Yolov7 augmentation. This method involves cropping and stitching nine images, effectively reducing the interference of harmful backgrounds on target features and contributing to the enhancement of the network’s generalization ability. Contribute to pahrizal/YOLOv7-Segmentation development by creating an account on GitHub. Next, we’ll download our dataset in the right format. Create a file with the name “custom. jp looks like that: Apr 19, 2023 · YOLOv7 significantly boosts both speed and accuracy. p (float, optional): Probability of applying the mosaic augmentation. Data augmentation techniques comes down to processes within position augmentation and color augmentation. Module 3 YOLOv7 + Tracking. 2 Training YOLOv7 in Colab. Open the terminal and enter the following command to download the project into the current directory. Create a custom hyperparameter file (e. By incorporating the variability attention module into the backbone network Nov 1, 2022 · Hi, I trained a model with the in yolov5 and got a map@50 of 0. Jan 26, 2023 · I think this modification will work, just take the first 4 elements of each line (bbox info), it will draw the bbox of your polygon. Unmanned surface vessel (USV) target detection algorithms often face challenges such as misdetection and omission of small targets due to significant variations in target scales and susceptibility to interference from complex environments. Four components make up the YOLOv7 algorithm: input, backbone, neck, and head. In our case, we use 20 000 images in total, including 15 000 for training, 4 000 for validation and 1 000 for testing. imgsz (int, optional): Image size (height and width) after mosaic pipeline of a single image. showcasing the effectiveness of the YOLOv7 with test-time augmentation in detecting small birds. Contribute to laitathei/YOLOv7-Pytorch-Segmentation development by creating an account on GitHub. pt is the largest and most accurate model available. Feb 3, 2021 · This YOLOv5 update implements copy-paste augmentation using the new copy_paste hyperparameter (set from 0-1 for 0-100% of labels copied and pasted). Module 2 Training Custom YOLOv7. According to Fig. Specify the class name and the estimated anchor boxes. Researchers aim to endow drones with these attributes in order to improve performance when patrolling in controlled areas for object detection. データ拡張(augmentation) データの拡張(augmentation)はデータを加工してデータを水増しする作業です。 今回はalbumentationsを使いました。 albumentationsは画像だけでなくアノテーション作業で作ったtxtファイルのBBox座標も変換してくれます。 albumentations公式 Jul 5, 2020 · Before trying TTA we want to establish a baseline performance to compare to. 76%, 95. Oct 16, 2023 · To address the problem of insufficient extraction of key features in images by YOLOv7, He-YOLOv7 adopts MixUp and Mosaic -6 mixed data augmentation techniques in the data augmentation module to better simulate mutual occlusion between targets in images, increase training difficulty, enrich the background information of samples, and improve the Aiming at the problems of insufficient expression ability and imperfect positioning loss function of YOLOv7 model in the single-stage detection network, an improved YOLOv7 model for insulator surface defect detection is proposed. YOLOv7は2022年7月に公開された最新バージョンであり、速度と精度の面で限界を押し広げています。. Inside the notebook, we first mount the Google Drive: from google. Trainable bag of freebies. YOLOX-CoAtNet. g. import os. Learn about the basic architecture of YOLOv7. YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. HSV) augmentation applied to individual images instead of entire mosaics. Head. For example, planet_14. py. 5 MB. If you want train on instance segmentation: python train_inseg. YOLOv7 network combined with various data augmentation methods. This YOLO v7 instance segmentation tutorial is focused on using official pre-trained YOLO v7 mask model. 3%. YOLOv7 outperforms both convolution-based and transformer-based OD models. App 1 - Security with Dashboard ; App 2 - Mining Safety Check ; App 3 - Retail Heat Maps Mar 27, 2024 · Differing from the original YOLOv7-tiny model, we applied the Mosaic-9 data-augmentation method to process input images. YOLOv5のデータ拡張ですが、Hyperparametersで YOLOv7 arch with resnets backbone; YOLOv7 arch with resnet-vd backbone (likely as PP-YOLO), deformable conv, Mish etc; GridMask augmentation from PP-YOLO included; Mosiac transform supported with a custom datasetmapper; YOLOv7 arch Swin-Transformer support (higher accuracy but lower speed); YOLOv7 arch Efficientnet + BiFPN; YOLOv7 Full Stack Object Detection Course. yaml file to include your desired augmentation settings under the appropriate keys (e. 2 \(\%\) in the YOLOv7-4, YOLOv7-FEAM, and YOLOv7-Trans models. 