More Than YOLO

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TensorFlow & Keras & Python

YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny

[Unofficial] YOLOv4-tiny, YOLOX

requirements: TensorFlow 2.1 (not test on 1.x), OpenCV, Numpy, PyYAML

News !

• Small batch size is used, because available GPU (8G) has small memory. Please use big batch size as possible.
• Online High Level augmentation will slow down training speed.
• When I tried to train yolov3 or yolov4, 'NaN' problem made me crazy.
• Support AccumOptimizer, Similar to 'subdivisions' in darknet.
• When I tried to train yolov3 or yolov4, I found that if I set weight decay to 5e-4，the result is unsatisfactory; if I set it to 0, everything is OK.
• Note that both yolov3-tiny and yolov4-tiny don't use anchor 0, so they use only anchors 1-7.[link]

This repository have done:

• [x] Backbone for YOLO (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, Unofficial-YOLOv4-tiny)
• [x] YOLOv3 Head
• [x] Keras Callbacks for Online Evaluation
• [x] Load Official Weight File
• [x] Data Format Converter(COCO and Pascal VOC)
• [x] K-Means for Anchors
• [x] Fight with 'NaN'
• [x] Train (Strategy and Model Config)
  • Define simple training in train.py
  • Use YAML as config file in cfgs
  • [x] Cosine Annealing LR
  • [x] Warm-up
  • [x] AccumOptimizer
• [ ] Data Augmentation
  • [x] Standard Method: Random Flip, Random Crop, Zoom, Random Grayscale, Random Distort, Rotate
  • [x] Hight Level: Cut Mix, Mix Up, Mosaic （These Online Augmentations is Slow）
  • [ ] More, I can be so much more ...
• [ ] For Loss
  • [x] Label Smoothing
  • [x] Focal Loss
  • [x] L2, D-IoU, G-IoU, C-IoU
  • [ ] ...

[toc]

0. Please Read Source Code for More Details

You can get official weight files from https://github.com/AlexeyAB/darknet/releases or https://pjreddie.com/darknet/yolo/.

1. Samples

1.1 Data File

We use a special data format like that,

path/to/image1 x1,y1,x2,y2,label x1,y1,x2,y2,label 
path/to/image2 x1,y1,x2,y2,label 
...

Convert your data format firstly. We present a script for Pascal VOC and a script for COCO.

More details and a simple dataset could be got from https://github.com/YunYang1994/yymnist.

1.2 Configure

# coco_yolov4.yaml
yolo:
  type: "yolov4"  # must be 'yolov3', 'yolov3_tiny', 'yolov4', 'yolov4_tiny' ‘unofficial_yolov4_tiny’ and 'yolox'.
  iou_threshold: 0.5
  score_threshold: 0.005
  max_boxes: 100
  strides: "32,16,8"
  anchors: "12,16 19,36 40,28 36,75 76,55 72,146 142,110 192,243 459,401"
  mask: "6,7,8 3,4,5 0,1,2"
  name_path: "./data/coco/coco.name"

train:
  label: "coco_yolov4" # any thing you like
  anno_path: "./data/coco/train2017.txt"
  image_size: "320,352,384,416,448,480,512,544,576,608"  # "416" for single mini batch size, "352,384,416,448,480" for Dynamic mini batch size.

  batch_size: 4
  init_weight_path: "./ckpts/yolov4.weights"
  save_weight_path: "./ckpts"

  loss_type: "CIoU+FL" # Must be "L2", "DIoU", "GIoU", "CIoU" or something like "L2+FL" for focal loss
  
  # turn on hight level data augmentation
  mosaic: false
  label_smoothing: false
  normal_method: true

  ignore_threshold: 0.7

test:
  anno_path: "./data/coco/val2017.txt"
  image_size: "608"
  batch_size: 1
  init_weight_path: "./ckpts/yolov4.weights"

1.3 K-Means

Edit the kmeans.py as you like

# kmeans.py
# Key Parameters
K = 6 # num of clusters
image_size = 416
dataset_path = './data/pascal_voc/train.txt'

1.4 Inference

A Simple Script for Video, Device or Image

Only support mp4, avi, device id, rtsp, png, jpg (Based on OpenCV)

python detector.py --config=./cfgs/coco_yolov4.yaml --media=./misc/street.mp4 --gpu=false

A simple demo for YOLOv4

from core.utils import decode_cfg, load_weights
from core.model.one_stage.yolov4 import YOLOv4
from core.image import draw_bboxes, preprocess_image, postprocess_image, read_image, Shader

import numpy as np
import cv2
import time

# read config
cfg = decode_cfg('cfgs/coco_yolov4.yaml')
names = cfg['yolo']['names']

model, eval_model = YOLOv4(cfg)
eval_model.summary()

# assign colors for difference labels
shader = Shader(cfg['yolo']['num_classes'])

# load weights
load_weights(model, cfg['test']['init_weight_path'])

img_raw = read_image('./misc/dog.jpg')
img = preprocess_image(img_raw, (512, 512))
imgs = img[np.newaxis, ...]

tic = time.time()
boxes, scores, classes, valid_detections = eval_model.predict(imgs)
toc = time.time()
print((toc - tic)*1000, 'ms')

# for single image, batch size is 1
valid_boxes = boxes[0][:valid_detections[0]]
valid_score = scores[0][:valid_detections[0]]
valid_cls = classes[0][:valid_detections[0]]

img, valid_boxes = postprocess_image(img, img_raw.shape[1::-1], valid_boxes)
img = draw_bboxes(img, valid_boxes, valid_score, valid_cls, names, shader)

cv2.imshow('img', img[..., ::-1])
cv2.imwrite('./misc/dog_v4.jpg', img)
cv2.waitKey()

2. Train

!!! Please Read the above guide (e.g. 1.1, 1.2).

python train.py --config=./cfgs/coco_yolov4.yaml

3. Experiment

3.1 Speed

i7-9700F+16GB

i7-9700F+16GB / RTX 2070S+8G

3.2 Logs

Augmentations

Standard Method Package includes Flip left and right, Crop and Zoom(jitter=0.3), Grayscale, Distort, Rotate(angle=7).

YOLOv3-tiny(Pretrained on COCO; Trained on VOC)

YOLOv3(TODO; Pretrained on COCO; Trained on VOC; only 15 epochs)

YOLOv4-tiny(TODO; Pretrained on COCO; Trained on VOC)

YOLOv4(TODO; Pretrained on COCO; Trained on VOC)

Unofficial-YOLOv4-tiny(TODO; Pretrained on COCO, part of YOLOv3-tiny weights; Trained on VOC)

YOLOX(TODO; Pretrained on COCO, part of YOLOv4-tiny weights; Trained on VOC)

3.3 Details

Tiny Version

Common Version

Training a complete network is time-consuming ...

4. Reference

• https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/4-Object_Detection/YOLOV3
• https://github.com/hunglc007/tensorflow-yolov4-tflite
• https://github.com/experiencor/keras-yolo3

5. History

• Slim version: https://github.com/yuto3o/yolox/tree/slim
• Tensorflow2.0-YOLOv3: https://github.com/yuto3o/yolox/tree/yolov3-tf2