Pros of YOLOv5

• More established and widely adopted in the community
• Extensive documentation and tutorials available
• Easier to use and integrate into existing projects

Cons of YOLOv5

• Slightly lower accuracy compared to YOLOv7
• May have slower inference speed on certain hardware configurations

Code Comparison

YOLOv5:

from yolov5 import YOLOv5

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

YOLOv7:

from yolov7 import YOLOv7

model = YOLOv7('yolov7.pt')
results = model.detect('image.jpg')

Both repositories offer similar functionality for object detection, but YOLOv7 aims to provide improved accuracy and performance. YOLOv5 benefits from a larger community and more extensive documentation, making it easier for beginners to get started. However, YOLOv7 may offer better performance in certain scenarios, especially for advanced users who require state-of-the-art accuracy.

The code comparison shows that both libraries have similar usage patterns, with minor differences in method names and initialization. Users familiar with one library should find it relatively easy to switch to the other if needed.

Pros of darknet

• More established and mature project with a longer history
• Supports a wider range of YOLO versions (v2, v3, v4, etc.)
• Offers more comprehensive documentation and examples

Cons of darknet

• Less focused on the latest YOLO architectures
• May have slower inference speed compared to yolov7
• Requires more manual configuration and setup

Code Comparison

darknet:

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;

yolov7:

class YOLOv7(nn.Module):
    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
        super(YOLOv7, self).__init__()
        self.nc = nc  # number of classes

The darknet implementation is in C, while yolov7 uses Python with PyTorch. yolov7 offers a more modern and flexible approach, leveraging deep learning frameworks for easier development and deployment.

Pros of YOLOv6

• Better performance on edge devices due to its lightweight design
• Faster inference speed, especially on mobile platforms
• More frequent updates and active development from Meituan

Cons of YOLOv6

• Less versatile compared to YOLOv7, with fewer pre-trained models
• Lower accuracy on certain datasets, particularly for larger objects
• Limited documentation and community support compared to YOLOv7

Code Comparison

YOLOv6:

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

model = Inferer(weights='yolov6s.pt', device='cpu')
results = model.infer(source='image.jpg', conf_thres=0.25, iou_thres=0.45)

YOLOv7:

from models.experimental import attempt_load
from utils.general import non_max_suppression

model = attempt_load('yolov7.pt', map_location='cpu')
results = non_max_suppression(model(img), conf_thres=0.25, iou_thres=0.45)

Both repositories offer state-of-the-art object detection capabilities, but they cater to different use cases. YOLOv6 is more suitable for edge devices and mobile applications due to its lightweight design and faster inference speed. On the other hand, YOLOv7 provides better accuracy and versatility, making it more appropriate for a wider range of applications where computational resources are less constrained.

Pros of YOLOX

• Modular design allows for easier customization and experimentation
• Includes anchor-free detection, which can improve performance on small objects
• Offers a range of pre-trained models for different use cases

Cons of YOLOX

• May have slightly lower performance on some benchmarks compared to YOLOv7
• Less extensive documentation and community support

Code Comparison

YOLOX:

from yolox.exp import Exp as MyExp

class Exp(MyExp):
    def __init__(self):
        super(Exp, self).__init__()
        self.depth = 0.33
        self.width = 0.50
        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

YOLOv7:

from models.yolo import Model
from utils.torch_utils import select_device

device = select_device('0')
model = Model('yolov7.yaml', ch=3, nc=80).to(device)
model.train()

Both repositories offer powerful object detection capabilities, but YOLOX provides a more modular approach, while YOLOv7 focuses on pushing the boundaries of performance. YOLOX's anchor-free detection can be advantageous in certain scenarios, while YOLOv7 may offer better overall performance on standard benchmarks. The choice between the two depends on specific project requirements and preferences.

Pros of Detectron2

• More comprehensive and flexible framework for object detection and segmentation
• Supports a wider range of models and tasks (e.g., instance segmentation, keypoint detection)
• Better documentation and community support

Cons of Detectron2

• Generally slower inference speed compared to YOLOv7
• Steeper learning curve and more complex setup process
• Requires more computational resources for training and inference

Code Comparison

Detectron2 example:

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)

YOLOv7 example:

from models.experimental import attempt_load
from utils.general import non_max_suppression

model = attempt_load('yolov7.pt')
pred = model(img)[0]
pred = non_max_suppression(pred)