Calculate map object detection python. Improve your object detection accuracy using TIDE.

AveragePrecision is defined as the average of the precision scores after each true positive, TP in the scope S. 95] or AP@[. ground_truth = [[0,6,234,45,362], [1,1,156,103,336], Oct 6, 2018 · It appears this is a rather simple solution. So I wanted to know if someone has worked with Tensorflow object detection API models and know how to display Training data accuracy or Testing accuracy. I used this github source below to add in relevent code. This project supports different bounding box formats as in COCO, PASCAL, Imagenet, etc. These algorithms commonly rely on machine learning or deep learning methods to generate valuable outcomes. To my knowledge, YOLOv5 stores them as (xmid, ymid, width, height) in relative format. 2 Designing Intersection over Union metric for Object Detection. Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples. 7925. Mean Average Precision (mAP): Key Takeaways. e. 875. SyntaxError: Unexpected token < in JSON at position 4. Additionally, they help in understanding the model's handling of false positives and false negatives. Mar 2, 2022 · In any case, in object detection they have slightly different meanings: let: TP - set of predicted objects that are successfully matched to a ground truth object (above IOU threshold for whatever dataset you're using, generally 0. In our course, " YOLOv8: Video Object Detection with Python on For object detection in images the mAP (mean average precision) metric is often used to see how good the implementation is. 25) / 2 = 0. contourArea() function and converts the area to a real-world unit of measurement using a scale factor. 78 + 0. For the exact paper refer to this. 7) FP - set of predicted objects that were not successfully matched to a ground truth object FN - set of Nov 16, 2023 · Object Detection with YOLOv5. In object detection, a model predicts the locations of objects and their classes (i. predict(x_test) average_precision_score(y_test, predictions) answered Jan 20, 2021 at 17:20. You can use the SequenceMatcher class from the built-in difflib module to compare two arrays. One of the most popular algorithms to date for real-time object detection is YOLO (You Only Look Once), initially proposed by Redmond et. So how do we measure the performance of our object detection model? Object Detection Metrics. Oct 30, 2019 · Here is the complete algorithm: Compute the main orientation (main axis, computed with PCA) Rotate your image according to the main axis orientation. If the centroid is on the left, then your image is well oriented, else rotate it about 180°. Dec 2, 2020 · average mAP = (1 + 0. In simple terms, object detection is a two-step process. Here's how to calculate the IoU of two axis-aligned bounding boxes. I need to calculate the mAP described in this question for object detection using Tensorflow. Object Detection. Let’s get started. These metrics quantify how well a model performs in Jun 26, 2023 · In this example, we'll see how to train a YOLOV8 object detection model using KerasCV. In this tutorial, you will figure out how to use the mAP (mean Average Precision) metric to evaluate the performance of an object detection model. KerasCV includes pre-trained models for popular computer vision datasets, such as ImageNet, COCO, and Pascal VOC, which can be used for transfer learning. Based on the APs of the 2 classes (0. You just need to know in which format YOLOv7 coordinates are stored. It contains 170 images with 345 instances of pedestrians, and we will use it to illustrate how to use the Nov 11, 2022 · For instance, in the evaluation document of the COCO object detection challenge, AP and mAP are the same. Before moving forward, make sure you have torch and torchvision installed:! python -m pip install torch torchvision YOLOv5's got detailed, no-nonsense documentation and a beautifully simple API, as shown on the repo itself, and in the following example: The following article is really useful (although it is using Python instead of C++) if you are using a single camera to calculate the distance: Find distance from camera to object/marker using Python and OpenCV. In the final example frame you can see how the object is correctly identified and the class name matches. And you have rectangular shape bounding boxes predicted. Apr 3, 2024 · YOLO8 — Object Detection 1. Mar 23, 2019 · 1. The inference time of my model is very high (~10 seconds total time for prediction and ) even though I am using a Nvidia Tesla K80 GPU in my cluster node. 50 and mAP@0. 949 + 0. 