remhardt / yet-another-yolov4-pytorch

YOLOv4 Pytorch implementation with all freebies and specials.

Yet-Another-YOLOv4-Pytorch

This is implementation of YOLOv4 object detection neural network on pytorch. I'll try to implement all features of original paper.

• Model
• Pretrained weights
• Custom classes
• CIoU
• YOLO dataset
• Letterbox for validation
• HSV transforms for train
• MOSAIC for train
• Dropblock layers for training
• Self attention attack
• Notebook with guide
• MiWRC for forward attention (Was not implemented in official implementation, neck should be changed from PAN to BiFPN)

What you can already do

You can use video_demo.py to take a look at the original weights realtime OD detection. (Have 9 fps on my GTX1060 laptop!!!)

You can train your own model with mosaic augmentation for training. Guides how to do this are written below. Borders of images on some datasets are even hard to find.

You can make inference, guide bellow.

Initialize NN

#YOU CAN USE TORCH HUB
m = torch.hub.load("VCasecnikovs/Yet-Another-YOLOv4-Pytorch", "yolov4", pretrained=True)

import model
#If you change n_classes from the pretrained, there will be caught one error, don't panic it is ok

#FROM SAVED WEIGHTS
m = model.YOLOv4(n_classes=1, weights_path="weights/yolov4.pth")

#AUTOMATICALLY DOWNLOAD PRETRAINED
m = model.YOLOv4(n_classes=1, pretrained=True)

Download weights

You can use torch hub or you can download weights using from this link: https://drive.google.com/open?id=12AaR4fvIQPZ468vhm0ZYZSLgWac2HBnq

Initialize dataset

import dataset
d = dataset.ListDataset("train.txt", img_dir='images', labels_dir='labels', img_extensions=['.JPG'], train=True)
path, img, bboxes = d[0]

"train.txt" is file which consists filepaths to image (images\primula\DSC02542.JPG)

img_dir - Folder with images labels_dir - Folder with txt files for annotion img_extensions - extensions if images

If you set train=False -> uses letterboxes If you set train=True -> HSV augmentations and mosaic

dataset has collate_function

# collate func example
y1 = d[0]
y2 = d[1]
paths_b, xb, yb = d.collate_fn((y1, y2))
# yb has 6 columns

Y's format

1. Num of img to which this anchor belongs
2. BBox class
3. x center
4. y center
5. width
6. height

Forward with loss

y_hat, loss = m(xb, yb)

!!! y_hat is already resized anchors to image size bboxes

Forward without loss

y_hat,  _ = m(img_batch) #_ is (0, 0, 0)

Check if bboxes are correct

import utils
from PIL import Image
path, img, bboxes = d[0]
img_with_bboxes = utils.get_img_with_bboxes(img, bboxes[:, 2:]) #Returns numpy array
Image.fromarray(img_with_bboxes)

Get predicted bboxes

anchors, loss = m(xb.cuda(), yb.cuda())
confidence_threshold = 0.05
iou_threshold = 0.5
bboxes, labels = utils.get_bboxes_from_anchors(anchors, confidence_threshold, iou_threshold, coco_dict) #COCO dict is id->class dictionary (f.e. 0->person)
#For first img
arr = utils.get_img_with_bboxes(xb[0].cpu(), bboxes[0].cpu(), resize=False, labels=labels[0])
Image.fromarray(arr)