Pytorch custom image dataset. 3. This architecture is trained to do segmentation of the 20+1 classes of the Pascal VOC 2012 Dataset (20 foreground and 1 background class). datasets module. Run the notebook in your browser (Google Colab) Read the Getting Things Done with Pytorch book; Here’s what we’ll go over: Install required libraries; Build a custom dataset in YOLO/darknet format Aug 28, 2020 · load file0 with 50000 samples and keep it as an attribute. So I have this line of code to load a dataset of images from two classes called "0" and "1" for simplicity: train_data = torchvision. The shortcoming of this approach would be that you wouldn’t be able to easily shuffle the data. 2 Create a dataset class¶. path. I already have built an image library (in . This function will allow us to identify the number of items that have been successfully loaded from our custom dataset. 000 images / 2 classes / 10. Oct 22, 2019 · 1. train_data = datasets. from torchvision import transforms. Normalize([0. TensorDataset() and torch. Define a custom dataset. data_loader = torch. import numpy as np. I don’t have any an idea about how to combine those images and ID and converting into tensors. It will give you one pair of pictures in each iteration of the loop. The patches are a by-product of __getitem__() hence the dataloader doesn't really know about those. However, when trying to return the embedding size tuples, I am not getting tuples but tensors and I’m not sure why. __len__() == 500: ### Load dataset and do a transform operation on the data In a previous version of the software the requirement was simply to retrieve the images from a folder, so it was quite simple to load all the images Nov 8, 2021 · Note that this is necessary since we used OpenCV to load images in our custom dataset, but PyTorch expects the input image samples to be in PIL format. map-style and iterable-style datasets, customizing data loading order, automatic batching, single- and multi-process data loading, automatic memory pinning. Jan 10, 2021 · Preparing the image dataset. The issue lies here: The dataset by itself contains 2 folders Train and Test. cat. csv : contains all ID of Image like 4325. I read up the pytorch tutorials on custom dataloaders but most of them are written considering the dataset is in a csv format. For a simple example, you can read the PyTorch MNIST dataset code here (this dataset is used in this PyTorch example code for further illustration). Torchvision provides many built-in datasets in the torchvision. And there’s a csv file Jan 23, 2023 · I have some images organized in folders as shown in the following picture: In order to create a PyTorch DataLoader I defined a custom Dataset in this way. Today, YOLOv5 is one of the official state-of-the-art models with tremendous Nov 8, 2021 · Loading Image datasets in custom data loader - PyTorch Forums. 4. It is suppose to look something like these images. e, they have __getitem__ and __len__ methods implemented. Dataset Transforming and augmenting images. Hello. There happens to be an official PyTorch tutorial for this. Introduction. ImageFolder(root=TRAIN_PATH, transform=transforms. Dataset is the main class that we need to inherit in case we want to load the custom dataset, which fits our requirement. Jan 18, 2023 · The MNIST dataset is a widely used dataset for handwriting recognition and is a great dataset to use as an example for creating a custom dataset in Pytorch. INPUT_IMAGE_WIDTH) that our model can accept Oct 18, 2019 · 2. 05587. I have a separate Images folder and train and test csv file with images ids and labels . Image of shape [3, H, W], a pure tensor, or a PIL Image of size (H, W) target: a dict containing the Nov 20, 2019 · A simple image classification with 10 types of animals using PyTorch with some custom Dataset. Oct 28, 2020 · When I've enough images I want to load my list of images using Pytorch as if it was a dataset if img_list. image_size - the spatial size of the images used for training ImageFolder. join(TRAIN_DATA_DIR), train_transform) and then I prepare the loader to be used with my model in this way: train_loader = torch. Simple pytorch example: import cv2. Let's define a function that will take a list of images' file paths and their labels and visualize them in a grid. One of the main reasons I started writing this article was because I wanted to try coding GANs on a custom image dataset. ai based in New Jersey. DataLoader class. #include <torch/torch. Let’s take a look at both these options. I used ImageFolder but this doesn't load gray images by default as it converts images to RGB. glob("D:\\Neda\\Pytorch\\U-net\\my_data\\imagesResized\\*. WIP is an acronym for Work-In-Progress Apr 8, 2023 · Before we begin, we’ll have to import a few packages before creating the dataset class. Your custom dataset should inherit Dataset and override the following