nms iou threshold yolov5

One of the solutions is Soft NMS algorithm. IOU threshold: IOU stands for Intersection Over Union. As we repeat the steps, we eliminate all overlapping (lower score) bounding boxes and leave the highest score one. Beta In other words, we set a confidence threshold to eliminate bounding boxes with less than a specific score value. We hate SPAM and promise to keep your email address safe.. on Apr 7, 2022 Maintainer @rabiyaabbasi default settings are in detect.py argparser, you may modify these to whatever you want: yolov5/detect.py Lines 211 to 243 in 5f941a8 def parse_opt (): parser = argparse. box_score_threshold: During inference, only return proposals with a score greater than box_score_threshold. privacy statement. Could you also please clarify these IoU values in the table description, the columns says mAP@0.5 but the detailed explanation is using some completely different values? The below image shows the IoU concept, where the black part is an overlap area (intersection) between two boxes. Thats a pretty big difference. We do the same thing with this line, xx1 = torch.max(xx1, x1[idx]), x1[idx] is actually P.x1 since idx is the index of the current selected prediction S in P. Similarly, we find xx2, yy1 and yy2 and obtain intersection box coordinates of all the boxes present in P with the selected prediction S. Next we find width w and height h of every intersection box and take a max with 0.0 to avoid negative w and h due to non-overlapping BBoxes. Hi I wanted to know what are the default values of confidence and IoU threshold while training the data . The score is the multiplication of the objectness score and classification probability. Now, we are ready to discuss how NMS works. Reproduce mAPbypython test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65, Reproduce speedbypython test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45. This time we have two boxes in the final plot. Includes preprocessing, inference and NMS. Usage There are two ways to use rikai-yolov5. in nonmaxsuppression function, iou = box_diou(boxes[i], boxes) > iou_thres # iou matrix << Why would the Bank not withdraw all of the money for the check amount I wrote? I can confirm on my custom trained yolo model that this indeed gives best results on the test set, but I'm failing to see the intuition behind this. Before we go ahead, I would like to discuss this one concept that we will be using in the following sections i.e. In 2007, right after finishing my Ph.D., I co-founded TAAZ Inc. with my advisor Dr. David Kriegman and Kevin Barnes. agnostic: (bool), when it is set to True, we do class-independent nms, otherwise, different class would do nms respectively. However, there are some problems with it that recent researches on NMS aim to fix. Congratulations on making it this far. If you continue to use this site we will assume that you are happy with it. What does skinner mean in the context of Blade Runner 2049. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. @eldarkurtic good news ! Secondly, attached below is my F1 score. @lucasvw If you are a Facebook employee using PyTorch on mobile, please visit Internal Login for possible . To load a pretrained YOLOv5s model with 10 output classes rather than the default 80: In this case the model will be composed of pretrained weights except for the output layers, which are no longer the same shape as the pretrained output layers. ArgumentParser () parser. This threshold is for performing Non-maximum suppression. Was this translation helpful? Please visit our Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution. For example, if score = [1, 0, 2] and mask = [True, False, True], then score[mask] = [1, 2]. NMS is a sequential process, and it cannot run in parallel. Torch.index_select selects elements from a input tensor according to an index list. [1] https://medium.com/@whatdhack/reflections-on-non-maximum-suppression-nms-d2fce148ef0a, [2] https://www.jeremyjordan.me/evaluating-image-segmentation-models/, [3] https://github.com/rbgirshick/fast-rcnn/blob/master/lib/utils/nms.py, [4] https://medium.com/koderunners/intersection-over-union-516a3950269c, Filed Under: Deep Learning, Face Detection, Object Detection, PyTorch, Theory. These are completely separate. We do that for dog, cat, and others. In YOLOv5, the nms_max_overlap parameter is used in the "detect" file, but not in the "track" file. To set the confidence threshold of the custom-trained YOLOv5 model, Use the following: To retrieve the inference image and the other values, use. This question is completely irrelevant given that --iou is used for the NMS, and not for metric calculation. conf_thres=0.001, # confidence threshold Well occasionally send you account related emails. