README.md

[PYTORCH] YOLO (You Only Look Once)

Introduction

Here is my pytorch implementation of the model described in the paper YOLO9000: Better, Faster, Stronger paper.

An example of my model's output.

How to use my code

With my code, you can:

• Train your model from scratch
• Train your model with my trained model
• Evaluate test images with either my trained model or yours

Requirements:

• python 3.6
• pytorch 0.4
• opencv (cv2)
• tensorboard
• tensorboardX (This library could be skipped if you do not use SummaryWriter)
• numpy

Datasets:

I used 4 different datases: VOC2007, VOC2012, COCO2014 and COCO2017. Statistics of datasets I used for experiments is shown below

Dataset	Classes	#Train images/objects	#Validation images/objects
VOC2007	20	5011/12608	4952/-
VOC2012	20	5717/13609	5823/13841
COCO2014	80	83k/-	41k/-
COCO2017	80	118k/-	5k/-

Create a data folder under the repository,

cd {repo_root}
mkdir data

• VOC: Download the voc images and annotations from VOC2007 or VOC2012. Make sure to put the files as the following structure:

  VOCDevkit
  ├── VOC2007
  │   ├── Annotations  
  │   ├── ImageSets
  │   ├── JPEGImages
  │   └── ...
  └── VOC2012
      ├── Annotations  
      ├── ImageSets
      ├── JPEGImages
      └── ...
  

• COCO: Download the coco images and annotations from coco website. Make sure to put the files as the following structure:

  COCO
  ├── annotations
  │   ├── instances_train2014.json
  │   ├── instances_train2017.json
  │   ├── instances_val2014.json
  │   └── instances_val2017.json
  │── images
  │   ├── train2014
  │   ├── train2017
  │   ├── val2014
  │   └── val2017
  └── anno_pickle
      ├── COCO_train2014.pkl
      ├── COCO_val2014.pkl
      ├── COCO_train2017.pkl
      └── COCO_val2017.pkl
  

Setting:

• Model structure: In compared to the paper, I changed structure of top layers, to make it converge better. You could see the detail of my YoloNet in src/yolo_net.py.
• Data augmentation: I performed dataset augmentation, to make sure that you could re-trained my model with small dataset (~500 images). Techniques applied here includes HSV adjustment, crop, resize and flip with random probabilities
• Loss: The losses for object and non-objects are combined into a single loss in my implementation
• Optimizer: I used SGD optimizer and my learning rate schedule is as follows:

Epoches	Learning rate
0-4	1e-5
5-79	1e-4
80-109	1e-5
110-end	1e-6

• In my implementation, in every epoch, the model is saved only when its loss is the lowest one so far. You could also use early stopping, which could be triggered by specifying a positive integer value for parameter es_patience, to stop training process when validation loss has not been improved for es_patience epoches.

Trained models

You could find all trained models I have trained in YOLO trained models

Training

For each dataset, I provide 2 different pre-trained models, which I trained with corresresponding dataset:

• whole_model_trained_yolo_xxx: The whole trained model.
• only_params_trained_yolo_xxx: The trained parameters only.

You could specify which trained model file you want to use, by the parameter pre_trained_model_type. The parameter pre_trained_model_path then is the path to that file.

If you want to train a model with a VOC dataset, you could run:

• python3 train_voc.py --year year: For example, python3 train_voc.py --year 2012

If you want to train a model with a COCO dataset, you could run:

• python3 train_coco.py --year year: For example, python3 train_coco.py --year 2014

If you want to train a model with both COCO datasets (training set = train2014 + val2014 + train2017, val set = val2017), you could run:

• python3 train_coco_all.py

Test

For each type of dataset (VOC or COCO), I provide 3 different test scripts:

If you want to test a trained model with a standard VOC dataset, you could run:

• python3 test_xxx_dataset.py --year year: For example, python3 test_coco_dataset.py --year 2014

If you want to test a model with some images, you could put them into the same folder, whose path is path/to/input/folder, then run:

• python3 test_xxx_images.py --input path/to/input/folder --output path/to/output/folder: For example, python3 train_voc_images.py --input test_images --output test_images

If you want to test a model with a video, you could run :

• python3 test_xxx_video.py --input path/to/input/file --output path/to/output/file: For example, python3 test_coco_video --input test_videos/input.mp4 --output test_videos/output.mp4

Experiments:

I trained models in 2 machines, one with NVIDIA TITAN X 12gb GPU and the other with NVIDIA quadro 6000 24gb GPU.

The training/test loss curves for each experiment are shown below:

• VOC2007
• VOC2012
• COCO2014
• COCO2014+2017

Statistics for mAP will be updated soon ...

Results

Some output predictions for experiments for each dataset are shown below:

• VOC2007

• VOC2012

• COCO2014

• COCO2014+2017