IoU a better detection evaluation metric

Choosing the best model architecture and pretrained weights for your task can be hard. If you’ve ever worked on an object detection problem then you’ve undoubtedly come across plots and tables similar to those below while comparing different models.

Right image source: YOLOv4 [3]. Left image source: EfficientDet [4]

The main thing that you get out of comparisons like these is which model has a higher mAP on the COCO dataset than other models. But how much does that really mean to you? You need to stop strictly looking at aggregate metrics, look instead at the data and model results in more detail to understand what’s working and what’s not.

In recent years, great strides are being made to provide similar detection results with faster models, meaning mAP is not the only factor to consider when comparing two detectors. However, no matter how fast your model is, it still needs to provide high-quality detections that meet your requirements.

While it is important to be able to compare different models easily, reducing the performance of a model down to a single number (mAP) can obscure the intricacies in the model results that may be important to your problem. You should also be considering:

• Bounding box tightness (IoU)
• High confidence false positives
• Individual samples to spot check performance
• Performance on classes most relevant to your task

What is mAP?

Mean average precision (mAP) is used to determine the accuracy of a set of object detections from a model when compared to ground-truth object annotations of a dataset.

We won’t go into full detail here, but you should understand the basics. There is a wide selection of posts discussing mAP in more detail if you are interested [6,_ 7_].

IoU

Intersection over Union (IoU) is used when calculating mAP. It is a number from 0 to 1 that specifies the amount of overlap between the predicted and ground truth bounding box.

• an IoU of 0 means that there is no overlap between the boxes
• an IoU of 1 means that the union of the boxes is the same as their overlap indicating that they are completely overlapping

#object-detection #machine-learning #visualization #evaluation #fiftyone

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IoU a better detection evaluation metric

Choosing the best model architecture and pretrained weights for your task can be hard. If you’ve ever worked on an object detection problem then you’ve undoubtedly come across plots and tables similar to those below while comparing different models.

Right image source: YOLOv4 [3]. Left image source: EfficientDet [4]

The main thing that you get out of comparisons like these is which model has a higher mAP on the COCO dataset than other models. But how much does that really mean to you? You need to stop strictly looking at aggregate metrics, look instead at the data and model results in more detail to understand what’s working and what’s not.

In recent years, great strides are being made to provide similar detection results with faster models, meaning mAP is not the only factor to consider when comparing two detectors. However, no matter how fast your model is, it still needs to provide high-quality detections that meet your requirements.

While it is important to be able to compare different models easily, reducing the performance of a model down to a single number (mAP) can obscure the intricacies in the model results that may be important to your problem. You should also be considering:

• Bounding box tightness (IoU)
• High confidence false positives
• Individual samples to spot check performance
• Performance on classes most relevant to your task

What is mAP?

Mean average precision (mAP) is used to determine the accuracy of a set of object detections from a model when compared to ground-truth object annotations of a dataset.

We won’t go into full detail here, but you should understand the basics. There is a wide selection of posts discussing mAP in more detail if you are interested [6,_ 7_].

IoU

Intersection over Union (IoU) is used when calculating mAP. It is a number from 0 to 1 that specifies the amount of overlap between the predicted and ground truth bounding box.

• an IoU of 0 means that there is no overlap between the boxes
• an IoU of 1 means that the union of the boxes is the same as their overlap indicating that they are completely overlapping

#object-detection #machine-learning #visualization #evaluation #fiftyone

Deep Learning Project : Real Time Object Detection in Python & Opencv

Real Time Object Detection in Python And OpenCV

Github Link: https://github.com/Chando0185/Object_Detection

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#python project #object detection #python opencv #opencv object detection #object detection in python #python opencv for object detection

Intersection over Union — Object Detection Evaluation Technique

This article will describe the concept of IoU in any Object Detection Problem. It will also walk you through the application of the same

#opencv #intersection #object #detection #evaluation #technique

Practical Evaluation Metrics for a Semantic Search Bot

A Guide to Product Metrics in AI

Every Data Scientist working in the Enterprise AI domain must have, or will be dealing with smart chatbots. With the surge in NLP models such as the BERT family, the GPT family and other heavyweight models, semantic question answering has become very easy.

Add to this knowledge-base providers such as Elasticsearch, which allow for custom search functions, the bots have become efficient as well.

However, when you build a smart-bot you need to quantify its performance. This is very important in order to figure out if it is even a good idea to go ahead with the bot. Hence, it is very important to design performance metrics for your bot.

In this post, I talk about a question answering bot, trained on a knowledge base. What this means is that there is a document that contains a set of unique question-answer pairs belonging to one or more topics.

Chatbots that belonged to the pre-Language Model era work on word-pair similarity. This means that given two sentences, similarity between the constituent words, in the vector form, is calculated (using Cosine similarity score).

However, all that glitters isn’t gold. Not every answer that comes up at the top of the list of top matches is not necessarily the best answer around.

The problem with these models is not in the way similarity is measured, but the way words are represented in sentences. For example, the word ‘park’ can have a different meaning in the sentence “I need to park my car somewhere”, and a different meaning in “let’s have a stroll in the park”. This difference in meaning makes a huge difference to how bots find similarity. Older models only look for similarity of words but not contexts.

Enter Language Models! These models represent words along with sentences. This helps the models capture context of words in sentences. This is the value added by language models to Natural Language Understanding.

Word similarity is still calculated using the cosine similarity over the vectorised forms of each sentence.

#chatbot-testing #product-analytics #product-metrics #chatbot-design #chatbots #practical evaluation metrics for a semantic search bot

Metrics to Use to Evaluate Deep Learning Object Detectors

Different approaches have been employed to solve the growing need for accurate object detection models. More recently, with the popularization of the convolutional neural networks (CNN) and GPU-accelerated deep-learning frameworks, object- detection algorithms started being developed from a new perspective. CNNs such as R-CNN, Fast R-CNN, Faster R-CNN, R-FCN, SSD and Yolo have highly increased the performance standards on the field.

Once you have trained your first object detector, the next step is to know its performance. Sure enough, you can see the model finds all the objects in the pictures you feed it. Great! But how do you quantify that? How should we decide which model is better?

Since the classification task only evaluates the probability of the class object appearing in the image, it is a straightforward task for a classifier to identify correct predictions from incorrect ones. However, the object detection task localizes the object further with a bounding box associated with its corresponding confidence score to report how certain the bounding box of the object class is detected.

A detector outcome is commonly composed of a list of bounding boxes, confidence levels and classes, as seen in the following Figure:

Object detection metrics serve as a measure to assess how well the model performs on an object detection task. It also enables us to compare multiple detection systems objectively or compare them to a benchmark. In most competitions, the average precision (AP) and its derivations are the metrics adopted to assess the detections and thus rank the teams.

Understanding the various metric:

IoU:

Guiding principle in all state-of-the-art metrics is the so-called Intersection-over-Union (IoU) overlap measure. It is quite literally defined as the intersection over union of the detection bounding box and the ground truth bounding box.

Dividing the area of overlap between predicted bounding box and ground truth by the area of their union yields the Intersection over Union.

An Intersection over Union score > 0.5 is normally considered a “good” prediction.

#2020 aug tutorials # overviews #computer vision #deep learning #metrics #object detection