Exam AWS Certified Machine Learning - Specialty topic 1 question 258 discussion

Object Detection vs. Semantic Segmentation Object detection (A/B) localizes hair regions using bounding boxes, which is faster and simpler than semantic segmentation (C/D), which requires pixel-level masking. For basic grouping tasks, precise boundaries are unnecessary, making object detection more efficient. ResNet-50 vs. XGBoost ResNet-50 (A/C) is a pre-trained CNN model optimized for image classification (e.g., distinguishing curls vs. straight hair or blonde vs. brunette). It processes raw images directly, reducing preprocessing steps. XGBoost (B/D) is a tabular-data algorithm requiring manual feature extraction (e.g., color histograms, texture metrics), adding complexity and development time.

Going for object detection to identify hair is easy but customer needs hair style. So in this contest I believe Semantic Segmentation does a good job.

Definitely 'C' Semantic Segmentation is the algorithm for colour segmentation like hair

C. Semantic Segmentation allows for pixel-wise classification of the video frames, meaning it can precisely identify and isolate a visitor’s hair by labeling each pixel in the image as belonging to hair (or other categories).Object detection uses bounding boxes, which would not effectively isolate hair, especially in cases where hair might not have clear boundaries.

Copied from ChatGPT: "Semantic segmentation provides a pixel-level classification of the image, meaning it labels each pixel in the image with the class of the object it belongs to. However, it does not inherently detect whether the object is present in the image. Instead, it assumes that the objects of interest are already present and segments the image accordingly." Since the input is video stream, Not all the frames(images) contain hair! Therefore I would go for A.

semantic segmentation identifies hair, and Resnet for type and color.

I was sure that it was option C but but when we want to select the option requiring the least amount of effort, it must be A. Hair color is detected by ResNet-50, not by semantic algorithm. So, object detection algorithms are generally easier to implement and fine-tune compared to semantic segmentation algorithms. They can accurately locate and extract specific objects, such as hair, from the video frames, simplifying the subsequent analysis. Additionally, ResNet-50 is a widely used pre-trained model for image classification tasks, making it relatively straightforward to apply for determining hair style and hair color

We need Semantic Segmentation to identify the hair style and color by pixel level mapping

CHAT GPT4= C

I doubt that object detection will detect hair better than a semantic segmentation

Will go with A (Object detection) as semantic segmentation requires labelling every pixel in a picture which is more effort compared to object detection

C is the right. Semantic Segmentation = Pixel Level category assingment Restnet50 = used for image recognition and computer vision tasks

C The backbone is a network that produces reliable activation maps of image features. The decoder is a network that constructs the segmentation mask from the encoded activation maps. Amazon SageMaker semantic segmentation provides a choice of pre-trained or randomly initialized ResNet50 or ResNet101 as options for backbones. The backbones come with pre-trained artifacts that were originally trained on the ImageNet classification task. These are reliable pre-trained artifacts that users can use to fine-tune their FCN or PSP backbones for segmentation. Alternatively, users can initialize these networks from scratch. Decoders are never pre-trained. Semantic Segmentation algorithm is now available in Amazon SageMaker | AWS Machine Learning Blog https://aws.amazon.com/cn/blogs/machine-learning/semantic-segmentation-algorithm-is-now-available-in-amazon-sagemaker/

Letters B - D are wrong as they ultimately use a tabular classification model for an image problem, so we discard it. As we want a solution with the least effort, it is known that object detection requires less training effort than semantic segmentation, in addition to being able to keep the visitor's hair in the frame. Therefore, the correct alternative is Letter A.

Segmentation is too heavy

Using ResNet-50, you can determine hair style and hair color by passing the identified hair region from the semantic segmentation algorithm as input. ResNet-50 can classify the hair region into one of the 1000 categories from the ImageNet database, such as curly, straight, blonde, brunette, etc. Option C is the best option for your problem because it allows you to efficiently and accurately identify and classify a visitor’s hair in video frames using two powerful deep learning algorithms: semantic segmentation and ResNet-50.

Xgboost not for Image detection