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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
This repository have done:
[toc]
You can get official weight files from https://github.com/AlexeyAB/darknet/releases or https://pjreddie.com/darknet/yolo/.
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.
# 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"
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'
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
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()
!!! Please Read the above guide (e.g. 1.1, 1.2).
python train.py --config=./cfgs/coco_yolov4.yaml
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 |
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 |
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 |
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 ...