mmsegmentation

Pros of Detectron2

• More comprehensive object detection and instance segmentation capabilities
• Faster training and inference times due to optimized CUDA implementations
• Extensive pre-trained model zoo for various tasks

Cons of Detectron2

• Steeper learning curve for beginners due to its complexity
• Less focus on semantic segmentation compared to MMSegmentation
• Requires more computational resources for training and inference

Code Comparison

MMSegmentation:

from mmseg.apis import inference_segmentor, init_segmentor

config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'
checkpoint_file = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'

model = init_segmentor(config_file, checkpoint_file, device='cuda:0')
result = inference_segmentor(model, img)

Detectron2:

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

cfg = get_cfg()
cfg.merge_from_file("configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
cfg.MODEL.WEIGHTS = "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl"
predictor = DefaultPredictor(cfg)
outputs = predictor(img)

Pros of models

• Broader scope, covering various ML tasks beyond segmentation
• Official TensorFlow repository with extensive documentation
• Large community and ecosystem support

Cons of models

• Less specialized for segmentation tasks
• Potentially more complex to navigate for specific use cases
• May require more setup and configuration for segmentation projects

Code comparison

mmsegmentation:

from mmseg.apis import inference_segmentor, init_segmentor

config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'
checkpoint_file = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'

model = init_segmentor(config_file, checkpoint_file, device='cuda:0')
result = inference_segmentor(model, img)

models:

import tensorflow as tf
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.layers import Conv2D, UpSampling2D

base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(None, None, 3))
x = Conv2D(256, (3, 3), padding='same', activation='relu')(base_model.output)
x = UpSampling2D((2, 2))(x)
outputs = Conv2D(num_classes, (1, 1), activation='softmax')(x)

Pros of vision

• Broader scope, covering various computer vision tasks beyond segmentation
• Tighter integration with PyTorch ecosystem and core functionality
• More extensive documentation and tutorials for beginners

Cons of vision

• Less specialized for segmentation tasks compared to mmsegmentation
• Fewer pre-trained models specifically for semantic segmentation
• May require more custom code for advanced segmentation workflows

Code Comparison

mmsegmentation example:

from mmseg.apis import inference_segmentor, init_segmentor

config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'
checkpoint_file = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'

model = init_segmentor(config_file, checkpoint_file, device='cuda:0')
result = inference_segmentor(model, 'demo.png')

vision example:

import torchvision.models.segmentation as models

model = models.fcn_resnet50(pretrained=True)
model.eval()

from PIL import Image
from torchvision import transforms

input_image = Image.open('demo.png')
preprocess = transforms.Compose([transforms.ToTensor()])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0)

output = model(input_batch)['out'][0]

Pros of YOLOv5

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

Cons of YOLOv5

• Limited to object detection, less versatile than MMSegmentation
• May have lower accuracy for complex scenes compared to more advanced models
• Less flexibility in terms of model customization and experimentation

Code Comparison

YOLOv5:

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

MMSegmentation:

from mmseg.apis import inference_segmentor, init_segmentor
config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'
checkpoint_file = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'
model = init_segmentor(config_file, checkpoint_file, device='cuda:0')
result = inference_segmentor(model, 'test.jpg')

YOLOv5 focuses on simplicity and speed for object detection, while MMSegmentation offers a more comprehensive framework for various segmentation tasks with greater flexibility and customization options.

Pros of transformers

• Broader scope, covering various NLP tasks beyond segmentation
• Larger community and more frequent updates
• Extensive documentation and tutorials

Cons of transformers

• Less specialized for image segmentation tasks
• Potentially steeper learning curve for segmentation-specific applications
• May require more customization for optimal segmentation performance

Code comparison

mmsegmentation:

from mmseg.apis import inference_segmentor, init_segmentor

config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'
checkpoint_file = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'

model = init_segmentor(config_file, checkpoint_file, device='cuda:0')
result = inference_segmentor(model, img)

transformers:

from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
from PIL import Image

feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")

image = Image.open("path/to/image.jpg")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)

Pros of computervision-recipes

• Broader scope covering multiple computer vision tasks
• Includes Jupyter notebooks for easy experimentation and learning
• Provides end-to-end examples for various scenarios

Cons of computervision-recipes

• Less specialized in semantic segmentation compared to mmsegmentation
• May have fewer state-of-the-art models for segmentation tasks
• Potentially slower development cycle for cutting-edge segmentation techniques

Code Comparison

mmsegmentation:

from mmseg.apis import inference_segmentor, init_segmentor

config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'
checkpoint_file = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'

model = init_segmentor(config_file, checkpoint_file, device='cuda:0')
result = inference_segmentor(model, 'demo/demo.png')

computervision-recipes:

from azureml.contrib.services.aml_request import rawhttp
from azureml.contrib.services.aml_response import AMLResponse
from PIL import Image
import numpy as np

@rawhttp
def run(request):
    image = Image.open(request.files["image"].stream)
    # Perform segmentation using a pre-trained model
    result = model.predict(np.array(image))
    return AMLResponse(result.tolist(), 200)