2. To evaluate object detection models like R-CNN and YOLO, the mean average precision (mAP) is used. The width (in units such as inches, meters, etc. The mathematical notation for OKS is given by: where: Jun 6, 2024 · Object detection, within computer vision, involves identifying objects within images or videos. We will talk of the Object Detection relevant mAP. Compute the bounding box and centroid/barycenter. Object detection dataset used for evaluation. Given an input image, it can return the labels, bounding boxes, confidence scores, masks and skeletons of objects. Jul 15, 2020 · In this tutorial, you will figure out how to use the mAP (mean Average Precision) metric to evaluate the performance of an object detection model. "Axis-aligned" means that the bounding box isn't rotated; or in other words that the boxes lines are parallel to the axes. A solid understanding of IoU requires practical applications. Here is a summary of the steps to calculate the AP: Generate the prediction scores using May 14, 2024 · The MediaPipe Hand Landmarker task lets you detect the landmarks of the hands in an image. They shed light on how effectively a model can identify and localize objects within images. 50: . Note that this function in python is exactly the same but must be called from the depth frame and the x and y must be cast to May 6, 2020 · After I had run inference over each image in my test set, I imported a python package to calculate mAP in my Colab notebook. Thì chúng ta thấy bài toán có thể đo lường qua Precision, Recall, Confusion Metric, Accuracy…. 75 = 0. Dec 9, 2022 · You have trained you first object detection model, YOLO or R-CNN. predict. Object keypoint similarity (OKS) is finally computed as the arithmetic average across all labeled keypoints in an instance. 32 / 30) * 100 = 31. これらを理解 May 18, 2021 · I want to calculate and curve a graph for mAP/epochs for a training dataset. 95] incrementing Click here to download the full example code. So, the mean average precision for the model across all four classes is approximately 0. Here I represent a python code to calculate mAP with given boxes edge from your prediction and data. 625. Parameters: A path to the directory where the output plots will be saved. Measure Size of an Object using Python OpenCV Nov 18, 2022 · y_abs = y_rel * img_h. After running the input through the model, it returns an array of results Hashes for object-detection-metrics-0. KerasCV also provides a range of visualization tools for inspecting the intermediate representations May 4, 2023 · and run predict to detect all objects in it: results = model. This tutorial discussed how to calculate the mean average precision (mAP) for an object detection model. following formula is used to calculate precision: Precision = TP/ TP+FP Nov 17, 2023 · Meta AI's Detectron2 - Instance Segmentation and Object Detection. May 12, 2023 · I normally use model. 修論執筆に際してかなり丁寧にまとめたつもりです．. this is function that I used. The code sample described in these instructions is available on GitHub. For this tutorial, we will be finetuning a pre-trained Mask R-CNN model on the Penn-Fudan Database for Pedestrian Detection and Segmentation. A good overview of these metrics is here. Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources. ), where we need to judge whether or not the model has good accuracy across all classes. 949 and 0. 75. 05%. Evaluation metrics play a crucial role in understanding the performance of object detection models. I am using gRPC for getting predicitons from the model. In the next few sections, we will cover steps that led to the development of Faster R-CNN object detection Jun 5, 2022 · SSD MobileNet is the model I am working on. May 12, 2022 · Object-detection-and-localization is among the fastest evolving areas of machine learning. It is the algorithm /strategy behind how the code is going to detect objects in the image. Còn với một model OD thì chúng ta sẽ làm quen với Precision, Recall, AP và mAP. Computer Vision deep learning faster rcnn implementation keras object detection object detection algorithms python. Measuring the size of an object can be applied to anything, no matter what its outline, color, or background, and in this tutorial, we will see which technology Nov 1, 2021 · There are two scripts in the parent directory: train. Detectron2 is Meta AI (formerly FAIR - Facebook AI Research)'s open source object detection, segmentation and pose estimation package - all in one. The numerator will be lesser as the prediction fails to predict the area inside the ground truth. predict("cat_dog. 47% = WBC AP mAP = 84. Finally, we'll perform real-time edge detection inference on a video and save the resulting video as an MP4 file using Mar 3, 2021 · Finished building your object detection model?Want to see how it stacks up against benchmarks?Need to calculate precision and recall for your reporting?I got Mar 19, 2024 · To calculate the mean average precision (mAP), you simply average the average precision values obtained for each class. It is available on github for people to use. jpg") The predict method accepts many different input types, including a path to a single image, an array of paths to images, the Image object of the well-known PIL Python library, and others. TorchVision Object Detection Finetuning Tutorial ¶. You can just calculate the y_score (or predictions) and then use sklearn. Jun 24, 2021 · The last thing to do is to merge the data to derive the distance to the object with the object detection. The ConfusionMatrix class can be used to generate confusion matrix for the object detection task. Given all that, you should be able to compute the width and height the bounding boxes easily. 