YOLOv6

Pros of YOLOv5

• More established and widely adopted in the community
• Extensive documentation and tutorials available
• Broader range of pre-trained models for various tasks

Cons of YOLOv5

• Slightly lower inference speed compared to YOLOv6
• May require more computational resources for training

Code Comparison

YOLOv5:

from yolov5 import YOLOv5

model = YOLOv5('yolov5s.pt')
results = model('image.jpg')

YOLOv6:

from yolov6.core.inferer import Inferer

inferer = Inferer(model='yolov6s.pt', device='cpu')
results = inferer.infer('image.jpg')

Both repositories offer similar ease of use for inference, with YOLOv5 having a slightly more straightforward API. YOLOv5 provides a more comprehensive ecosystem with additional tools and utilities, while YOLOv6 focuses on improved speed and efficiency.

YOLOv5 is better suited for users who prioritize community support and a wide range of pre-trained models. YOLOv6 may be preferred by those seeking cutting-edge performance and faster inference times, especially on edge devices or in real-time applications.

Pros of YOLOv7

• Higher accuracy and performance on benchmark datasets
• More extensive documentation and community support
• Includes additional features like instance segmentation

Cons of YOLOv7

• Larger model size, potentially slower inference
• More complex architecture, which may be harder to understand and modify
• Less focus on mobile and edge device deployment

Code Comparison

YOLOv7:

class YOLOv7(nn.Module):
    def __init__(self, nc=80):
        super().__init__()
        self.model = parse_model(yaml_file, ch=[3])
        self.nc = nc

YOLOv6:

class YOLOv6(nn.Module):
    def __init__(self, config, channels=3, num_classes=None, anchors=None):
        super().__init__()
        self.backbone = build_backbone(config, channels, False)
        self.neck = build_neck(config)

The code snippets show that YOLOv7 uses a YAML file for model configuration, while YOLOv6 uses a more modular approach with separate backbone and neck components. YOLOv7's implementation may be more flexible, but YOLOv6's structure could be easier to customize for specific use cases.

Both repositories offer state-of-the-art object detection capabilities, with YOLOv7 focusing on high performance and additional features, while YOLOv6 emphasizes efficiency and mobile deployment. The choice between them depends on specific project requirements and hardware constraints.

Pros of darknet

• More established and widely used, with a larger community and extensive documentation
• Supports a broader range of YOLO versions (YOLOv3, YOLOv4, etc.)
• Offers pre-trained models for various tasks and datasets

Cons of darknet

• Written in C, which may be less accessible for some developers compared to YOLOv6's Python implementation
• Generally slower inference speed compared to YOLOv6's optimized architecture
• Less focus on mobile and edge device deployment

Code Comparison

darknet (C):

layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes)
{
    int i;
    layer l = {0};
    l.type = YOLO;

    l.n = n;
    l.total = total;
    l.batch = batch;
    l.h = h;
    l.w = w;
    l.c = n*(classes + 4 + 1);
    l.out_w = l.w;
    l.out_h = l.h;
    l.out_c = l.c;
    l.classes = classes;
    l.cost = calloc(1, sizeof(float));
    l.biases = calloc(total*2, sizeof(float));
    if(mask) l.mask = mask;
    else{
        l.mask = calloc(n, sizeof(int));
        for(i = 0; i < n; ++i){
            l.mask[i] = i;
        }
    }
    l.bias_updates = calloc(n*2, sizeof(float));
    l.outputs = h*w*n*(classes + 4 + 1);
    l.inputs = l.outputs;
    l.truths = 90*(4 + 1);
    l.delta = calloc(batch*l.outputs, sizeof(float));
    l.output = calloc(batch*l.outputs, sizeof(float));

    l.forward = forward_yolo_layer;
    l.backward = backward_yolo_layer;
#ifdef GPU
    l.forward_gpu = forward_yolo_layer_gpu;
    l.backward_gpu = backward_yolo_layer_gpu;
    l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
    l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
#endif

    fprintf(stderr, "yolo\n");
    srand(0);

    return l;
}

YOLOv6 (Python):

class YOLOv6(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.backbone = build_backbone(config)
        self.neck = build_neck(config)
        self.head = build_head(config)

    def forward(self, x):
        x = self.backbone(x)
        x = self.neck(x)
        x = self.head(x)
        return x

Pros of mmdetection

• Extensive model zoo with a wide variety of pre-trained models
• Highly modular and customizable architecture
• Comprehensive documentation and active community support

Cons of mmdetection

• Steeper learning curve due to its complexity
• Potentially slower inference speed compared to YOLOv6
• Larger codebase and higher resource requirements

Code Comparison

mmdetection:

from mmdet.apis import init_detector, inference_detector

config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
model = init_detector(config_file, checkpoint_file, device='cuda:0')
result = inference_detector(model, 'test.jpg')

YOLOv6:

from yolov6.utils.events import LOGGER
from yolov6.core.inferer import Inferer

model = Inferer('yolov6s.pt', device='cuda:0')
img = 'test.jpg'
result = model.infer(img, conf_thres=0.25, iou_thres=0.45, classes=None)

Pros of Detectron2

• More comprehensive and flexible framework for object detection, segmentation, and other CV tasks
• Extensive documentation and community support
• Built on PyTorch, offering easier customization and integration with other PyTorch models

Cons of Detectron2

• Steeper learning curve due to its complexity and extensive features
• Generally slower inference speed compared to YOLOv6
• Requires more computational resources for training and inference

Code Comparison

Detectron2:

from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg

cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"))
predictor = DefaultPredictor(cfg)
outputs = predictor(image)

YOLOv6:

from yolov6.utils.events import LOGGER, load_yaml
from yolov6.core.inferer import Inferer

cfg = load_yaml('configs/yolov6s.py')
inferer = Inferer(cfg, img_size=640)
results = inferer.infer(img_path)

Pros of YOLOR

• Implements a unified network for object detection, offering potential performance improvements
• Provides pre-trained models for various tasks, including object detection and instance segmentation
• Supports multiple backbones and offers flexibility in model architecture

Cons of YOLOR

• Less frequent updates and maintenance compared to YOLOv6
• May have higher computational requirements due to its unified network approach
• Documentation could be more comprehensive for easier implementation and customization

Code Comparison

YOLOv6:

from yolov6.core.evaler import Evaler
from yolov6.utils.config import Config

cfg = Config.fromfile('configs/yolov6s.py')
evaler = Evaler(cfg, img_size=640)
evaler.eval()

YOLOR:

from yolor.models.models import *
from yolor.utils.datasets import *
from yolor.utils.utils import *

model = Darknet('cfg/yolor_p6.cfg', img_size=640)
model.load_state_dict(torch.load('yolor_p6.pt')['model'])

Both repositories offer YOLO-based object detection models, but YOLOv6 focuses on efficiency and deployment optimization, while YOLOR emphasizes a unified network approach. YOLOv6 provides more recent updates and extensive documentation, making it potentially easier to implement and customize for specific use cases.