methods: Nov 29, 2018 · I split the previous dataset two three groups of train, validation and test and here is the code: from custom_dataset import CustomDataset. class MyDataset(torchvision. ConcatDataset after loading the lists, for example (where trans is a set of pre-defined Pytorch transformations): l = [] l. no_grad(): detections_batch = ssd_model(tensor) By default, raw output from SSD network per input image contains 8732 Aug 31, 2020 · This post will discuss how to create custom image datasets and dataloaders in Pytorch. The only specificity that we require is that the dataset __getitem__ should return a tuple: image: torchvision. Most tutorials I came across were using one of the popular datasets (such as MNIST, CIFAR-10, Celeb-A, etc) that come pre-installed into the framework and ready to be used out-of-the-box. When running this with the images, the output looks like this. dog … rat. class CustomDataset(Dataset): def __init__(self, root, dirs=None, transforms=None): self. torch. transform function on your image. The loader is an instance of DataLoader class which can work like an iterable. Inside Train there are 26684 images. 0. A simple demo of image classification using pytorch. Learn about the PyTorch foundation. class ImageFolderCustom(Dataset): def __init__(self, targ_dir, transform=None): Jul 13, 2023 · Export in YOLOv5 Pytorch format, then copy the snippet into your training script or notebook to download your dataset. SubsetRandomSampler also passing the indices. Then, we initialize and build the vocabs for both source and target columns in our train data frame. from torch. ImageFolder. Jul 20, 2022 · @CasellaJr If you don't want to get into the headache of setting your own custom dataset via a class as in the docs in the comment above then I'd suggest the following: 1 - Create "positive" and "negative" folders in each of the train and test folders. transform ( callable, optional) – A function/transform that takes in a PIL image and returns a May 26, 2018 · Assuming you have wrapped your data in a custom Dataset object: Loading train/val/test datasets with images in separate folders using Pytorch. For example, [5000, 3000, 1500,…], which has a length of 10 because there are 10 classes. I found a VAE code online. total_imgs = natsort. v2 modules. png. It represents a Python iterable over a dataset, with support for. Jan 9, 2019 · What you need to do, is to get your data from somewhere and convert it into a Tensor, but this is up to you. prepare_input(uri) for uri in uris] tensor = utils. All datasets are subclasses of torch. Dataset class: def __init__(self, ds_main, ds_energy): self. As of now, I have my images in two folders structured like this : Folder 1 - Clean images. Also, we compare three different approaches for training viz. You can see from the output of above that X_batch and y_batch are PyTorch tensors. Train object detector on multi-class custom dataset using Faster R-CCN in PyTorch. Resume training by loading model checkpoint. Now lets talk about the PyTorch dataset class. Join the PyTorch developer community to contribute, learn, and get your questions answered. Learn how our community solves real, everyday machine learning problems with PyTorch. The problem is that it gives always the same error: TypeError: tensor is not a torch image. To do so, l have tried the following import numpy as np import torch. The data directory structure is as follows: I want to Sep 18, 2021 · 0. import matplotlib. transforms = transforms. Datasets that are prepackaged with Pytorch can be directly loaded by using the torchvision. Examples of various machine learning data sets can be found here. png") Apr 24, 2020 · It won’t divide the folders automatically. jpg and img3. In your class BlurDataset you only return one image in the __getitem__ method. These two datasets are paired (original and ground truth image). test. Feb 25, 2021 · I use a custom DataLoader class to read the images and the labels. , config. I would like to build a torch. manual_seed(42) We’ll import the abstract class Dataset from torch. ImageFolder): Let’s create a dataset class for our face landmarks dataset. Apr 8, 2023 · loader = DataLoader(list(zip(X,y)), shuffle=True, batch_size=16) for X_batch, y_batch in loader: print(X_batch, y_batch) break. from_numpy(landmarks)} so I think it returns a tensor already Feb 7, 2020 · I want to load a dataset of grayscale images. e. Resize(): allows us to resize our images to a particular input dimension (i. Usually approprietly chosen augmentations leads to better results. tif. pyplot as plt import seaborn as sns import torch import os from skimage import io, transform from torch import nn, optim from torch. total_imgs = natsorted(all_imgs) Now we need to define the two specialized function for our custom dataset. Your dataset should be a folder that contains a set of sub-folders. h>. Each