15% and 0. 9 % and 0. Note this is only available for segment labels, not for box labels. /weights/best. The Omni I am trying to see the effect of data augmentation on the yolov7 results. It has the highest accuracy (56. DATASET_PATH = r"data/dataset_enero30_noaug/". Jun 8, 2023 · Abstract. Nov 8, 2022 · A DA-YOLOv7 model was established via the YOLOv7 network combined with various data augmentation methods. Model [25] proposed an improved algorithm of YOLOv7 based on HorNet convolution and the BoTNet attention mechanism, which overcame 3. I trained a model in yolov7 with the same data and params and only get a map@50 of ~0. It can be seen that our model has a tremendous advantage in terms of performance. 3% AP, 2. I cover how to set up the environment, prereqs for t Directly edit the default. The DA-YOLOv7 model had the best detection performance and a strong generalisation ability Aug 15, 2023 · Download the YOLO v7 project. Module 1 YOLOv7 Intro + Theory . Data augmentation is a key technique for increasing the accuracy of object detection datasets. 433 MB. 28% and 0. A DA-YOLOv7 model was established via the YOLOv7 network combined with various data augmentation methods. YOLOv7 has the higher accuracy value, with 56. train() command. With this full-stack object detection Feb 26, 2024 · We conducted a results analysis of the YOLOv7 model on the dataset [13, 14] and compare its performance with other deep-learning architectures including YOLOv3 and YOLOv5. I get very weird results The model without data augmentation Feb 22, 2024 · Step 4: Create a Custom Configuration File for Training. 5 % AP for medium and large targets, respectively. py Jan 17, 2024 · The YOLOv7 detection algorithm is the latest open source model algorithm from the YOLOv4 team. 👍 2 IamSVP94 and lordet01 reacted with thumbs up emoji All reactions Aug 8, 2023 · In this work we implement photometric da ta augmentation strategies to improv e YOLOv7-p5. This allows for the model to learn how to identify objects at a smaller scale than normal. This YOLO v7 tutorial enables you to run object detection in colab. 3, the backbone is built using convolution, the E-ELAN module, and the SPPCSPC module; the neck is made up of Feature Pyramid Network (FPN) + Path Aggregation Network (PAN); and the head is chosen to represent the three target sizes of the large, medium, and small Dec 4, 2023 · When working with objects on a smaller scale, higher detection accuracy and faster detection speed are desirable features. The DA-YOLOv7 model had the best detection performance and a strong generalisation ability in complex scenes, with mAP, Precision, Recall, F1 score and average detection time of 96. YOLOX with panoptic segmentation. 00% , which is about 7% higher than the baseline model (YOLOv7). What makes YOLOv7 more efficient? Architecture summary. Second, deep learning methods, especially those used for small infrared target recognition, still need to address the difficulties associated with Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors - Issues · WongKinYiu/yolov7 Feb 9, 2024 · This paper proposes the TF-YOLOv7 model to address the mutual occlusion problem of targets in UAV instance segmentation, as shown in Fig. 1 YOLOv7 algorithm principle. We must now develop a customized configuration file. YOLOv7 weights are trained without pre-trained weights using the COCO dataset from Microsoft. 54%, 95. Furthermore, the YOLOv9e model sets a new standard for large models, with 15% fewer parameters and 25% less computational need than YOLOv8x , alongside a Nov 12, 2023 · The augmentation is applied to a dataset with a given probability. , hsv_h, hsv_s, hsv_v, degrees, translate, etc. Attributes: dataset: The dataset on which the mosaic augmentation is applied. metrics without the need to add extra images to the da taset. 21% compared Jun 6, 2023 · Save Augmentation. 1. -Y Feb 26, 2024 · It operates with 42% fewer parameters and 21% less computational demand than YOLOv7 AF, yet it achieves comparable accuracy, demonstrating YOLOv9's significant efficiency improvements. YOLOV7-TINY-AnchorFree. To train on coco, the commond is very simple. Experimental results show that the YOLOv7 model is 551% faster and 2% more accurate than the Transformer-based model SWINL Cascade Move your (segmentation custom labelled data) inside "yolov7-segmentation\data" folder by following mentioned structure. 