95 serve specific purposes in evaluating object detection models, depending on the How to calculate precision, recall, F1-score, ROC AUC, and more with the scikit-learn API for a model. The currently popular Object Detection definition of mAP was first formalised in the PASCAL Visual Objects Classes(VOC) challenge in 2007, which included various image processing tasks. Sep 23, 2023 · I have the following code that calculates precision-recall curve for object detection task, where detections are matched to ground-truth first by creating 1-to-1 pairs starting from detection with the highest confidence score and matching it to ground-truth object for which the overlap is the highest. This is the 4th lesson in our 7-part series on the YOLO Object Detector: Introduction to the YOLO Family. 5 = 1. This gives us some much-needed perspective on what expectations we may have from a robust object detection Jul 31, 2019 · For object detection in images the mAP (mean average precision) metric is often used to see how good the implementation is. mAP (mean average precision) is an important parameter to tell whether an object detection model is good. Refresh the page, check Medium ’s site status, or find something interesting to read. 31%. Better evaluation metric. The Mar 12, 2019 · I have deployed my object detection model to Google Kubernetes Engine. Now let’s simplify this statement a bit with the help of the below image. My model is trained using faster_rcnn_resnet101_pets configuration. Nov 7, 2016 · Intersection over Union (IoU) is used to evaluate the performance of object detection by comparing the ground truth bounding box to the preddicted bounding box and IoU is the topic of this tutorial. I assumed that: TP - is the detection with intersection over union (IoU) > threshold, same class and only the first detection of a given object To answer your questions: Yes your approach is right; Of A, B and C the right answer is B. One more thing before we get ready for some evaluation: Usually in an object detection/instance segmentation algorithm, there are multiple categories. Jan 26, 2018 · Both these domains have different ways of calculating mAP. Oct 5, 2019 · The COCO Object Detection Challenge: evaluates detection using 12 metrics where: mAP (interchangeably referred to in the competition by AP) is the principal metric for evaluation in the competition, where AP is averaged over all 10 thresholds and all 80 COCO dataset categories. You can call one of the detect functions to trigger inferences. Figure 2: Three objects are present in this image for simple object tracking with Python and OpenCV. This class is a utility class for computing detection metrics such as precision, recall, and mean average precision (mAP) of an object detection model. Some of them encircle the object you want to identify perfectly, but some of them are too big, too small, or too far away from the correct location. Jan 3, 2023 · To calculate precision in 3D object detection, we need a set of ground truth labels and a set of predicted labels. We need to compute the Euclidean distances between each pair of original centroids ( red) and new centroids ( green ). May 21, 2024 · Run the task. Oct 25, 2018 · Traditionally, this is called “mean average precision” (mAP). Lastly, we have the most important directory, the pyimagesearch directory. Classify the image inside each bounding box and assign it a label. Step 6 - Calculate Average Precision. The prediction should be in shape= (-1,5) [probability, xmin, ymin, xmax, ymax]and data in shape= (-1,4 Mar 22, 2023 · For the object detection comparison of the 5 model sizes The YOLOv8m model achieved an mAP of 50. Usage In the test code, you need to declare the ConfusionMatrix class with the appropriate parameters. Average precision(AP) is a typical performance measure used for ranked sets. I expect the function that can calibrate IoU threshold such as mAP90, mAP1. mAP@0. 4. Understanding a Real-Time Object Detection Network: You Only Look Once (YOLOv1) A Better May 2, 2020 · Here's an illustrative plot that calculates mAP scores for 30 classes: 31. The explanation is the following: In order to calculate Mean Average Precision (mAP) in the context of Object Detection you must compute the Average Precision (AP) for each class, and then compute the mean across all classes. Steps 3 and 4 - Calculate confusion matrix, precision, and recall. 87% = RBC AP 96. 80) / 4 = 0. Its use is different in the field of Information Retrieval (Reference [1] [2] )and Multi-Class classification (Object Detection) settings. 