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. I am trying to perform inference on my custom YOLOv5 model. to better use yolov5 you should know a little more in the tutorial, add the step by step. model. Thank you for your contributions to YOLOv5 and Vision AI ! It is where Non-Maximum Suppression (NMS) comes to play, keeping the most probable bounding boxes and eliminating other less-likely bounding boxes. There would be many bounding boxes, each with 80 probabilities. By clicking Sign up for GitHub, you agree to our terms of service and Happy trainings with YOLOv5 ! Next we extract all the x1 coordinates according to the indices present in order. This site uses Akismet to reduce spam. These 3 files are designed for different purposes and utilize different dataloaders with different settings. Well occasionally send you account related emails. You switched accounts on another tab or window. So this is really odd, and I'm not sure if this is functioning as expected or not, but it seems the NMS implementation lets overlapping detections slide by. You signed in with another tab or window. Thanks for contributing an answer to Stack Overflow! We learned what NMS is? To get started: YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled): If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. # set model parameters model.conf = 0.25 # NMS confidence threshold model.iou = 0.45 # NMS IoU threshold model.agnostic = False # NMS class-agnostic model.multi_label = False # NMS multiple labels per box model.max_det = 1000 # maximum number of detections per image # set image img = 'https: . And any box that has an IoU of less than 0.8 with the highest confidence box will not be suppressed and will be treated as a separate valid bounding box. The examples below are with an IOU threshold of 0.2: Is this the expected behavior? python yolov5\detect.py --source vid.mp4 --weights runs\train\exp\weights\best.pt --img 640 --iou-thres 0.5. The functions takes as as input the prediction, confidence (objectness score threshold), num_classes (80, in our case) and nms_conf (the NMS IoU threshold). = [0, 15, 16] for COCO persons, cats and dogs, # Automatic Mixed Precision (AMP) inference, # array of original images (as np array) passed to model for inference, # updates results.ims with boxes and labels, https://pytorch.org/hub/ultralytics_yolov5, TFLite, ONNX, CoreML, TensorRT Export tutorial. AJ-RR closed this as completed on Sep 2, 2020. Jun 1, 2022 Understanding the Logic and Tricky Part of NMS Object detection models like YOLOv5 and SSD predict objects' locations by generating bounding boxes (shown in blue rectangles below).. parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold'), In detect.py We can choose the selection criteria to arrive at the desired results. rev2023.7.5.43524. GIF by Author. Let us get to the nitty-gritty of this post, the actual algorithm. Are there good reasons to minimize the number of keywords in a language? Please! The simple yet efficient way to deal with this case is to use Soft-NMS. As a leader in vision ML and AI, we do offer professional consulting, from simple expert advice up to delivery of fully customized, end-to-end production solutions for our clients, such as: For more information please visit https://www.ultralytics.com. Well occasionally send you account related emails. @glenn-jocher By the way, are you calculating the AP value in yolov5 in the same way as coco? how To fuse the handle of a magnifying glass to its body? I appreciate it for answering my former question. A report detailing each student's PPE compliance in . privacy statement. CI tests verify correct operation of YOLOv5 training (train.py), testing (test.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit. The experimental results show that under the same training conditions, compared . The text was updated successfully, but these errors were encountered: Hello @AJ-RR, thank you for your interest in our work! results can be printed to console, saved to runs/hub, showed to screen on supported environments, and returned as tensors or pandas dataframes. Check the detection.y file for an explicit explanation on own to use the model. I hope you enjoyed reading this. To analysis precision and recall we need to balancing two separate scores. Ideal conf-threshold for mAP computation is 0.0, 0.001 is in place for speed. Update to v5 2 ( #5) 6c7846a. The idea is very simple "instead of completely removing the proposals with high IOU and high confidence, reduce the confidences of the proposals proportional to IOU value".Now let us apply this