Pros of Detectron2

• More comprehensive and advanced computer vision library, focusing on object detection and segmentation
• Faster training and inference times due to optimized implementation
• Extensive model zoo with pre-trained models for various tasks

Cons of Detectron2

• Steeper learning curve, requiring more in-depth knowledge of computer vision concepts
• Less beginner-friendly documentation compared to ComputerVision-Recipes
• Primarily focused on PyTorch, limiting flexibility for users preferring other frameworks

Code Comparison

Detectron2 (object detection):

cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"))
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml")
predictor = DefaultPredictor(cfg)

ComputerVision-Recipes (image classification):

from azureml.core import Workspace
from azureml.train.dnn import TensorFlow

ws = Workspace.from_config()
estimator = TensorFlow(source_directory=project_folder,
                       script_params=script_params,
                       compute_target=compute_target,
                       entry_script='train.py')

Pros of models

• Broader scope covering various ML domains beyond computer vision
• Larger community and more frequent updates
• Official TensorFlow implementation, ensuring compatibility and optimization

Cons of models

• Steeper learning curve due to its extensive codebase
• Less focused on end-to-end solutions for specific computer vision tasks
• May require more setup and configuration for specific use cases

Code Comparison

models:

import tensorflow as tf
from official.vision.image_classification import resnet_model

model = resnet_model.resnet50(num_classes=1000)

computervision-recipes:

from utils_cv.classification.model import get_model

model = get_model('resnet50', num_classes=1000, pretrained=True)

Summary

While models offers a comprehensive collection of TensorFlow implementations for various ML tasks, computervision-recipes provides more focused, end-to-end solutions for computer vision problems. The former benefits from a larger community and frequent updates, but may have a steeper learning curve. The latter offers simpler implementations for specific CV tasks but has a narrower scope. Choose based on your project requirements and familiarity with the frameworks.

Pros of YOLOv5

• Faster training and inference times
• More up-to-date with recent advancements in object detection
• Simpler architecture, making it easier to understand and modify

Cons of YOLOv5

• More focused on a single task (object detection) compared to the broader scope of Computer Vision Recipes
• Less comprehensive documentation and tutorials for beginners
• Fewer pre-trained models for diverse computer vision tasks

Code Comparison

YOLOv5:

from ultralytics import YOLO

model = YOLO('yolov5s.pt')
results = model('image.jpg')
results.show()

Computer Vision Recipes:

from azureml.contrib.services.aml_request import rawhttp
from azureml.contrib.services.aml_response import AMLResponse

@rawhttp
def run(request):
    image = request.files["image"]
    # Process image using Computer Vision Recipes
    return AMLResponse(result, 200)

The YOLOv5 code is more straightforward for object detection tasks, while Computer Vision Recipes offers a more flexible framework for various computer vision applications.

Pros of Mask_RCNN

• Focused specifically on instance segmentation, providing a deep implementation
• Includes pre-trained models on the COCO dataset
• Offers detailed documentation and examples for training and inference

Cons of Mask_RCNN

• Limited to instance segmentation tasks, less versatile than computervision-recipes
• May require more domain-specific knowledge to use effectively
• Less actively maintained, with fewer recent updates

Code Comparison

Mask_RCNN:

import mrcnn.model as modellib
from mrcnn import utils

class InferenceConfig(coco.CocoConfig):
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1

model = modellib.MaskRCNN(mode="inference", config=InferenceConfig(), model_dir=MODEL_DIR)

computervision-recipes:

from azureml.core import Workspace
from azureml.core.model import Model

ws = Workspace.from_config()
model = Model(ws, 'my_model')
model.download(target_dir=os.getcwd(), exist_ok=True)

Pros of mmdetection

• More comprehensive and specialized for object detection tasks
• Regularly updated with state-of-the-art algorithms and models
• Extensive documentation and community support

Cons of mmdetection

• Steeper learning curve for beginners
• More focused on object detection, less versatile for other computer vision 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')

computervision-recipes:

from utils_cv.detection.plot import plot_detections
from utils_cv.detection.model import faster_rcnn

model = faster_rcnn(pretrained=True)
img = Image.open("test.jpg")
outputs = model(img)
plot_detections(img, outputs[0])

Both repositories offer valuable tools for computer vision tasks, with mmdetection focusing more on object detection and providing a wider range of models and algorithms. computervision-recipes offers a broader scope of computer vision applications but may not be as specialized or up-to-date for object detection specifically.

Pros of Detectron

• More focused on object detection and segmentation tasks
• Provides pre-trained models for immediate use
• Extensive documentation and examples for various use cases

Cons of Detectron

• Limited to PyTorch framework
• Steeper learning curve for beginners
• Less comprehensive in covering other computer vision tasks

Code Comparison

Detectron2 (Detectron's successor) code snippet:

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

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

Computer Vision Recipes code snippet:

from azureml.core import Workspace
from azureml.core.model import Model

ws = Workspace.from_config()
model = Model(ws, 'my_model')
model.download(target_dir='.', exist_ok=True)

Detectron focuses on object detection configurations and predictions, while Computer Vision Recipes emphasizes Azure integration and model management. Detectron provides a more specialized toolkit for object detection tasks, whereas Computer Vision Recipes offers a broader range of computer vision solutions integrated with Azure services.