mmdetection

Pros of Detectron2

• Faster training and inference speeds
• More extensive documentation and tutorials
• Tighter integration with PyTorch ecosystem

Cons of Detectron2

• Steeper learning curve for beginners
• Less flexibility in customizing model architectures
• Fewer pre-trained models available out-of-the-box

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')

Detectron2:

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

cfg = get_cfg()
cfg.merge_from_file("configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml")
cfg.MODEL.WEIGHTS = "detectron2://COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl"
predictor = DefaultPredictor(cfg)
outputs = predictor(image)

Both frameworks offer powerful object detection capabilities, but Detectron2 excels in performance and PyTorch integration, while MMDetection provides more flexibility and a gentler learning curve for newcomers to the field.

Pros of YOLOv5

• Simpler architecture and easier to use for beginners
• Faster training and inference times
• More lightweight and suitable for edge devices

Cons of YOLOv5

• Less flexible for customization and advanced use cases
• Limited to YOLO-based architectures
• Smaller community and fewer pre-trained models

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')

YOLOv5:

import torch

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

MMDetection offers a more modular and customizable approach, while YOLOv5 provides a simpler, more streamlined implementation for object detection tasks.

Pros of models

• Broader scope, covering various ML tasks beyond object detection
• Officially maintained by Google, ensuring long-term support and updates
• Extensive documentation and tutorials for beginners

Cons of models

• Less specialized for object detection compared to mmdetection
• Steeper learning curve due to its broader focus
• May require more setup and configuration for specific tasks

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')

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')
image = tf.image.decode_jpeg(tf.io.read_file('test.jpg'), channels=3)
detections = model(tf.expand_dims(image, axis=0))

Both repositories offer powerful tools for object detection, but mmdetection is more specialized and easier to use for this specific task, while models provides a broader range of ML capabilities with official Google support.

Pros of Mask_RCNN

• Simpler implementation, easier to understand and modify
• Focused specifically on instance segmentation tasks
• Well-documented with detailed explanations and examples

Cons of Mask_RCNN

• Less flexible and fewer features compared to mmdetection
• Not actively maintained, with fewer recent updates
• Limited to Mask R-CNN architecture, while mmdetection supports multiple models

Code Comparison

Mask_RCNN:

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)

mmdetection:

from mmdet.apis import init_detector, inference_detector

config_file = 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
checkpoint_file = 'checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth'
model = init_detector(config_file, checkpoint_file, device='cuda:0')
result = inference_detector(model, img)

Pros of Detectron

• Developed and maintained by Facebook AI Research, benefiting from their expertise
• Extensive documentation and tutorials for ease of use
• Strong integration with PyTorch and Caffe2

Cons of Detectron

• Less frequent updates compared to MMDetection
• Smaller community and fewer third-party contributions
• More limited range of pre-trained models and algorithms

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')

Detectron:

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

cfg = get_cfg()
cfg.merge_from_file("configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml")
cfg.MODEL.WEIGHTS = "detectron2://COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
predictor = DefaultPredictor(cfg)
outputs = predictor(image)

Both repositories offer powerful object detection frameworks, with MMDetection providing more frequent updates and a wider range of models, while Detectron benefits from Facebook's expertise and integration with PyTorch and Caffe2.

Pros of darknet

• Lightweight and fast, optimized for real-time object detection
• Supports both CPU and GPU computation
• Easy to use with pre-trained models and simple configuration files

Cons of darknet

• Limited model architectures (primarily YOLO-based)
• Less extensive documentation and community support
• Fewer pre-trained models and datasets available

Code comparison

darknet:

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

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')

Summary

darknet is a lightweight and fast object detection framework, primarily focused on YOLO-based models. It's easy to use but has limited model options and community support. mmdetection offers a wider range of models and better documentation but may be more complex to set up and use.