yuto3o / yolox

More Than YOLO(v3, v4, v3-tiny, v4-tiny)
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keras tensorflow yolov3 yolov3-tiny yolov4 yolov4-tiny

More Than YOLO

English | 中文

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


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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)

gif

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

A simple demo for YOLOv4

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

Model 416x416 512x512 608x608
YOLOv3 219 ms 320 ms 429 ms
YOLOv3-tiny 49 ms 63 ms 78 ms
YOLOv4 344 ms 490 ms 682 ms
YOLOv4-tiny 51 ms 66 ms 83 ms
Unofficial-YOLOv4-tiny 64 ms 86 ms 110 ms
YOLOX 67 ms 83 ms 104 ms

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

Model 416x416 512x512 608x608
YOLOv3 59 ms 66 ms 83 ms
YOLOv3-tiny 28 ms 30 ms 33 ms
YOLOv4 73 ms 74 ms 91 ms
YOLOv4-tiny 30 ms 32 ms 35 ms
Unofficial-YOLOv4-tiny 30 ms 31 ms 34 ms
YOLOX 42 ms 45 ms 50 ms

3.2 Logs

Augmentations

Name Abbr
Standard Method SM
Dynamic mini batch size DM
Label Smoothing LS
Focal Loss FL
Mosaic M
Warm-up LR W
Cosine Annealing LR CA

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)

SM DM LS FL M Loss AP AP@50 AP@75
L2 26.6 61.8 17.2
L2 27.3 62.4 17.9
L2 26.7 61.7 17.1
CIoU 30.9 64.2 25.0
CIoU 32.3 65.7 27.6
CIoU

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

SM DM LS FL M Loss AP AP@50 AP@75
CIoU 46.5 80.0 49.0
CIoU

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

SM DM LS FL M Loss AP AP@50 AP@75
CIoU 35.1 70.2 30.0
CIoU

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

SM DM LS FL M Loss AP AP@50 AP@75
CIoU
CIoU

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

SM DM LS FL M Loss AP AP@50 AP@75
CIoU 35.0 65.7 33.8
CIoU

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

SM DM LS FL M Loss AP AP@50 AP@75
CIoU 40.6 72.2 40.3
CIoU

3.3 Details

Tiny Version

Stage Freeze Backbone LR Steps
0 Yes 1e-3 (w/ W) 4000
1 Yes - 32*4000
2 No 1e-3 to 1e-6 (w/ CA) 48*4000

Common Version

Stage Freeze Backbone LR Steps
0 Yes 1e-3 (w/ W) 4000
1 Yes - 80*4000
2 No 1e-3 to 1e-6 (w/ CA) 120*4000

Training a complete network is time-consuming ...

4. Reference

5. History