image is going to be with a shape as (3, 200, 200) Also I have something like 40 images on each folder (train and test) How dose it look my data folders? train. # get all the image and mask path and number of images. Assuming you only plan on running resent on the images once and save the output for later use, I suggest you write your own data set, derived from ImageFolder. The requirements for a custom dataset implementation in PyTorch are as follows: Must be a subclass of torch. Apr 8, 2020 · In this video we have downloaded images online and store them in a folder together with a csv file and we want to load them efficiently with a custom Dataset Oct 5, 2018 · Hello, I have a dataset composed of labels,features,adjacency matrices, laplacian graphs in numpy format. root (str or pathlib. csv file where 1st column is filename of images in training set and second column has varying number of labels. This continues forever. You are right the preliminary augmentation of your dataset and saving augmented images consumes all the disk memory in the case of big datasets. My model Aug 27, 2019 · So, here I have images’ path & all 3 classes’ label path, as you can see our custom dataset loader will return images, labels1, labels2, labels3 imread() will read the images. I’ve created a custom dataset class (code bellow) and I would like to know if I’m thinking it right. We will talk more about the dataset in the next section. I’m loading the model and modifying the last layer by: Apr 6, 2021 · I’m very new to PyTorch or python although I know basics of programming. Or manually prepare your dataset. inputs = [utils. DataLoader(train_data, TRAIN_BATCH_SIZE Jan 21, 2022 · You can make a PyTorch dataset for any collection of images that you want, e. from_numpy(image),‘masks’: torch. The "normal" way to create custom datasets in Python has already been answered here on SO. folder_data = glob. For this story, I’ll use my own example of training an object detector for the DARPA SubT Challenge . Sample of our dataset will be a dict {'image': image, 'landmarks': landmarks}. If you want to get all 5 pairs of pictures in the . Multiple pre-loaded datasets are much simpler to load and use for training using Dataset and Dataloader class. This framework has the follow features: It is based on PyTorch framework. Popular datasets such as ImageNet, CIFAR-10, and MNIST can be used as the Oct 4, 2021 · In the previous sections of this PyTorch Data Loader tutorial, we learned to download a custom dataset, structure it, load it as a PyTorch dataset and access its samples with the help of DataLoaders. In Pytorch, these components can be used to create deep learning models for tasks such as object recognition, image classification, and image segmentation. transform ( callable, optional) – A function/transform that takes in a PIL image and returns a Nov 29, 2018 · I have two dataset folder of tif images, one is a folder called BMMCdata, and the other one is the mask of BMMCdata images called BMMCmasks(the name of images are corresponds). We will go through the process of downloading the dataset from the official MNIST link, creating the dataset class, loading and visualizing the data. For each patient, I have a histopathology Whole Slide Images (WSI). . I found a few datasets like Leed Sports Database. Jul 3, 2022 · I managed to create train code for my own dataset, using the pretrained COCO model, overcome the memory issues with CUDA (using 2 environments, one 2GB and another with 10GB) with image and batch sizes. 000 images each class) I train a pretrained model using the tools from references/detection At the heart of PyTorch data loading utility is the torch. This Dec 12, 2023 · I want to fine-tune the I3D model for action recognition from torch hub, which is pre-trained on Kinetics 400 classes, on a custom dataset, where I have 4 possible output classes. What is your dataset directory Dataset stores the samples and their corresponding labels, and DataLoader wraps an iterable around the Dataset to enable easy access to the samples. data. Our dataloader would process the data, and return 25 batches of 4 images each. Hence, we override the below methods in the dataset class: Jan 21, 2022 · You can make a PyTorch dataset for any collection of images that you want, e. So far I’ve managed to use ImageFolder to use my own Dataset but it lacks the labels of all images. The DCGAN paper uses a batch size of 128. Here, we use a custom dataset containing 43956 images belonging to 11 classes for training(and validation). In addition to this, PyTorch also provides a simple API that can be used to directly download and load images from some commonly used datasets in Welcome to the PyTorch Dataloaders and Transforms tutorial. Oct 9, 2019 · Now I want to show you how to re-train Yolo with a custom dataset made of your own images. Dataset. But, I am getting some