75 and map of ~0. YOLOv7-E6 object detector (56 FPS V100, 55. , custom_hyps. you can use the python train_det. In comparison to its earlier iterations, the official YOLOv7 offers incredible speed and precision. The You Only Look Once (YOLO) series, has incorporated various data-augmentation training strategies to improve model performance. To receive this update: Mar 12, 2024 · Due to the broad usage and widespread popularity of drones, the demand for a more accurate object detection algorithm for images captured by drone platforms has become increasingly urgent. The library offers various convenience methods and classes to help visualize results beautifully, but the underlying implementation for detection is a Mask R-CNN. It indicates that the small object detection layer, FEAM module, and Trans module can increase the network performance of small object detection in airport surface surveillance. 4 % AP for small targets, and 1. May 21, 2021 · * you have to have original image, labeled datagithub (source code):https://github. 4% increment in the cost and model size, respectively. 3 demonstrate the excellent performance of the proposed model on the validation dataset. Here, take a look: It takes 4 pictures (as set in batch-size = 4), but they are not the original pictures. detector = yolov4ObjectDetector( "tiny-yolov4-coco" ,className,anchorBoxes,InputSize=inputSize); Nov 1, 2023 · Following, data augmentation techniques are applied to increase the variability of the training samples, overcome overfitting and enhance the performance of the detection model. ONNX, PyTorch, TensorRT, DeepSparse, CoreML; Module 6 YOLOv7 Apps . Module 4 Flask Integration . To realize the automatic picking of dragon fruit, this paper proposes a detection method of dragon fruit based on RDE-YOLOv7 to identify and YOLOV5改进-CONTEXT_AUGMENTATION_MODULE 08:01 YOLOV5改进-添加辅助训练头 07:12 YOLOV7改进-添加Deformable Conv V2 YOLOV7改进-具有隐式知识 With its amazing characteristics, YOLOv7 is a real-time object detector that is now transforming computer vision. yaml” in the (yolov7/data) folder. pt, yolov5m. 1. Mosaic [video] is the first new data augmentation technique introduced in YOLOv4. We enhanced the YOLOv7 model and conducted relevant experiments. Mar 26, 2023 · YOLOv7 seg 標註範例. 12 mins. For more details, please refer to our report on Arxiv. This option also resulted in the highest mAP, and lowest model size and cost compared YOLOv5 当社のAIアーキテクチャの最新バージョンである v7. We'll cover the following. com/saehan-choi/Fancy/blob/main/yolo_format_data_augmentation. Leveraging a synergy of advanced techniques such as Group Convolution Dec 1, 2023 · Compared with the YOLOv7 model, our model has a higher overall detection performance than YOLOv7 and improves the overall detection accuracy by 2. 修改 In the YOLOv7 architecture, the head responsible for the final output is called the lead head, and the head used to assist in training is called the auxiliary head. Then install the required packages. 762. 8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. - Use cv2. 884 and a map of 0. One such possible application domain can be semiconductor defect inspection. Aug 27, 2022 · YOLOシリーズの2022年最新版「YOLOv7」について、環境構築から学習の方法までまとめます。. 65, fir Mar 31, 2023 · There is a great demand for dragon fruit in China and Southeast Asia. py will automatically apply some augmentation only for detection. 1%. 12% improvement in mAP and only 0. YOLOv7 segmentation pytorch implementation guide. If you want to save the transformed images and labels. Let’s take a look at how this process works given the following 4 images and wanting a final image size of 256×256: 4 images to Mosaic together. The Ultralytics YOLOv8 repo supports a wide range of data augmentations. 025 s per image Nov 17, 2023 · Instance Segmentation with YOLOv7. The DA-YOLOv7 model had the best detection performance and a strong generalisation ability in complex scenes, with mAP, YOLOv7-DCN object detection algorithm uses DCNv2 as the backbone network to detect the main targets in fishing vessel operations, improving the network's ability to detect deformable targets. The main structure of YOLOv7 model is circled by the red rectangle, and the numbers near Feb 19, 2023 · The field of object detection using Deep Learning (DL) is constantly evolving with many new techniques and models being proposed. 