mmtracking

Pros of Detectron2

• More comprehensive object detection framework with broader scope
• Larger community and more extensive documentation
• Better performance and faster training on some benchmarks

Cons of Detectron2

• Steeper learning curve for beginners
• Less focused on video-based tasks like tracking
• Requires more computational resources for training

Code Comparison

MMTracking example:

from mmtrack.apis import init_model, inference_mot

config_file = 'configs/mot/deepsort/deepsort_faster-rcnn_fpn_4e_mot17-private-half.py'
checkpoint_file = 'checkpoints/deepsort_faster-rcnn_fpn_4e_mot17-private-half_20210517_001732-d066fa2a.pth'

model = init_model(config_file, checkpoint_file, device='cuda:0')
result = inference_mot(model, video_path)

Detectron2 example:

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

cfg = get_cfg()
cfg.merge_from_file("config.yaml")
cfg.MODEL.WEIGHTS = "model_weights.pth"
predictor = DefaultPredictor(cfg)
outputs = predictor(image)

Pros of YOLOv5

• Simpler architecture and easier to use for beginners
• Faster inference speed, especially on edge devices
• More extensive documentation and community support

Cons of YOLOv5

• Limited to object detection tasks, less versatile than mmtracking
• May have lower accuracy compared to more complex models in mmtracking
• Fewer pre-trained models and datasets available out-of-the-box

Code Comparison

YOLOv5:

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

mmtracking:

from mmtrack.apis import init_model, inference_mot
config_file = 'configs/mot/deepsort/deepsort_faster-rcnn_fpn_4e_mot17-private-half.py'
checkpoint_file = 'checkpoints/deepsort_faster-rcnn_fpn_4e_mot17-private-half_20210803_001244-d025f200.pth'
model = init_model(config_file, checkpoint_file, device='cuda:0')
result = inference_mot(model, 'demo/demo.mp4', output_file='output.mp4')

YOLOv5 offers a more straightforward API for quick object detection tasks, while mmtracking provides a more comprehensive framework for various tracking tasks with more complex configuration options.

Pros of darknet

• Lightweight and efficient implementation in C
• Extensive support for YOLO object detection models
• Easy to use command-line interface for training and inference

Cons of darknet

• Limited support for other computer vision tasks beyond object detection
• Less modular architecture compared to mmtracking
• Fewer pre-trained models and datasets available out-of-the-box

Code comparison

darknet:

network *net = parse_network_cfg(cfgfile);
load_weights(net, weightfile);
detection_layer l = net->layers[net->n-1];

mmtracking:

from mmtrack.apis import init_model, inference_mot

config_file = 'configs/mot/deepsort/deepsort_faster-rcnn_fpn_4e_mot17-private-half.py'
checkpoint_file = 'checkpoints/deepsort_faster-rcnn_fpn_4e_mot17-private-half_20210517_001732-d066c24b.pth'

model = init_model(config_file, checkpoint_file, device='cuda:0')
result = inference_mot(model, video_path)

The darknet code showcases its C-based implementation, while mmtracking demonstrates its Python-based, modular approach with easy-to-use APIs for various tracking tasks.

Pros of Mask_RCNN

• Focused specifically on instance segmentation, providing a simpler and more specialized implementation
• Well-documented with extensive tutorials and examples for ease of use
• Includes pre-trained models on COCO dataset, enabling quick start for transfer learning

Cons of Mask_RCNN

• Limited to instance segmentation tasks, lacking support for other computer vision tasks
• Less frequently updated compared to mmtracking, potentially missing recent advancements
• May require more manual configuration for custom datasets or tasks

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)

mmtracking:

from mmtrack.apis import init_model, inference_mot
config_file = 'configs/mot/deepsort/deepsort_faster-rcnn_fpn_4e_mot17-private-half.py'
checkpoint_file = 'checkpoints/deepsort_faster-rcnn_fpn_4e_mot17-private-half_20210517_001732-a36a1f0c.pth'
model = init_model(config_file, checkpoint_file, device='cuda:0')
result = inference_mot(model, video_file, frame_rate=30)

Pros of models

• Broader scope, covering various ML tasks beyond just tracking
• Larger community and more frequent updates
• Official TensorFlow implementation, ensuring compatibility

Cons of models

• More complex structure, potentially harder to navigate
• Less specialized for tracking tasks compared to mmtracking
• May require more setup and configuration for specific use cases

Code comparison

mmtracking:

from mmtrack.apis import init_model, inference_mot

config_file = 'configs/mot/deepsort/deepsort_faster-rcnn_fpn_4e_mot17-private-half.py'
checkpoint_file = 'checkpoints/deepsort_faster-rcnn_fpn_4e_mot17-private-half_20210517_001210-d9ab3169.pth'
model = init_model(config_file, checkpoint_file, device='cuda:0')

models:

import tensorflow as tf
from object_detection import model_lib_v2

pipeline_config = 'pipeline.config'
model_dir = 'path/to/model_dir'
config = tf.estimator.RunConfig(model_dir=model_dir)
train_and_eval_dict = model_lib_v2.create_estimator_and_inputs(
    run_config=config,
    pipeline_config_path=pipeline_config)

Pros of Detectron

• More established and widely adopted in the computer vision community
• Extensive documentation and tutorials for easier onboarding
• Broader scope, covering various object detection and segmentation tasks

Cons of Detectron

• Less focused on video-based tasks and multi-object tracking
• May require more setup and configuration for specific tracking tasks
• Not as actively maintained as mmtracking (last update was in 2019)

Code Comparison

mmtracking:

from mmtrack.apis import init_model, inference_mot

config_file = 'configs/mot/deepsort/deepsort_faster-rcnn_fpn_4e_mot17-private-half.py'
checkpoint_file = 'checkpoints/deepsort_faster-rcnn_fpn_4e_mot17-private-half_20210517_001732-d066fa53.pth'

model = init_model(config_file, checkpoint_file, device='cuda:0')
result = inference_mot(model, video_path, frame_rate=30)

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)