errors. img1. Grayscale(num_output_channels=1) or. The labels are provided in a . prepare_tensor(inputs) Run the SSD network to perform object detection. root_dir = root. tv_tensors. COCO128 is an example small tutorial dataset composed of the first 128 images in COCO train2017. My question is Can we create our own dataset in pytorch (without using ImageFolder) with the images in the format . Apr 8, 2018 · I’d like to build my custom dataset. Jun 15, 2018 · I am trying to load my own dataset and I use a custom Dataloader that reads in images and labels and converts them to PyTorch Tensors. I'm having images in the format . DataLoader(yesno_data, batch_size=1, shuffle=True) 4. A generic data loader where the images are arranged in this way by default: This class inherits from DatasetFolder so the same methods can be overridden to customize the dataset. Oct 10, 2022 · HuggingFace streaming (iterable) dataset support (--dataset hfids:org/dataset) Webdataset wrapper tweaks for improved split info fetching, can auto fetch splits from supported HF hub webdataset; Tested HF datasets and webdataset wrapper streaming from HF hub with recent timm ImageNet uploads to https://huggingface. png format). ) very easy to construct. We will read the csv in __init__ but leave the reading of images to __getitem__. Nov 22, 2023 · A custom dataset would work: from PIL import Image. jpg is a black cat image, whereas both img2. Jun 30, 2021 · all_imgs = os. grayscale(image) Jun 8, 2021 · To do so, I need to make custom datasets (in this case CIFAR10) and give the number of images in each class. self. Dataset and implement functions specific to the particular data. Community. ConcatDataset after loading the lists, for example (where trans is a set of pre-defined Pytorch transformations): Jun 8, 2023 · Transforms are algorithms used to alter certain aspects of the images such as color, size, shape, brightness, etc. Summary. datasets module, as well as utility classes for building your own datasets. ToTensor, transforms. 如下，筆者以狗狗資料集為例，下載地址。 主要常以資料位址、子資料集的標籤和轉換條件…. To split the dataset, you could use torch. It is designed to train on custom dataset. The code seems to work well but the problem is that when I set all of the elements of the vector to 5000, which Sep 2, 2022 · I am importing MNIST dataset as train_data_MNIST = torchvision. I have attached my code below. I’m trying to process some MR images in DICOM format to classify them into two classes. Particularly, you learned: How to work with pre-loaded image datasets in PyTorch. I am trying to make a customised dataset and also split the data randomly to train and test. One issue that I’m facing is that I would like to skip images when training my model if/when labels don’t contain certain objects. However when the Dataloader is instantiated it returns strings x "image" and y "labels" but not the real values or tensors when read ( iter ) Mar 2, 2022 · As I've noticed that all the 2160 images in the dataset are read without any hiccups(i print the index number for every iteration) but the loop would not stop and reads the 2161st image which results in an Index out of range exception that gets handled by reading a random image. This will be necessary when we begin training our model! dataroot - the path to the root of the dataset folder. Now continue with 2. Path) – Root directory path. txt file. In this tutorial, you learned how to work with image datasets and transforms in PyTorch. I have some images stored in properly labeled folders (e. Sep 22, 2021 · Figure 2. train. Your custom dataset should inherit Dataset and override the following methods: Apr 8, 2023 · You can use this custom image dataset class to any of your datasets stored in your directory and apply the transforms for your requirements. pth extension. append(datasets. yaml. Dataset class that you inherit from then calls __getitem__ with the index given by enumerate. Correct labels are colored green, and incorrectly predicted labels are colored red. I preprocess each image, obtaining a variable number of 224 x 224 patches and keep them in a folder with the patient ID. Compose([transforms. My custom dataset class is given below: class CustomDataSet(Dataset): def __init__(self, main_dir, transform): self. Inside Test there are 3000 images. import torch. sampler. random_split, torch. But this folder structure is only correct if you are using all the images for train set: But this folder structure is only correct if you are using all the images for train set: I have some images stored in properly labeled folders (e. 2. As already discussed, the init method deals with accessing the data files, and getitem is where the data is read at particular indexes, preprocessed, and returned in the form of PyTorch tensors: tensors are the core data structure PyTorch works with Jun 20, 2019 · I have 3 separate image folders for train, test and