idea to the above example. Already on GitHub? which can be set by: Models can be transferred to any device after creation: Models can also be created directly on any device: ProTip: Input images are automatically transferred to the correct model device before inference. BjarneKuehl pushed a commit to fhkiel-mlaip/yolov5 that referenced this issue on Aug 26, 2022. Hardware Platform (Jetson / GPU) dGPU T4 DeepStream Version 5.0 TensorRT Version 7.1 (probably) NVIDIA GPU Driver Version (valid for GPU only) 440.82 archs => 7.5 [deviceQuery] Built the yolov5s engine in DS docker container using tensorrtx/yolov5 which gives the below output for no of boxes detected: [inference . However, why does it show map@0.5IOU in the results? Give feedback. Since each bounding box has a score, we can sort them by descending order. Object Confidence Thresholding Our prediction tensor contains information about B x 10647 bounding boxes. Since more bounding boxes require more time in NMS, we should probably eliminate low-score predictions as they are likely not to survive NSM anyway. ProTip: TensorRT may be up to 2-5X faster than PyTorch on GPU benchmarks CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit. When an electromagnetic relay is switched on, it shows a dip in the coil current for a millisecond but then increases again. to your account, for editing nms to DIOU NMS, I add box_diou function and compare box_diou with iou_thres in general.py, iou = box_diou(boxes[i], boxes) > iou_thres # iou matrix <<. If the confidence threshold is high enough, it can remove many bounding boxes and dramatically improve the speed. Step 1 : Select the prediction S with highest confidence score and remove it from P and add it to the final prediction list keep. For simplicity, well deal with only one class (dog). Shouldn't it be the IOU threshold that is set? Published 31 Aug 2022 Abstract In track construction, it is an important and necessary guarantee for production safety to check the number of workers and tools before and after the track maintenance. We need to deal with multiple classes like cat and others in real object detection datasets. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Here I show my training results for person detection (only one class). to your account. parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') We simply return keep that stored our filtered predictions. The scalability, and robustness of our computer vision and machine learning algorithms have been put to rigorous test by more than 100M users who have tried our products. Sign in So in this way we keep only those predictions whose IoU with the current prediction S is less than the thresh_iou and remove all those with greater than threshold. I have searched the YOLOv5 issues and discussions and found no similar questions. You signed in with another tab or window. F-1 scores: Increasing the F1 score indicates an improvement in the overall performance of a model, Confidence -> is IoU Overlapping result. Notice below that P is a tensor containing tensors of the form (x,y, a,b,c). Why the default value is different in val.py and detect.py? Confusion matrix used in the test.run() is initialized with the default value of iou_thres=0.45. Asking for help, clarification, or responding to other answers. You switched accounts on another tab or window. You switched accounts on another tab or window. 0.1: nms_iou_threshold: IOU threshold used during inference in non-maximum suppression post processing. Use the flag --conf_thresh to change the threshold. for editing nms to DIOU NMS, I add box_diou function and compare box_diou with iou_thres in general.py. In this case, we perform NMS for each class as an object detection model outputs scores for all classes that the target dataset supports. Table Notes (click to expand) If the above blue and red boxes overlap with an IoU value of 0.5 or more, we say they are for the same object (dog in this case), and we should suppress (eliminate) the blue box because the red box has a higher score. So selection of threshold value is vital for the performance of the model. Verb for "Placing undue weight on a specific factor when making a decision". Woah !! . Usually, we take its value as 0.5, but it depends on the experiment you are doing.As discussed in the NMS algorithm above, we extract the BBox of highest confidence score and remove it from P. Now that we have a good grasp of how NMS works, let us implement it in PyTorch so that you can use it in your future Object Detection pipelines . In this paper, by introducing DIOU-NMS and Alpha-IoU, based on the YOLOv5 network structure, the non-maximum inhibition in the original