2: indicates that the keypoint is labeled and visible. Explore the key concepts in object detection and learn how they are implemented in SSD and Faster RCNN, which are available in the Tensorflow Detection API. Introduction. To do this, we need to know: The distance of the camera from an object. May 2, 2022 · In this tutorial, you will learn Mean Average Precision (mAP) in object detection and evaluate a YOLO object detection model using a COCO evaluator. 物体検出の評価指標であるmAPの算出方法を詳しく説明している記事が少なかったのでまとめました．. detect(mp_image) For a complete example of running an Object Detector on an image, see the code example for details. I will cover in detail what is mAP, how to calculate it, and give you an example of how I use it Nov 29, 2022 · In this video, we will see how to calculate the dimensions of a fixed camera object on a flat surface. The implementation of this open source project is really clear. This repo contains code we've found useful to speed up the results analysis for object detection projects. proto files of protobuff using the following commands running one by one If the issue persists, it's likely a problem on our side. Step 5 - Calculate area under the precision-recall curve. This denoted by AP@[. Learn about image gradients, gradient orientation and magnitude, Sorbel and Scharr filters, as well as automated ways to calculate the optimal threshold range for Canny edge detection. 5 : . Intel’s Pre-Trained Models: The team at Intel has trained these models and optimized them to run with OpenVINO. It houses 3 very important scripts. average mAP = (1 + 0. For a given input image, a classification algorithm would output a probability distribution of interested classes. , dog, cat, human, vehicle, etc. Just accuracy, not mAP, because the model do find mAP but not accuracy. It is faster because it uses a region proposal network (RPN) to generate ROIs directly from the feature maps of the CNN. Aug 22, 2018 · YOLO (You Only Look Once) is a method / way to do object detection. (9. gz; Algorithm Hash digest; SHA256: 552ab6f737026c86ecb738400e533634a62d05e32f5f87e0112cbdcdc47e90e4 To avoid such cases, you can follow a more convenient and feasible approach. Performance metrics are key tools to evaluate the accuracy and efficiency of object detection models. The Compute Accuracy For Object Detection tool calculates the accuracy of a deep learning model by comparing the detected objects from the Detect Objects Using Deep Learning tool to ground reference data. The mean average precision (mAP) of a set of queries is defined by Wikipedia as such: where Q is the number of queries in the set and AveP (q mAP is Mean Average Precision. Refresh. After poking around the c++ examples, the realsense sdk provides a function known as get_distance (x,y) where it will return the depth distance according to the x,y coords. Jan 13, 2023 · The MAP (mean average precision) is the most widely used metric for object detection. Sep 15, 2023 · It evaluates a model’s ability to precisely localize objects with high precision. aims to detect not only the presence of interested classes in an Oct 25, 2018 · Now, my question is when I run the inference as provided in the example , I get an output dict with detection scores, number of detections, detection classes, detection masks etc for each image, so from these outputs how do I calculate the mAP for my test set? Any guidance in this direction will be really helpful, thanks in advance. draw_object_info(bgr_frame, depth_frame) With this you get the object distance. py: used to draw inference from our model and see the object detector in action. These instructions show you how to use the Hand Landmarker with Python. In my last article we looked in detail at the confusion matrix, model accuracy Jun 28, 2022 · 1. It now can be installed as a package with Apr 26, 2022 · This repository contains over 200 neural network models for tasks including Object Detection, Classification, Image Segmentation, Handwriting Recognition, Text-to-Speech, Human Pose Estimation, and others. mAP sẽ là một tiêu chí đo lường của các model Object Detection. AP [class] = 1 #thresolds ∑ iou ∈ thresholdsAP [class,iou] AP [class] = 1 #thresolds ∑ iou ∈ thresholds A P [ c l a s s, i o u] Step 2: Calculate the final AP by averaging the AP over different classes. Lines 7 - 10: get the coordinates of the intersection area. It now can be installed as a package with Aug 17, 2014 · For axis-aligned bounding boxes it is relatively simple. Mar 14, 2022 · Identification and localization of objects in photos is a computer vision task called ‘object detection’, and several algorithms has emerged in the past few years to tackle the problem. 05: . Unlock the potential of YOLOv8, a cutting-edge technology that revolutionizes video Object Detection. Canny(). def get_iou(bb1, bb2): """ Calculate the Intersection over Union (IoU) of two bounding boxes. Improve your object detection accuracy using TIDE. The current state of the art has an mAP of 63. 