yolov5

Pros of darknet

• Supports a wider range of YOLO versions (YOLOv3, YOLOv4, etc.)
• Offers more customization options for advanced users
• Implements some additional features like Gaussian YOLOv3

Cons of darknet

• Less user-friendly, especially for beginners
• Slower development cycle and less frequent updates
• More complex setup process compared to YOLOv5

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;

YOLOv5:

class YOLOLayer(nn.Module):
    def __init__(self, anchors, nc, img_size, yolo_index, layers, stride):
        super(YOLOLayer, self).__init__()
        self.anchors = torch.Tensor(anchors)

The darknet implementation is in C, while YOLOv5 uses Python with PyTorch. YOLOv5's code is generally more readable and easier to modify for most users familiar with modern deep learning frameworks. However, darknet's C implementation may offer performance benefits in certain scenarios.

Pros of YOLOv7

• Improved accuracy and performance over YOLOv5
• Incorporates advanced techniques like compound scaling and dynamic label assignment
• Supports additional tasks like instance segmentation and pose estimation

Cons of YOLOv7

• Less extensive documentation and community support compared to YOLOv5
• Fewer pre-trained models and configurations available
• Steeper learning curve for implementation and customization

Code Comparison

YOLOv5:

from yolov5 import YOLOv5

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

YOLOv7:

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)

Both repositories offer powerful object detection capabilities, but YOLOv7 provides improved accuracy and additional features at the cost of a steeper learning curve and less extensive community support. YOLOv5 remains more accessible and widely adopted, while YOLOv7 pushes the boundaries of performance and versatility.

Pros of Detectron2

• More comprehensive and flexible framework for object detection and segmentation
• Supports a wider range of models and architectures
• Better integration with PyTorch ecosystem

Cons of Detectron2

• Steeper learning curve and more complex setup
• Slower inference speed compared to YOLOv5
• Requires more computational resources for training and inference

Code Comparison

YOLOv5:

from yolov5 import YOLOv5

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

Detectron2:

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

cfg = get_cfg()
cfg.merge_from_file("config.yaml")
predictor = DefaultPredictor(cfg)
outputs = predictor(image)

YOLOv5 offers a simpler API for quick implementation, while Detectron2 provides more flexibility and customization options. YOLOv5 is better suited for real-time applications and edge devices, whereas Detectron2 excels in research and complex computer vision tasks. The choice between the two depends on the specific requirements of your project, balancing factors such as ease of use, performance, and adaptability.

Pros of YOLOv6

• Improved accuracy and speed compared to YOLOv5
• Optimized for edge devices and mobile platforms
• Includes advanced features like anchor-free detection and SimOTA label assignment

Cons of YOLOv6

• Less extensive documentation and community support
• Fewer pre-trained models available
• Limited flexibility for custom architectures

Code Comparison

YOLOv5:

from models.yolo import Model
model = Model('yolov5s.yaml')
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 efficient object detection implementations, but YOLOv5 provides a more established ecosystem with extensive documentation and community support. YOLOv6, on the other hand, focuses on improved performance and optimization for specific use cases, particularly on edge devices. The code structure differs slightly, with YOLOv6 using an Inferer class for inference, while YOLOv5 allows direct model instantiation and inference. Users should consider their specific requirements and deployment scenarios when choosing between these two options.

Pros of MMDetection

• Extensive model library with support for various object detection algorithms
• Highly modular and customizable architecture
• Comprehensive documentation and tutorials

Cons of MMDetection

• Steeper learning curve due to complex architecture
• Slower inference speed compared to YOLOv5
• Larger codebase and potentially higher resource requirements

Code Comparison

MMDetection configuration example:

model = dict(
    type='FasterRCNN',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch'),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='StandardRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=80,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0., 0., 0., 0.],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
    # model training and testing settings
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=

Pros of models

• Broader scope: Covers a wide range of machine learning models and tasks
• Official TensorFlow support: Maintained by Google's TensorFlow team
• Extensive documentation and tutorials

Cons of models

• Steeper learning curve: More complex structure due to diverse models
• Potentially slower inference: Not optimized specifically for object detection

Code comparison

models:

import tensorflow as tf
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils

model = tf.saved_model.load('path/to/saved_model')
detect_fn = model.signatures['serving_default']

YOLOv5:

import torch

model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
results = model('image.jpg')
results.print()

Key differences

• models provides a comprehensive suite of machine learning models, while YOLOv5 focuses specifically on object detection
• YOLOv5 offers simpler implementation and faster inference for object detection tasks
• models integrates seamlessly with TensorFlow ecosystem, while YOLOv5 uses PyTorch
• YOLOv5 provides easier deployment options, including mobile and edge devices
• models offers more flexibility for custom model architectures and research purposes