YOLOv5 is replaced by the non-maximum inhibition based on DIOU_Loss, and the original IoU system is replaced by the -IoU system. Looking at the above settings, the IoU threshold (--iou) is also set low in the speed test so that the NMS execution is faster, excluding bounding boxes with a small overlap. Then, we calculate the IoU between the blue and red boxes as the intersection over the union area of the two boxes. conf_thres: (float) confidence threshold. After the full training phase, the evaluation of best and last checkpoints is triggered with the call to the test.run() with iou_thres=0.7 value for the NMS. Object Detection: Calculating mean Average Precision (mAP) with Confidence, YOLOv5 Transfer Learning In Simple Steps Without Losing Your Mind, Faster R-CNN: Real-Time Object Detection with RPN, Mask R-CNN: Faster R-CNN + Mask Branch (2017), Fast R-CNN: Understanding why its 213 Times Faster than R-CNN and More Accurate, Object Detection: Intersection over Union (IoU), RetinaNet: One-stage Detector + Focal Loss (2017), the speed test of YOLO v5 and the report of mAP, https://github.com/ultralytics/yolov5/blob/master/README.md, Smart Tech Information: From Concept to Coding. For example, we could set the confidence threshold to 0.05 and eliminate all bounding boxes with 0.05 or lower scores before starting NMS. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. I have trouble understanding the difference between 'conf-thres' and 'iou_thres' in val.py and detect.py. Is there a specific reason why is this changed in the YoloV5 to 0.6/0.7? Doing so does not change the nature of NMS. This means that detection scores are ranging from 0.0 to 1.0 and you must filter them with your confidence threshold. It can work well even when a dog and a cat are very close to each other since we are dealing with different classes separately, overlapping bounding boxes for different classes can survive NMS. 'yolov5s' is the lightest and fastest YOLOv5 model. We also get overlap threshold IoU thresh_iou. How to get class and bounding box coordinates from YOLOv5 predictions? So the mAP is the averaged of all object classes. @isJunCheng mAP is only accurate when evaluate all the way down to near zero confidence. Hello, yoloV5. These windows supposedly contain only one object, and a classifier is used to obtain a probability/score for each class. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. How to get a predicted image of YOLOv5 model? This course is available for FREE only till 22. However, it can sacrifice the accuracy (mAP) because a lower score does not necessarily mean the prediction is wrong. We define the function that takes in a list of prediction boxes along with their confidence score and a thresh_iou. It is a class of algorithms to select one entity (e.g., bounding boxes) out of many overlapping entities. For example, if you want to increase the speed, set the confidence threshold to a higher value (say, 0.25), and if you want to improve mAP, set the confidence threshold to a lower value (say, 0.001). The text was updated successfully, but these errors were encountered: Hello @eldarkurtic, thank you for your interest in YOLOv5! Hi, privacy statement. We can choose the selection criteria to arrive at the desired results. In our case index = order and input = x1. Modify Confidence Threshold and NMS IoU Threshold in realtime user sliders and instantly see the effects. Connect and share knowledge within a single location that is structured and easy to search. Do I need to use this confidence value for detection (!python detect.py --weights runs/train/exp4/weights/best.pt --img 416 --conf 0.352 --source ../dataset)? To receive this update: Thank you for spotting this issue and informing us of the problem. Making statements based on opinion; back them up with references or personal experience. If you look closely at the speed test of YOLO v5 and the report of mAP, you can see that the parameters are adjusted differently for speed and mAP. It is a float. For example, a model would predict a bounding box with confidence scores like 75% dog, 20% cat, and some probabilities for the other 78 classes. We got to know how NMS works and implemented it in PyTorch. How to get confidence score from a trained pytorch model. (So here, S is actually P[idx]). So all the other extra overlapping boxes have been cleared out and we get a nice and clean prediction. In other words, we take the maximum and suppress the non-maximum ones, hence the name non-maximum suppression. Models and datasets download automatically from the latest YOLOv5 release. It is based on the packaged ultralytics/yolov5. For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com. By clicking Sign up for GitHub, you agree to our terms of service and Then, well find the next highest score bounding box that does not overlap with the highest score bounding box to eliminate lower score ones. Have a question about this project? Pull Requests (PRs) are also always welcomed! Non-Maximum Suppression Speed versus mAP, 3.1. How do I get the coordinate where an edge intersects a face using geometry nodes? Custom data training, hyperparameter evolution, and model exportation to any destination. 