9% with more than double the Oct 7, 2023 · It measures the accuracy of the object detector by evaluating the degree of overlap between the detection box and the ground truth box. Evaluation of YOLOv3 on cell object detection: 72. content_copy. please noted that the iou key in that function is threshold for NMS, not for calculate the mAP. Để Calculate mAP for object detection. Sep 20, 2019 · Step 1: For each class, calculate AP at different IoU thresholds and take their average to get the AP of that class. In this case: mAP = (0. The first step to finding the distance to an object or marker in an image is to calibrate and compute the focal length. Researchers who want to evaluate their work using different datasets than those offered by the competitions, need to implement their own version of the There are a bunch of different object detection challenges, the most famous one being the COCO challenge. There are two kinds of models. Based on this equation, the mAP is 0. Feb 17, 2021 · 2. I will cov As you know, mAP uses Intersection Over Union(IOU), Precision(P), and Recall(R) which are calculated based on the number of true positives(TP), false positives(FP), and false negatives(FN). mAP (Mean Average Precision), AP (Average Precision)は物体検出の精度を比較するための指標です．. From the ground truth bounding boxes and the prediction scores obtained from the trained object detection Nov 12, 2023 · ultralytics. The higher the score, the more accurate the model is in its detections. Accurate object detection. DetMetrics. 75 + 0. We make no distinction between AP and mAP (and likewise AR and mAR) and assume the difference is clear from context. Objects May 9, 2024 · For instance, in the evaluation document of the COCO object detection challenge, AP and mAP are the same. Here is a summary of the steps to calculate the AP: Generate the prediction scores using Oct 3, 2021 · If you are using the Tensorflow Object Detection API, it provides a way for running model evaluation that can be configured for different metrics. A Beginner's Guide to Object Detection. utils. . 2% on the COCO dataset, whereas the largest model, YOLOv8x achieved 53. Sep 12, 2020 · I am trying to count the number of True Positives (TP), False Positives (FP), and False Negatives (FN) for object detection task with two classes. Additionally, it calculates the height and width of the detected objects in pixel units and provides functionality to convert them to real-world units such as centimeters (cm). The Object Detector task will return the objects detected within the input image or frame. If the area of the predicted box is higher, the denominator will be higher, making the IoU Mean Average Precision in Practice: Object Detection. 5 or 0. Post to Facebook! Like tutorial Must be logged in to Like Like 0. Access to a well-curated dataset allows learners to engage with real-world challenges Jul 15, 2020 · PyLessons Published July 15, 2020. Find bounding boxes containing objects such that each bounding box has only one object. py: used to train our object detector. Steps 1 and 2 generate the prediction scores and convert them into class labels. metrics import average_precision_score. Average precision is related to the area May 27, 2022 · We use mean Average Precision (mAP) when evaluating object detection models like YOLO and SSD. It provides: Easy creation of a pandas inference dataframe enabling detailed analysis. If you found this blog helpful or have any constructive criticism feel free to Nov 12, 2023 · Introduction. A tutorial on how to do this is here. YOLOv8, or "You Only Look Once," is a state-of-the-art Deep Convolutional Neural Network renowned for its speed and accuracy in identifying objects within videos. 958)/2 = 0. I want to know, is there a way to find the accuracy of the model. mAP = (0. This article explains the objective of mAP and how to calculate it. To calculate it for Object Detection, you calculate the average precision for each class in your data based on your model predictions. Detections with lower IoU will be classified as FP. keyboard_arrow_up. As no packages that make the calculation for you were available at this time, I adapted the implementation from João Cartucho, which uses files which hold the detection results. A ground truth box or label is an annotated box showing where the object is (the annotation is often done by hand, and the ground truth box is considered the object's actual position). Although on-line competitions use their own metrics to evaluate the task of object detection, just some of them offer reference code snippets to calculate the accuracy of the detected objects. The mAP compares the ground-truth bounding box to the detected box and returns a score. It is the ratio of the overlap area to the combined area of prediction and ground truth. Your nested lists will need to be flattened for this to work, which can be done with nested simple list comprehensions: from difflib import SequenceMatcher. - aksaayush/Object-Detection-with-YOLOv5-Height-and-Width-Conversion Description. 