validation set. autograd import Datasets¶. sel_dirs = dirs. People mostly use csv files to create dataset. Subset passing the indices, or torch. The inputs would be the noisy images with artifacts, while the outputs would be the clean images. import glob. The Dataset Class (source: Image by Author) We create our Train_Dataset class by inheriting the Dataset class: from torch. torch::Tensor read_data(const std::string& loc) {. fit the Lightning Data Module automatically handles Nov 11, 2020 · My images are of size 600x800. So, I am trying to create a custom dataset with taking help from this post. NUM_CLASSES = 2 # background=0 included, Suzanne = 1. Transforms can be used to transform or augment data for training or inference of different tasks (image classification, detection, segmentation, video classification). data as data_utils # get the numpy data Dec 10, 2018 · If you want to detect and track your own objects on a custom image dataset, you can read my next story about Training Yolo for Object Detection on a Custom Dataset. It can train on multi-class dataset. Iterate over the data. Define a function to visualize images and their labels. I want to feed these to pytorch neural network. 1 Create dataset. , \\0 and \\1), and in those cases I can use torch. PyTorch Foundation. Pytorch provides pre-trained deeplabv3 on Pascal dataset, I would like to train the same architecture on Dec 27, 2019 · Hello, I hope everyone in the community is well. natsorted(all_imgs) //Error-1 Jun 9, 2020 · I am loading data from multiple datasets. medical data, random images you pulled off the Internet, or photos you took. Select a Model. The label for each patch in a given patient folder is the label assigned to the WSI. 5], [0. They just have images in zip file as data and visualized Apr 22, 2022 · The problem with my current code is that batch_size is only affecting the number of images (as in number of image paths) I am loading since those directly connected to __len__() and __getitem__(). May 15, 2019 · The PyTorch data loading tutorial covers image datasets and loaders in more detail and complements datasets with the torchvision package (that is often installed alongside PyTorch) for computer vision purposes, making image manipulation pipelines (like whitening, normalization, random shifting, etc. Dec 10, 2020 · I am doing image classification with PyTorch. ToTensor()) train_loader = DataLoader(train_data, batch_size=16, shuffle=True) However as shown below: for img, label in train_loader Log the original and reconstructed images using TensorBoard. Dataset is an abstract class representing a dataset. ImageOps. Format the images to comply with the network input and convert them to tensor. In 2020, Glenn Jocher, the founder and CEO of Ultralytics, released its open-source implementation of YOLOv5 on GitHub. He covers topics related to artificial intelligence in our life, Python programming, machine learning, computer 1. These options are configured by the Learn about PyTorch’s features and capabilities. jpg are tabby cat images); I would like to efficiently load the image and label using Jun 20, 2022 · Training YOLOv5 Object Detector on a Custom Dataset. If yes, can somebody show me the code for that. For the Train_Dataset class, We first inherit PyTorch's Dataset class. from pathlib import Path. at the moment I am getting an error Jul 29, 2018 · PyTorch ImageFolder assumes that images are organized in the following way. , \0 and \1), and in those cases I can use torch. Ratan: Sep 8, 2022 · 2. Respective tutorials can be easily found on Pytorch official website (Dataset and Dataloader) The DataLoader combines the dataset and a sampler, returning an iterable over the dataset. I would like to try it on my own images (800 total images 160 of which are val images). transform = transform all_imgs = os. ImageFolder takes the root folder as an argument and will use all images from all subfolders as data samples. In [ ]: def display_image_grid(images_filepaths, predicted_labels=(), cols=5): rows = len Dec 14, 2022 · The first point to note is that any custom dataset class should inherit from PyTorch's primitive Dataset class, that is torch. nn import functional as F from torchvision import datasets, transforms from torch. Thus, if you pass trainer. YOLOv5 offers a family of object detection architectures pre-trained on the MS COCO dataset. jpg,…so on and contains Labels like cat,dog. I’m trying to get my custom data set to return the following: Image tensor Policy (unique ID) numerical columns tensor categorical columns tensor categorical embedding sizes tuple I have 1 through 4 coming back correctly. data_utils. Built-in datasets¶. transforms. torch::Tensor tensor = Jul 20, 2019 · Hello fellow Pytorchers, I am trying to add normalization to the custom Dataset class Pytorch provides inside this tutorial. training from scratch, finetuning