ZeyuRuan / YouOnlyLookOnce-TF2.0

Tensorflow 2.0 Keras implementation for the original YOLO - YUMMY SALTY FISH

YouOnlyLookOnce-TF2.0

Pure Tensorflow 2.0 Keras implementation for the original YOLO Paper

This repository has no dependency on Darknet

This is the final project for CSCI-1470 Deep Learning @ Brown University

Credits:

• LoveHRTF, 20' ScM.
• Gengg, 20' ScM.
• SHIXINLIU, 20' ScM.
• ZeyuRuan, 20' ScM.

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Brief and Branches

dataset-pascal-voc

Original YOLO, which can be trained on PASCAL-VOC 2007 and 2012 datasets with 20 classes, and outputs tensor with size [None, 7, 7, 30] for 20 classes of objects.

master

Modified code for training on COCO 2017 dataset, and the last dense layer was increased to 8192 to compensate for extra object classes, and outputs tensor with size [None, 7, 7, 90] for 80 classes of objects.

Tensorboard

Tensorboard was enabled in this project. Log files were kept in ~\YouOnlyLookOnce-TF2.0\src\logs To start the local tensorboard server, use the following command in ~\YouOnlyLookOnce-TF2.0\src:

tensorboard --logdir logs/gradient_tape

File Structure

Code

~\YouOnlyLookOnce-TF2.0

  --src-|
        |-logs-|
        |      \-gradient_tape\ ........................... Log files generated by tensorboard logger
        |-config.py ....................................... Training configrations
        |-main.py ......................................... Main function
        |-model.py ........................................ Model class contains model, optimizer, and loss functions
        |-dataset.py ...................................... Dataset class for generating batches of data
        |-train.py ........................................ Train loop
        |-test.py ......................................... Test loop
        |-video_application.py ............................ Real-time detection using webcam
        |-image-application.py ............................ Perform detection on single image
        \-visualize.py-| .................................. Bounding box drawing and convert detection results to image
                       |-generate_prediction()
                       |-visualization()
                       |-decoder()
                       \-nms()
        
  --data-|
         |-voc_label.py ................................... Generate label files for PASCAL-VOC 2007 and 2012
         |-train.txt ...................................... PASCAL-VOC 2007 + 2012 training image absolut path list
         |-test.txt ....................................... PASCAL-VOC 2007 + 2012 testing image absolut path list
         |-train_coco.txt ................................. COCO 2017 training image absolut path list
         |-test_coco.txt .................................. COCO 2017 testing image absolut path list
         |-VOCdevkit (Using PASCAL VOC)-| ................. PASCAL-VOC 2007 + 2012 dataset
         |                              |-VOC2007/
         |                              \-VOC2012/
         \-coco (Using COCO 2017)-| ....................... COCO 2017 dataset
                                  |-annotations/
                                  \-images-|
                                           |-train2017/
                                           \-val2017/
                     
  --tool-|
         |-preprocess_coco.py ............................. Function to generate COCO 2017 train/test absolut path list from dataset
         |-coco_format.py ................................. COCO 2017 data preprocess class
         \-preprocess_pascal_voc.py ....................... Function to generate PASCAL-VOC train/test absolut path list from datasets

Model Checkpoints and Temporary Files

  --checkpoints-| ......................................... Model checkpoint files
                |-checkpoint
                |-ckpt-xx.index
                |-ckpt-xx.data-00000-of-00002
                \-ckpt-xx.data-00001-of-00002
  
  --evaluation-/ .......................................... Evaluation results

  --tmp-| ................................................. Generated image files
        |-default_folder/
        |-single_images/
        \-epoch_x/
  
  --doc-\

Trained Model

Trained models were provided in Google Drive. To use the model trained on PASCAL-VOC dataset with 20 classes of objects, you will need to switch to branch dataset-pascal-voc. Otherwise use master branch for using the model trained on COCO 2017 with 80 classes of objects.

Two trained models were provided below for both PASCAL-VOC 2007 + 2012 and COCO. Model for PASCAL-VOC was trained for around an entire day on one single AMD RADEON VII GPU, and model for COCO 2017 was trained on one signle NVIDIA RTX 2080Ti GPU.

• Model on PASCAL-VOC 2007 and 2012 for 100 epochs: here
• Model on COCO 2017 for 315 epochs: here

1. Data Gather and Pre-process ()

1.1. Using COCO 2017

This Section Requires Using Branch master

setup_coco.sh was provided for automation. It proves a one line solution for downloading all required datasets and generates trainable files. Following command is for utilizing this file, the process may take over an hour:

bash setup_coco.sh

Manual setup is also available by using the following instructions.

• Follow the instructions here

1.2. Using PASCAL VOC 2012 and 2007

This Section Requires Using Branch dataset-pascal-voc

setup_pascal_voc.sh was provided for automation. It proves a one line solution for downloading all required datasets and generates trainable files. Following command is for utilizing this file:

bash setup_pascal_voc.sh

Manual setup is also available by using the following instructions.

1.2.1. Data Download and Extraction

Under dir ~/YouOnlyLookOnce-TF2.0/src/

Follow the instructions here This step will download and extract the PASCAL VOC 2012 and 2007 datasets

1.2.2. Re-scale and Coordinate Transformation

Under dir ~/YouOnlyLookOnce-TF2.0/src/

Follow the instructions here. This step will generate path for training data train.txt and test.txt

2. Train

Under dir ~/YouOnlyLookOnce-TF2.0/src/

Test outputs will be generated every time when model was auto saved (per 5 epochs for dataset-pascal-voc and everytime for master), and located in /YouOnlyLookOnce-TF2.0/tmp/epoch_x

2.1. Train a new model

To train from scratch, model checkpoints will be stored in /YouOnlyLookOnce-TF2.0/checkpoints/: python main.py

2.2. Restore from a checkpoint

To resume training from the latest saved checkpoint: python main.py --restore

3. Test

Under dir ~/YouOnlyLookOnce-TF2.0/src/

To test the latest checkpoint on test set: python main.py --mode=test

Note that dropout will be eliminated in test and visualization mode by using this command.

4. Evaluation

Evaluation is only available in branch dataset-pascal-voc

To evaluate the model, we have to test the model first and get the test results logged. After testing, there should be a directory ~/YouOnlyLookOnce-TF2.0/evaluation, under which we have the test result of each class. Then run the command to evaluate:
python main.py --mode=eval

5. Visualization

5.1. Real-time Video Detection

We have developed a simple script to visualize the detection, a webcam is required.
To perform realtime detection: \

• Place trained model under ~/YouOnlyLookOnce-TF2.0/checkpoints
• Connect a webcam and ensure the driver was installed
• Run python video_application.py under dir ~/YouOnlyLookOnce-TF2.0/src/
• The realtime video will be shown on screen

5.2. Image Detection

To perform detection on a list of image, we have provided a simple script to read the candidate image, perform detection, and sotre the file in local drive. \

To perform the detection: \

• Place trained model under ~/YouOnlyLookOnce-TF2.0/checkpoints
• Use image_application.py under dir ~/YouOnlyLookOnce-TF2.0/src/
• The generated image file will be stored in ~/YouOnlyLookOnce-TF2.0/tmp/single_images

Following is the sample command for utilizing this script: \

python image_application.py --path=/home/foo/Documents/YouOnlyLookOnce-TF2.0/test_image.jpg