1my_nms.pyPython . NMS iteratively removes lower scoring boxes which have an IoU greater than iou_threshold with another (higher scoring) box. Sign in privacy statement. Hi @glenn-jocher , I have been going through the code a bit and realized that the iou and iou_thresh are actually IoUs used for the non-max suppression and not for the mAP calculation. https://github.com/ultralytics/yolov5/wiki. By clicking Sign up for GitHub, you agree to our terms of service and Modify Confidence Threshold and NMS IoU Threshold in realtime user sliders and instantly see the effects: Wiki - https://github.com/ultralytics/yolov5/wiki Tutorials - https://docs.ultralytics.com/yolov5 Docs - https://docs.ultralytics.com If you are interested in reading about this I recommend that you check out this research paper Soft-NMS. It is a float. We set an IoU threshold (hyperparameter) to determine if two predicted bounding boxes are for the same object or not. So far, we have talked about one dog class only. This guide explains how to load YOLOv5 from PyTorch Hub at https://pytorch.org/hub/ultralytics_yolov5. Find centralized, trusted content and collaborate around the technologies you use most. We also initialise an empty list keep as discussed above to store the filtered predictions. When I say coordinate (x1,y1), I refer to the lower left coordinate of the BBox and (x2,y2) refers to the upper right coordinate of the BBox. Update: For example, if scores = [0.7, 0.3, 0.6], then order = scores.argsort() = [1, 2, 0]. add_argument ( '--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path (s)') I found that when the conf and IOU thresholds are set to 0.001 and 0.6, more training epochs are required to obtain better AP values. They do not just predict one bounding box per object. My question(s):-, In val.py 4. IoU (Intersection over Union) 3.3. mAP (mean Average Precision) 3.4. This one not sure, Reference https://medium.com/@shrutisaxena0617/precision-vs-recall-386cf9f89488, @glenn-jocher plz correct me if i am wrong. Question. Thank you in advance. Independence Day Sale 25% Discount on all Courses and Programs. Draw the initial positions of Mlkky pins in ASCII art. Access additional Ultralytics resources: Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. You can set these to whatever you want. Learn how your comment data is processed. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit. mAP@0.5 is when True Positiveness is those with IoU bigger than 0.5. Have a question about this project? @glenn-jocher what's your intuition behind this? See pandas .to_json() documentation for details. During the training test.run is called with the default value iou_thresh=0.6: And then, after the training phase, there is an evaluation of the best and last models, which is invoked with iou_thresh=0.7: @eldarkurtic ah this is only applies to the narrow use case of full COCO dataset training, so no other datasets will see this. order will contain the respective indices of the scores if it had been sorted. Why this isn't 0.5, so that the generated plots are consistent with the metric we are looking at (mAP@0.5)? Yes, I have noticed it since I am running Yolo on the full COCO dataset :) . To install run: YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled): If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. A few important aspects of each: YOLOv5 PyTorch Hub models areAutoShape() instances used for image loading, preprocessing, inference and NMS. I have tried searching for this online, but can't seem to find a good explanation. Regarding your question on calculating FPS, you are correct that the FPS calculation for YOLOv5 is the inverse of the total processing time for each frame, which is the sum of the preprocess, inference, and postprocess times you mentioned. NMS is the most commonly used algorithm for this task. IoU in mathematical terms can be represented by the following expression, Intersection Over Union(IoU) = (Target Prediction) / (Target U Prediction). As discussed in the NMS algorithm above, we extract the BBox with the highest confidence score and remove it from P. We select S using idx = order[-1] and then push this prediction in filtered box list keep. The Intersection over Union (IoU) metric, also referred to as the Jaccard index, is essentially a method used usually to quantify the percent overlap between the ground truth BBox (Bounding Box) and the prediction BBox. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, The future of collective knowledge sharing, Also to get confidence value in results as a pandas dataframe use. Each predicted bounding box has a confidence score which indicates how likely (the model believes) an object exists in a bounding box. train.py dataloaders are designed for a speed-accuracy compromise, val.py is designed to obtain the best mAP on a validation dataset, and detect.py is designed for best real-world inference results. YOLOv5 also uses that internally. In other words, anchor boxes provide reasonable priors for object shapes (width-height ratios) calculated from the training dataset. Looks like the NMS removed all the overlapping predictions and we got a very clean looking final prediction. So, we can use it to rank bounding boxes. Another way of eliminating lower score bounding boxes is to limit the number of bounding boxes we give to NMS. Non Maximum Suppression (NMS) is a technique used in numerous computer vision tasks. Step 2 : Now compare this prediction S with all the predictions present in P. Calculate the IoU of this prediction S with every other predictions in P. If the IoU is greater than the threshold thresh_iou for any prediction T present in P, remove prediction T from P. Step 3 : If there are still predictions left in P, then go to Step 1 again, else return the list keep containing the filtered predictions. We use cookies to ensure that we give you the best experience on our website. W11. @aonsafdar Hello, thanks for asking about the differences between train.py, detect.py and val.py in YOLOv5 . If there any questions, leave a comment below and I will try to answer them as soon as possible. You signed in with another tab or window. https://github.com/ultralytics/yolov5/wiki. You switched accounts on another tab or window. 586), Starting the Prompt Design Site: A New Home in our Stack Exchange Neighborhood, Testing native, sponsored banner ads on Stack Overflow (starting July 6), Temporary policy: Generative AI (e.g., ChatGPT) is banned, How to print the confidence level of a predicted image, Convert YoloV3 output to coordinates of bounding box, label and confidence, How find confidence for each classes in yolo darknet. Why is it better to control a vertical/horizontal than diagonal? If this is a Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you. In terms of accuracy, one image wont usually have so many ground truth bounding boxes, so it may still produce a good mAP. Why is this not the same as in the evaluation during the training, since the reported accuracy of the same model evaluated during the training (with iou_thres=0.6) and after the training (with iou_thres=0.7) most likely won't be the same? Now, lets extend the process to multiple classes. Any help is appreciated. UPDATED 26 March 2023. to sort license plate digit detection left-to-right (x-axis): Results can be returned in JSON format once converted to .pandas() dataframes using the .to_json() method. Have a question about this project? import yolov5 # load model model = yolov5.load . Next, we find intersection bounding box of the selected prediction S = P[idx] with all other predictions left in P. A thing to note is that(xx1,yy1) is the lower left corner of intersection BBox and(xx2,yy2)is the upper right corner of intersection BBox, Since to find the xx1 coordinate of the intersection BBox of the selected prediction S with any prediction T in P, we do xx1 = max(S.x1, T.x1). if box_diou function is correct, is this enough to convert nms to DIOU nms?? Before we discuss how NMS works, we must try to answer why we need it first. Thats it from my side. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Since we have less number of bounding boxes handled by NMS, it could run a lot faster depending on the limit. Example: python detect.py --source data/images --weights yolov5s.pt --conf 0.25. Their union area is the orange area, and their intersection area is the purple area. The YOLOv5n approach achieved the highest mAP of 77.40% for small and large instances, followed by the YOLOv5m model having a mAP of 75.30%. COCO dataset format support (for training) 4. nms torchvision.ops.nms(boxes: Tensor, scores: Tensor, iou_threshold: float) Tensor [source] Performs non-maximum suppression (NMS) on the boxes according to their intersection-over-union (IoU).
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