643 MB. 9% AP) outperforms both transformer-based detector SWIN- Oct 23, 2021 · Thanks for asking about image augmentation. imwrite function to save transformed images. This is a complete tutorial and covers all variations of the YOLO v7 object detector. 4 Run YOLOv7 on Windows 10/11. Resize any remaining bounding boxes that are cut off by the cutout. Jan 13, 2021 · The high level augmentation overview is here, you can see augment_hsv() working correctly, modifying an entire image (and background). The visual guidance system is an important part of a picking robot. 5. 2. 025 s per image May 4, 2023 · To increment your custom dataset with data augmentation, you will need to modify your dataset configuration file, which is typically a . 00%, which is about 7% higher than the baseline model (YOLOv7). Dec 12, 2023 · 3. Module 5 Model Conversion . com Jan 19, 2023 · Figure 12: Tree structure of the "Yolov7_Dataset" folder For each folder, there is an images folder, containing the images, and a labels folder containing the associated labels generated previously. YOLOv7 outperforms YOLOR, YOLOX, Scaled YOLOv4. In addition, the difference between YOLOV7 and MobileOne-YOLO after data enhancement was not significant, indicating that MobileOne-YOLO was able to effectively detect the Creating Your coco dataset or Just coco. The pipeline weld surface defect detection model in this paper is shown in Fig. Data Augmentation: Key takeaways. Furthermore, the model Jan 12, 2023 · The second step is to create a Colab notebook, set a GPU for processing using Edit, Notebook settings, and choosing GPU. Default to 640. 7 \(\%\) to 5. Firstly, we introduce a specialized detection branch designed to . In this paper, an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks, such as the irregular shapes and varying sizes of ships. pt. 第7回目はYOLOv7によるInstance segmentationを紹介します。. ). py --config-file xxx. Images are never presented twice in the same way. colab Dec 7, 2023 · Therefore, data augmentation is necessary to improve model accuracy. この最新リリースに取り組んでいる間、私たちは2つの目標を常に念頭に置いていた YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56. In summary, the proposed YOLOv7-PSW lightweight method realizes the light weight of the model while maintaining high accuracy. As discussed in Step 2, there are two ways of passing class labels along with bounding boxes coordinates: 1. Oct 1, 2023 · As outlined in Table 3, results indicate that YOLOv7_CBAM4 emerged as the best YOLOv7 + CBAM model as when compared to the baseline YOLOv7 model, there was a 1. 98. Aug 16, 2023 · In order to solve the small-target-detection problem 3. YOLOv7 was trained only on MS COCO dataset without using a pre-trained model . 4% AP on my machine, so I close the albumentations augmentation. So I have retrained the model with the original parameters/code from the paper, next I want to turn off all data augmentations in the code, so I can see the performance of the model wo data augmentation. This is of great significance for promoting the application of object detection algorithms on edge devices. 3. On the ODv2 challenge dataset, the AP result of YOLOv7-sea is 59. You can try register your down Jul 21, 2022 · Just because it cause +50% training time but only increase +0. - Before saving the augmented label need to convert into yolo YOLOv7 segmentation pytorch implementation guide. Introduction. 13 mins. For this research, we ha ve used a dataset. The new method uses the Mish activation function to replace the original activation function, which further improves the expression ability of the network. Dec 15, 2023 · Utilizing YOLOv7 model with test-time augmentation, our approach involves increasing the input resolution, incorporating multiscale inference, considering flipped images during the inference process, and employing weighted boxes fusion to merge detection results. pt, or you own checkpoint from training a custom dataset . yaml --num-gpus 1 function: The train_det. To achieve further im-provements to our proposed YOLOv7-sea, we provide some useful strategies such as data augmentation, Test time aug-mentation (TTA), and bundled box fusion (WBF). The results of the study demonstrated that SC4-YOLOv7 significantly improved the average accuracy of recognizing air conditioner external units in street view images from 87. specify the name of the pretrained YOLO v4 detection network trained on COCO dataset. Mar 5, 2024 · In this study, we proposed a lightweight ship detection model for SAR images based on YOLOv7, with rotated bounding boxes. 