9535. val () to evaluate the model, but its only give mAP50 and mAP50-95 of all classes and each class. In the setting of mAP calculation for Object Detection of the Pascal VOC Challenge, the TP, FP, and FN are defined as follows: Creating evaluation metrics for projects involving object detection takes a surprising amount of time. The COCO evaluation metrics includes analogous measures of precision and recall for object detection use cases. While researching object detection, you will likely encounter terms such as AP (Average Precision), mAP (Mean Average Precision), and IoU (Intersection over Union), all of which are object detection metrics that help build better-performing models. This project showcases object detection using YOLOv5, a state-of-the-art deep learning model, to detect objects in an image. Detection IoU threshold between 0 and 1 . # Show depth info of the objects mrcnn. 05 % is calculated by averaging the average precision values for all classes in the model. Below is the original Oct 11, 2021 · Apologies, but something went wrong on our end. As of my understanding the more the figure, the better and it is a good indicator to rely on when deciding to stop the training / train further. 75 * 1 = 0. Detection confidence threshold between 0 and 1 . Finally, it prints the size of the object in the chosen unit of measurement. Apr 16, 2017 · On the other hand, I want to say that we can find an exact implementation of mAP specifically for each dataset. With recent advancements in deep learning based computer vision models, object detection applications are easier to develop than ever before. 9. text. metrics. Sarah. As to be expected now we observe a much higher mAP score for the detection example with overall higher IoU scores. Aug 15, 2023 · 1: indicates that the keypoint is labeled but not visible. Jan 30, 2023 · This function takes in two inputs, boxA and boxB, which are two bounding boxes in the format: [x0,y0,x1,y1] and returns the IoU score, IoU and the intersection area, boxI. Unexpected token < in JSON at position 4. Jan 19, 2015 · focalLength = (marker[1][0] * KNOWN_DISTANCE) / KNOWN_WIDTH. 84 + 0. 15% = Platelets AP 74. The official implementation of this idea is available through DarkNet (neural net implementation from the ground up in C from the author). As no packages that make the calculation for you were available at this time, I adapted the implementation from João Cartucho, which uses files which hold the detection results. 75) / 2 = 0. Since I benefited from MSCOCO Detection Dataset , the author of MSCOCO present a good module in python to measure this metric. For more information about the capabilities, models, and configuration options of this task, see the Overview. post1. Line 3 - 4: extract the bounding boxes coordinates. Mar/2019: First publish Nov 17, 2023 · In this guide, learn how to perform edge detection in Python and OpenCV with cv2. The accuracy of a model is evaluated using four accuracy metrics: the Average Precision (AP), the F1 score, the COCO mean Average Precision Oct 14, 2021 · Với một model phân loại Chó/Mèo đơn giản đi. Detections with lower confidence will be excluded. predictions = model. As you have already integrated a camera for object detection, you can use the depth information that the camera uses to draw the bounding boxes for localizing objects to calculate the distance of that object from the camera. Both mAP@0. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). Example with skewness towards 0. tar. ) of this object. Best link is Stereo Webcam Depth Detection. Mar 24, 2019 · The mean average precision (mAP) or sometimes simply just referred to as AP is a popular metric used to measure the performance of models doing document/information retrival and object detection tasks. IoU is a common choice among state-of-the-art object detection tasks, which we will focus on today. Summary statistics for easy plotting. Here is my training code. Function that takes an image as input and returns Detections object. al [1]. Apr 17, 2023 · The code calculates the area of the object in pixels using the cv2. 59% = Platelets AP 77. metrics to calculate the average precision: from sklearn. And here were the results! Evaluation of EfficientDet on cell object detection: 78. Object Detection Metrics. detection_result = detector. 3, with only a handful of papers having achieved a mean average precision of over 60. Nov 4, 2018 · Faster R-CNN is an improved version of Fast R-CNN for object detection. For example, if we want to measure the sizes of the nuts we can use the diameter. Jul 23, 2018 · Step #2: Compute Euclidean distance between new bounding boxes and existing objects. Such an algorithm is an extension of the standard classification algorithm. 9535 Conclusion. Sep 29, 2017 · change the directory to models or model master of tensorflow then you have to compile the . 958), the mAP of the object detection model is calculated according to the next equation. yz xm va ww cd it dh jp ho yl