k_ins_seg_labels_trainval
|- bdd100k_labels_release
|- bdd100k_pan_seg_labels_trainval
|- bdd100k_sem_seg_labels_trainval

- content - 
  |- bdd100k -- bdd100k -- images -- 100k -- train -- (70000 images)
  |               |                |      |- val -- (10000 images)
  |               |                | 
  |               |                |- 10k -- train
  |               | 
  |               |
  |               |- labels -- (.json)
  |               |
  |               |- xml -- train -- (.xml)
  |               |- val -- (.xml)
  |
  |- pytorch-ssd - data -- VOCdevkit -- test -- VOC2007 -- (Annotations, ImageSets, JPEGImages,...)
       (our repo)   |                    |- VOC2007 -- (Annotations, ImageSets, JPEGImages,...)
                    |
                    |- bdd_files
                    |- images
                    |- models
                    |- ...
                    |- train_ssd_BDD.py
                    |- ssd_test_img.py
                    |- ...

Pre-processing

1. Convert BDD100K anotation format (.json) to VOC anotation format (.xml)

# Note:
# - This will take a while to run.
# - Have not check on its effect yet, will investigate further in the future.
$ python bdd2voc.py

This is what it should look like after the first pre-processing.

2. Remove training samples having no anotation (70000 to 69863)

$ python remove_nolabel_data.py

Download Pre-trained Models (VOC)

1. MobileNet-SSD

$ wget -P models https://storage.googleapis.com/models-hao/mobilenet-v1-ssd-mp-0_675.pth

2. MobileNetV2-SSDLite

$ wget -P models https://storage.googleapis.com/models-hao/mb2-ssd-lite-mp-0_686.pth

Train

1. Train MobileNet-SSD (VOC) (don't use cause not figure out where VOC2007 D is located).

$ python train_ssd_VOC.py --datasets ~/data/VOCdevkit/VOC2007/ --validation_dataset ~/data/VOCdevkit/test/VOC2007/ --net mb1-ssd --batch_size 24 --num_epochs 100 --scheduler cosine --lr 0.01 --t_max 200

2. Train MobileNet-SSD (BDD100K)

$ python train_ssd_BDD.py --datasets ../bdd100k/bdd100k/images/100k/train/ --validation_dataset ../bdd100k/bdd100k/images/100k/val/ --net mb1-ssd --batch_size 48 --num_epochs 200 --scheduler cosine --lr 0.01 --t_max 200

3. Resume training a trained model (BDD100K)

$ python train_ssd_BDD.py --datasets ../bdd100k/bdd100k/images/100k/train/ --validation_dataset ../bdd100k/bdd100k/images/100k/val/ --net mb1-ssd --batch_size 48 --num_epochs 200 --scheduler cosine --lr 0.01 --t_max 200 --resume models/mb1-ssd-Epoch-105-Loss-inf.pth 

4. Train pretrained-model MobileNetV2-SSDLite (BDD100K)

$ python train_ssd_BDD.py --datasets ../bdd100k/bdd100k/images/100k/train/ --validation_dataset ../bdd100k/bdd100k/images/100k/val/ --net mb2-ssd-lite --pretrained_ssd models/mb2-ssd-lite-net.pth --scheduler cosine --lr 0.01 --t_max 100 --batch_size 36 --num_epochs 200 

Resume Training

$ python train_ssd_BDD.py --datasets ../bdd100k/bdd100k/images/100k/train/ --validation_dataset ../bdd100k/bdd100k/images/100k/val/ --net mb2-ssd-lite --resume models/(trained-model-name).pth --batch_size 16 --num_epochs 100 --scheduler cosine --lr 0.001 --t_max 100 --debug_steps 10

Test

1. Test on image

$ python ssd_test_img.py

2. Test on video

$ python ssd_test_video.py

Produce Detection Information

• Print out text file (.txt) of dectection information

notebook: print_text_files.ipynb

• File format: {image_name}.txt [ class_name confidence x1 y1 x2 y2 ]

car 0.980528 194 356 466 513
car 0.897605 752 372 975 467
car 0.414176 580 372 646 416
traffic_light 0.605162 844 178 867 225
traffic_light 0.602555 816 176 841 224
traffic_light 0.495851 109 62 141 124
truck 0.580303 167 265 485 483

References

• https://github.com/qfgaohao/pytorch-ssd
• https://github.com/leeesangwon/bdd100k_to_VOC Credit from Tran Le Anh in April 2020.

Updated in March 2023 by @btdat2506