93% to 91. git clone https://github. The dataset used for Nov 18, 2023 · Compared with the YOLOv7 model, the AP values of persons and trucks are increased by 0. Google colabを Nov 12, 2023 · YOLOv7 is a state-of-the-art real-time object detector that surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS. YOLOv5 🚀 applies online imagespace and colorspace augmentations in the trainloader (but not the testloader) to present a new and unique augmented Mosaic (original image + 3 random images) each time an image is loaded for training. Techniques like mosaic data augmentation, which combines multiple images into a single training sample, and dynamic shape training contribute to improved performance across diverse datasets. We then select the widely used YOLOv7 algorithm as the foundation and conduct a comprehensive Jan 16, 2024 · As for YoloV7, it is observed that due to its ability to apply native data augmentation using combination and aggregation techniques, it provides, in scenarios with other more basic techniques, a Dec 29, 2023 · YOLOv7 is a real-time object detection model and is one of the state-of-the-art single-stage detectors. 3 Data Augmentation. We rigorously explore the impact of each technique on detection performance. 5, outperforming the baseline model YOLOv7 by 5. 何か間違っていること等あればご指摘いただき、内容を充実させていければと思います。. 24 The researchers redesigned the model by redesigning the auxiliary head and lead head for the label assignment piece. import cv2. 7% mAP@0. The improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically Mar 9, 2024 · YOLOv7 leverages advanced data augmentation techniques to enhance the model’s robustness and generalization capabilities. Resize the images to the final image size (256×256). Traffic sign detection is a crucial task in computer vision, finding wide-ranging applications in intelligent transportation systems, autonomous driving, and To achieve further improvements to our proposed YOLOv7-sea, we provide some useful strategies such as data augmentation, Test time augmentation (TTA), and bundled box fusion (WBF). The YOLOv7-DCN-SORT target counting algorithm utilizes the YOLOv7-DCN obtained in the detection phase as the target detection model. See full list on learnopencv. pt and yolov5l. This command tests YOLOv5x on COCO val2017 at image size 640 pixels. yolov5x. yaml file. Jan 22, 2024 · Improved YOLOv7 network. Go to the data folder, create a file with name custom. 8%. 19 mins. Finally, we validate the improved performance of our new hybrid approach through empirical experimentation, and thus confirm its contribution to the field of target recognition and detection in remote sensing images. (Be sure to specify the proper directory), as the training process will be entirely dependent on that file. This study zeroes in on optimizing the YOLOv7 algorithm to boost its operational efficiency and speed on mobile platforms while ensuring high accuracy. The results in Section 4. Compared to its previous versions, YOLOv7 incorporates a series of improvements, including structural adjustments, data augmentation, and training strategies. Following, we have compared Yolov5 and Yolov7 single shot object detection algorithms in view of their highly achieved results ( Ultralytics/Yolov5 , 2020/2022 ; C. python opencv oop data-augmentation yolov7. yaml and paste the mentioned code below inside that. Manual picking of dragon fruit requires a lot of labor. Moreover, YOLOv7 outperforms other object detectors such as YOLOR Jan 18, 2023 · When looking at my created training data, it seems like yolov7 "merged" the pictures, so the pictures it learns from look like abominations from the original data. Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. @mkhoshbin72 I am doing it. Aug 16, 2023 · The results demonstrated that our enhanced YOLOv7 model outperforms the original network, exhibiting significant improvement in leakage reduction, with a mean Average Precision (mAP) of 81. Use the YOLOv7 PyTorch export. In this paper, we propose an improved YOLOv7 model. Apr 13, 2023 · The YOLOv7 model preprocessing strategy is combined with the YOLOv5 model preprocessing technique, and mosaic data augmentation is appropriate for identifying small objects. com Apply data augmentation techniques on YOLO v7 format dataset. I have not tested image-space (i. Note that this model requires YOLO TXT annotations, a custom YAML Jun 29, 2023 · Additionally, you may employ techniques like data augmentation, adjusting anchor sizes, or implementing more advanced architectures like YOLOv4 or YOLOv5. import sys. 1 ExperimentalSetup The experiments were conducted using the YOLOv7 model with different input resolutions, incorporat-ing multiscale inference, flipped image considera-tion, and data augmentation. YOLOv7 architecture is based on previous YOLO architectures of YOLOv4, YOLO-R, and scaled YOLOv4. 