Pros of Darknet (AlexeyAB)

• More frequent updates and active development
• Additional features and improvements, including support for newer GPU architectures
• Enhanced documentation and user guides

Cons of Darknet (AlexeyAB)

• Potentially more complex codebase due to additional features
• May have slightly higher system requirements for some functionalities

Code Comparison

Darknet (pjreddie):

void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{
    list *options = read_data_cfg(datacfg);
    char *train_images = option_find_str(options, "train", "data/train.list");
    char *backup_directory = option_find_str(options, "backup", "/backup/");

Darknet (AlexeyAB):

void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs, int benchmark_layers, char *chart_path)
{
    list *options = read_data_cfg(datacfg);
    char *train_images = option_find_str(options, "train", "data/train.list");
    char *backup_directory = option_find_str(options, "backup", "/backup/");

The AlexeyAB version includes additional parameters for more customization options during training.

Pros of YOLOv5

• Written in PyTorch, offering better flexibility and ease of use
• More extensive documentation and community support
• Includes features like model export, hyperparameter evolution, and multi-GPU training

Cons of YOLOv5

• Larger model size and potentially slower inference time
• Less established track record compared to Darknet's long history

Code Comparison

Darknet (C):

layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor)
{
    layer l = {0};
    l.type = CONVOLUTIONAL;
    // ... (additional initialization)
    return l;
}

YOLOv5 (Python):

class Conv(nn.Module):
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
        super().__init__()
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())

Pros of YOLOv7

• More recent and advanced YOLO implementation with improved performance
• Supports a wider range of modern deep learning techniques and architectures
• Active development and community support

Cons of YOLOv7

• More complex codebase and potentially steeper learning curve
• May require more computational resources for training and inference
• Less established than Darknet, which has been around longer

Code Comparison

Darknet (C):

layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor)
{
    layer l = {0};
    l.type = CONVOLUTIONAL;
    // ... (initialization continues)
}

YOLOv7 (Python):

class Conv(nn.Module):
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
        super().__init__()
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())

Pros of Detectron2

• Built on PyTorch, offering more flexibility and easier integration with modern deep learning ecosystems
• Provides a modular design, allowing for easier customization and extension of models
• Includes a wider range of pre-trained models and datasets out of the box

Cons of Detectron2

• Higher computational requirements and potentially slower inference speed
• Steeper learning curve due to more complex architecture and API

Code Comparison

Darknet (C):

layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor)
{
    layer l = {0};
    l.type = CONVOLUTIONAL;
    // ... (initialization continues)
}

Detectron2 (Python):

class Conv2d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
        super(Conv2d, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)

Pros of TensorFlow Models

• Broader range of pre-trained models and architectures
• Better integration with TensorFlow ecosystem and tools
• More active development and community support

Cons of TensorFlow Models

• Steeper learning curve for beginners
• Potentially slower inference compared to Darknet
• Larger codebase and more complex setup

Code Comparison

Darknet (C):

layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor)
{
    layer l = {0};
    l.type = CONVOLUTIONAL;
    // ... (additional initialization)
    return l;
}

TensorFlow Models (Python):

def conv2d(inputs, filters, kernel_size, strides=(1, 1), padding='valid', activation=None, use_bias=True, name=None):
    return tf.keras.layers.Conv2D(
        filters=filters, kernel_size=kernel_size, strides=strides,
        padding=padding, activation=activation, use_bias=use_bias, name=name
    )(inputs)

Pros of Mask_RCNN

• Provides instance segmentation in addition to object detection
• Built on top of Keras and TensorFlow, offering more flexibility and easier integration with other deep learning projects
• Includes pre-trained models on the COCO dataset

Cons of Mask_RCNN

• Generally slower inference time compared to Darknet
• Requires more computational resources for training and inference
• More complex architecture, which can be challenging for beginners

Code Comparison

Darknet (C):

image im = load_image("data/dog.jpg", 0, 0, 0);
network *net = load_network("cfg/yolov3.cfg", "yolov3.weights", 0);
detection *dets = get_network_boxes(net, im.w, im.h, 0.5, 0.5, 0, 1, &nboxes);

Mask_RCNN (Python):

import mrcnn.model as modellib
model = modellib.MaskRCNN(mode="inference", config=config, model_dir=MODEL_DIR)
model.load_weights(COCO_MODEL_PATH, by_name=True)
results = model.detect([image], verbose=1)

Both repositories offer powerful object detection capabilities, but Mask_RCNN provides additional instance segmentation features at the cost of increased complexity and computational requirements. Darknet, on the other hand, focuses on speed and efficiency, making it more suitable for real-time applications.