YOLOv4_tensorflow | 中文说明

• Implement yolov4 with tensorflow1

• tf.data.pipline

• [email protected]

introductions

• run the following command.

python val_voc.py

• if have no error, it's ok

converts yolov4.weights to fitting our code

• refer to this weights convert file, I'm already converted yolov4.weights to this project.
• you can download yolov4.weights from baidu cloud Extraction code: wm1j
• puting yolov4.weights into the "yolo_weights" folder, and run the command.

python convert_weight.py
python test_yolo_weights.py

• the ckpt weights file wound exits in the 'yolo_weights' folder(exists in the baidu cloud)
• and you'll see some images like this, it seems perfect

* * *

• the weights_name.txt contains all layer's name of the network

training on VOC2007 and VOC2012

• open config.py and modify the voc_root_dir to the root path of your VOC dataset
• just like this

path_to_voc_root_dir
        voc_dir_ls[0] (VOC_2007)
        |       |Annotations
        |       |JPEGImages
        |       |...
        voc_dir_ls[1] (VOC_2012)
        |        |Annotations
        |        |JPEGImages
        |        |...
        others_folder_name

• run the command

python train_voc.py

• put test images into voc_test_pic folder, and run the following command after the model training finished.

python val_voc.py

• it's the result of our code training(input_size:416*416, batch_size:2, lr:2e-4, optimizer:momentum) for a day(364999 steps), not bad

* *

• all configuration parameters are in the config.py, you can modify them according to your actual situation
• it's the image of loss value, and seems that the lr is too lower(2e-4), we should set it larger.

python show_loss.py 20 300

training on VOC2007 and VOC2012 with tf.data pipline

• just same with training on VOC2007 and VOC2012 but run training file

train_voc_tf_data.py

training on own dataset

• On January 5, 2021, the related code of training own dataset was deleted, and if you want to training your own dataset, following src/Data_voc.py and just modify the __init_args function, it will be working great

converts ckpt model to pb model

• open ckpt2pb.py , and modify 'ckpt_file_dir', 'class_num', 'anchors'. run.

python ckpt2pb.py

• you will see a pb model named 'model.pb' in 'ckpt_file_dir'
• if you want to use pb model, you will learn more from 'val_pb.py'
• run python file

python val_pb.py

• you will see the detection results with pb model
• you can download pb model from baidu cloud Extraction code: wm1j

some tips about config.py and training the model

1. the parameters of width and height in config.py should be 608, but i have not a powerful GPU, that is why i set them as 416
2. actually, you can replace mish activation with leaky_relu to save GPU memory, and then you can set batch_size from 2 to 4
3. learning rate do not set too large
4. when the loss value is Nan, please lower your learning rate.

my device

GPU : 1660ti (ASUS) 6G CPU : i5 9400f mem : 16GB os : ubuntu 18.04 cuda: 10.2 cudnn : 7 python : 3.6.9 tensorflow-gpu:1.14.0 numpy : 1.18.1 opencv-python : 4.1.2.30