0が リリースされ、新しいインスタンス・セグメンテーション・モデルをご紹介できることを嬉しく思います！. 1 YOLOv7 Object Detection on Images, Video & WebCam in Google Colab. The performance of any machine learning model depends Jul 9, 2022 · はじめに. YOLOv7 is a state-of-the-art object detector based on the YOLO family of models which have become popular for industrial applications. The model introduces the Swin Transformer structure in the backbone network, which constructs hierarchical feature maps by fusing deep network feature blocks and performs attention computation only in the local window with linear computational complexity Mar 4, 2024 · As mobile computing technology rapidly evolves, deploying efficient object detection algorithms on mobile devices emerges as a pivotal research area in computer vision. 由於我的資料集已標註過，只需將格式進行轉換(VGG Image Annotator → YOLO format)，程式已放在github，有興趣的可以上去看看。. YOLOX-GSConv Slim-neck by GSConv. 8% AP) among all known real-time object detectors with 30 FPS or higher on GPU V100. Oct 30, 2023 · Download Correctly Formatted Custom Data. To address these issues, we propose a small target enhanced YOLOv7 (STE-YOLO) approach. 2 and Section 4. yaml ) with your desired augmentations and pass the path to this file using the cfg parameter in the model. Thanks! Domains Augmentation and an Improved YOLOv7 Jian Zhang 1,2, Xinyue Yan 2, Kexin Zhou 2, Bing Zhao3,*, Yonghui Zhang1, Hong Jiang1, Hongda Chen2 and Jinshuai Zhang2 Create the YOLO v4 object detector by using the yolov4ObjectDetector function. Aug 13, 2023 · The experimental results on the challenging public dataset TT100K demonstrate that the SANO-YOLOv7 algorithm achieved an 88. This article addresses this issue by first analyzing the unique characteristics of datasets related to drones. A standard library used for instance segmentation, object detection and key point estimation in Python is Detectron2, built by Meta AI. Pass an image and bounding boxes to the augmentation pipeline and receive augmented images and boxes. In addition, examine the impact of data pre-processing and augmentation techniques and annotation skills on the final localization and detection accuracy. Pass class labels along with coordinates. Inside this file, you will need to add an augmentation section with parameters that specify how you want to augment your data. The YOLOv7 network with the best performance was selected. Backbone. Step 4. It is imperative to study the dragon fruit-picking robot. Improvement arising in of satellite YOLOv7 surveillance, Object-Detection Yu et al. YOL Dec 21, 2023 · This module optimizes the selection process between YOLOv7 and DETR, and further improves object detection accuracy. May 31, 2023 · 4. Nov 1, 2023 · The PR curves for YOLOV7 and MobileOne-YOLO show a significant increase in the closed region when data augmentation is applied, indicating improved model performance. Get. Lecture 1. Other options are yolov5s. 03%, 94. You can train with more data, change input size, add augmentation or change hyper parameters and train the model and see if map changes or not. May 13, 2020 · Mosaic data augmentation - Mosaic data augmentation combines 4 training images into one in certain ratios (instead of only two in CutMix). YOLOv5のデータ拡張 (水増し、Data Augmentation、データオーギュメンテーション)について、調べたことをまとめます。. Here's a short recap of everything we've learned: Data augmentation is a process of artificially increasing the amount of data by generating new data points from existing data. e. YOLOv7 uses the lead head prediction as guidance to generate coarse-to-fine hierarchical labels, which are used for auxiliary head and lead head learning, respectively. Train the custom yolov7 Model Meanwhile, YOLOv7-PSW can also be applied to other computer vision tasks to improve its performance and efficiency. Must be in the range 0-1. May 20, 2022 · Remove bounding boxes that aren’t in the cutout. Readme. aj ub zf yw pn fy qz zt xk bx