the convnet and convnet as a feature extractor, with the help of pretrained pytorch models. Jan 5, 2024 · Since the COCO dataset originally has 91 object classes, we need to change the final layers of the model to match the number of classes in our custom dataset. dataset1 = ds_main. Chris Fotache is an AI researcher with CYNET. batch_size - the batch size used in training. Developer Resources Sep 15, 2019 · I have made a dataset using pytoch dataloader and Imagefolder, my dataset class has two Imagefolder dataset. Jul 27, 2022 · The simple if stage == "fit" case helps you to define the needed stage to create the correct type of dataset. data import DataLoader. // You can for example just read your data and directly store it as tensor. datasets. For example, If one image doesn’t contain any target labels belonging to the class ‘Cars’, I would like to skip them. My images. Dataset Feb 10, 2022 · torch. With the dataset (20. So it makes sense to apply augmentations dynamically, on-the-fly. arXiv preprint arXiv:1706. The dataset should inherit from the standard torch. Torchvision supports common computer vision transformations in the torchvision. The torch. In your main method you call. Defining __len__ function. To use the ImageFolder class, you must first create the folder structure appropriately. I found solutions that load images with ImageFolder and after convert images in grayscale, using: transforms. png or . with torch. load new file and repeat until all files were used. shubz_308 November 8, 2021, 10:01pm 1. MNIST(root=path+"MNIST", train=True,transform=transforms, download=True)and I am trying to make a smaller dataset from MNIST, let's say the first 10,000 images and corresponding labels. listdir(main_dir) self. Migrate existing code to work in PyTorch 0. Aug 26, 2019 · 5. Dataset i. def __getFileNr(self, path): Dec 8, 2020 · Hello, I have some images in a folder. create batches of data from this file until it’s empty or the remaining number of samples is smaller than the batch size. Sep 14, 2020 · Rethinking atrous convolution for semantic image segmentation. jpg, 2345. co/timm Sep 9, 2019 · Hi, I’m trying to start my first pytorch project from a Kaggle Dataset, the goal is to simply classify some images. data import Dataset. def __len__(self): May 5, 2021 · I've currently tried to use "ImageFolder" from torchvisions datasets to load the images as follows: TRAIN_PATH = '/path/to/dataset/DATASET'. INPUT_IMAGE_HEIGHT, config. Upon completion of this tutorial, you Jan 28, 2021 · For example if we have a dataset of 100 images, and we decide to batch the data with a size of 4. ImageFolder(file_path, trans)) l. 等，作為繼承Dataset類別的自定義資料集的初始條件，再分別定義訓練與驗證的轉換條件傳入訓練集與驗證集。 Nov 22, 2022 · When it comes to creating the dataset, you have two options: Use PyTorch’s ImageFolder class. I have been trying with different image datasets, but all the sets return the same format, so my question is: How to load images and then displaying them in their real format using pytorch's dataloader? Jan 17, 2019 · It would look like this: transform = transforms. As you can see inside ToTensor() method it returns: return {‘image’: torch. Both train and validation set have multiple labels of varying number. DataLoader() that can take labels,features,adjacency matrices, laplacian graphs. This is memory efficient because all the images are not stored in the memory at once but read as required. ImageFolder(os. 5])]) Your code should have failed, because applying Normalize() on images does not work, but it hasn’t, since you never actually called the self. It automatically creates lables. Jul 6, 2020 · In this tutorial, you’ll learn how to fine-tune a pre-trained YOLO v5 model for detecting and classifying clothing items from images. 1. utils. main_dir = main_dir self. The challenge involved detecting 9 different objects inside a tunnel network — and they are very specific objects, not the regular one included in Feb 24, 2022 · Hi, I’m trying to define a custom Dataset for my medical images. ImageFolder(file_path2, trans)) image Nov 5, 2019 · The labels of image are [0, 1, 1] (e. Update results with reconstructed image and original image. def get_faster_rcnn_model(num_classes): """return model and preprocessing transform""". I need to create my own dataset in pytorch. Community Stories. In this tutorial, you will learn how to prepare your image dataset for image classification tasks Oct 7, 2018 · PyTorch 資料集類別框架. dataset2 = ds_energy. g. PyTorch domain libraries provide a number of pre-loaded dataset s (such as FashionMNIST) that subclass torch. Save each resnet output at the same location as the image file with . Dataset class, and implement __len__ and __getitem__. workers - the number of worker threads for loading the data with the DataLoader. transforms and torchvision. Our data is now iterable using the data_loader. lj nx sm ud ia ey gj vj wl ir