YOLOv1

Reimplementation of YOLOv1 with a resnet50 backbone in Pytorch (wip).

Requirment

albumentations 0.4.4
imgaug 0.2.6
numpy 1.17.3
opencv-python 4.1.2.30
scikit-image 0.16.2
scipy 1.4.1
tensorboard 2.0.0
pytorch 1.3.1
torchvision 0.4.2
tqdm 4.40.0

Train

• Open train.py
• go to main function and adapt to your need and dataset emplacement below lines:

 # Train and test files
voc_2007 = "/home/layely/Myprojects/datasets/VOCtrainval_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages"
voc_2012 = "/home/layely/Myprojects/datasets/VOCtrainval_11-May-2012/VOCdevkit/VOC2012/JPEGImages"
train_images_dir = [voc_2007, voc_2012]
train_files = ["voc_2007.txt", "voc_2012.txt"]
voc_2007_test = "/home/layely/Myprojects/datasets/VOCtest_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages"
test_images_dir = [voc_2007_test]
test_files = ["voc_2007_test.txt"]

channels, height, width = (3, 448, 448)
S = 7  # SxS grid cells
B = 2  # Number of bounding boxes per cell
C = 20  # Number of classes

# Training hyperparameters
epochs = 160
lr = 0.01
momentum = 0.9
weight_decay = 5e-4
opt = torch.optim.SGD
batch_size = 24 * num_gpu


# Image normalization parameters
# Note that images are squished to
# the range [0, 1] before normalization
mean = [0.485, 0.456, 0.406] # RGB - Imagenet means
std = [0.229, 0.224, 0.225] # RGB - Imagenet standard deviations

...
* run train.py

Test results

• open test.py and adapt:

voc_2007_test = "/home/layely/Myprojects/datasets/VOCtest_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages"
test_images_dir = [voc_2007_test]
test_files = ["voc_2007_test.txt"]

• run test.py
• result will be saved in ./test_results