Hmdb51 classes. frames_per_clip – Number of frames in a clip.

Hmdb51 classes Class-level Performance Evaluation for the HMDB51 Dataset: The histogram illustrates the accuracy of the proposed technique for each class in the HMDB51 dataset. New intent discovery aims to uncover novel intent categories from user utterances to expand the set of supported intent classes. Outputs will not be saved. This first-of-its-kind video dataset and evaluation protocol can greatly facilitate visual You signed in with another tab or window. Classes From Videos in The Wild Khurram Soomro, Amir Roshan Zamir and Mubarak Shah CRCV-TR-12-01 November 2012 (HMDB51 [5] and UCF50 [9] are the currently the largest ones with 6766 clips of 51 actions and 6681 clips of 50 actions re-spectively. Dataset i. In this paper, we propose to squeeze the time axis of a video sequence into the channel dimension and present a lightweight video recognition network, term as SqueezeTime, for mobile video understanding. . The selected frame is also used as the initial frame to obtain the stacked optical flows of 10 I encountered the same problem when I was using IPython notebook-like tools. The work that I have done is follwing: I added a method def slowfast_8x8_resnet50_hmdb51(nclass=51, pretrai UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. Overview. For more You signed in with another tab or window. - sghong977/Surgical-Bleeding-AMAGI Download scientific diagram | Sample action classes from HMDB51: Top row (full body motion): Somersault, Fencing, Push-ups; Bottom row (motion from specific body parts or face): Clap, Chew, Eat. change main. See the clip sampling documentation for more information. category represents the target class, and annotation is a list of points from a hand-generated Kitchens Domain Adaptation dataset [48], where the dataset is partitioned into four classes for training as ID and four classes for testing as OOD, with a total of 4,871 video clips. It is a critical task for the development and service expansion of I tried to train slowfast network in hmdb51 dataset, I can run the program successfully but the accuracy is pretty low about 0. /(dirs of class names) . The classes are grouped into five main types: general facial actions; facial actions with object manipulation; general body movements; body movements with object interaction; and body movements for human interaction [16] . 88 and an F1-score of 0. The actions are human focussed and cover a broad range of classes The majority of existing action recognition datasets suffer from two disadvantages: 1) The number of their classes is typically very low compared to the richness of performed actions by humans in reality, e. Start by defining a PyTorch model class and modify the Res3D_18 architecture to include 51 classes of HMDB51 dataset. First please check if there is any hidden files under your dataset_path. Join the PyTorch developer community to contribute, learn, and get your questions answered You signed in with another tab or window. HMDB51 dataset. md $ cd mm. Similarly, HMDB51 25/26 and UCF101 50/51 are constructed based on HMDB51 [39] and UCF101 [57], with a total of 6,766 and 13,320 video clips respectively. Tools. Two evaluation strategies were used: [1] "One frame per video" method For each batch element, a random video was selected, and for each video/element a single frame is selected and given as input to the spatial stream. PyTorch implemented C3D, R3D, R2Plus1D models for video activity recognition. batch_iter(self. PyTorch offers 3 action recognition datasets — Kinetics400 (with 400 action classes), HMDB51 (with 51 action classes) and UCF101 (with 101 action classes). from_dataset (str): Dataset name of source action class. Could you tell your split policy of train/val/test datasets for HMDB51 and UCF101. You can use get_classes() and get_segmentation_classes() to see the available classes and segmentation classes, respectively HMDB51¶ class torchvision. pth is working on --n_finetune_classes 101 only but not on 51 is it true? Could you please check and let me know because i 3. Similar to UCF101 dataset, HMDB51 dataset also provides three training and testing splits. , ability to generalize to a novel set of unseen classes on a handful of tasks, To analyze traffic and optimize your experience, we serve cookies on this site. Click here to check the published results on UCF101 (updated October 17, 2013) UCF101 is an action recognition data set of realistic action videos, We fused the two streams by averaging the softmax scores. It consists of 101 action classes, over 13k clips and 27 hours of | Find, read and cite all the research you need on ResearchGate HMDB51 [5] 51 6766 Dynamic Yes 2011 Movies, Y ouTube, W eb Tools. The following commands illustrate how to extract the videos. 80 on the former and an accuracy of 0. gories we focus on the class of algorithms for action recog- It consists of 101 action classes, over 13k clips and 27 hours of video data. Kinetics400. It is composed of 13,320 videos distributed in 101 action classes and it is presented as an extension of UCF50 . Learn about the PyTorch foundation. This data set is an extension of UCF50 data set which has Through experiments on UCF101, HMDB51, and Kinetics-600 datasets, we showcase the effectiveness and applicability of our proposed approach in addressing the challenges of ZS-VAR. It was developed by the researchers: H. HMDB51 is an About. The database consists of realistic user uploaded videos containing camera motion and cluttered background. The HMDB51 dataset [16] is a large collection of uncontrolled videos from various sources, including movies and YouTube videos. On the other hand, the HMDB51 contains 6766 video clips extracted from various sources organized in 51 action classes. from publication: Optimized deep learning-based cricket activity The HMDB51 dataset contains 6766 video clips distributed into 51 classes. Root directory of the HMDB51 Dataset. The horizontal axis represents the different classes, while the vertical axis denotes the corresponding accuracy percentage. r2plus1d = r2plus1dVideoClassifier(baseNetwork,string(classes), "InputSize",inputSize); To further address this dataset challenge, we have constructed a new dataset, termed PA-HMDB51, with both target task labels (action) and selected privacy attributes (skin color, face, gender, nudity, and relationship) annotated on a per-frame basis. Kinetics (root, frames_per_clip[, ]) Generic Kinetics dataset. Is this pre_trained model on hmdb51 dataset? could you please check it, because resnext-101-kinetics-hmdb51_split1. datasets) action classes with over 13,000 video samples for a total of 27 hours. 107407 (using precomputed HOG/HOF "STIP" features from site, averaging for 3 splits) The HMDB51 dataset is a large collection of realistic videos from various sources, including movies and web videos. video_sampler (Type[torch. sh. In the context of the whole project (for HMDB51 only), the folder structure will look like: Datasets, Transforms and Models specific to Computer Vision - pytorch/vision HMDB51¶ class torchvision. The following snippet should be sufficient to reproduce clear symptoms of the bug. txt. datasets) Imagenette (class in torchvision. Although two datasets consist of similar classes of human actions, they show different characteristics in terms of average length of the action sequence. 3 % for * For a directory, the directory structure defines the classes (i. This dataset consider every video as a collection of video clips of fixed size, specified by ``frames_per_clip``, where the step in frames between To address this issue we collected the largest action video database to-date with 51 action categories, which in total contain around 7,000 manually annotated clips extracted from a variety of This database, that we call HMDB51, comprises 51 distinct human action categories, with overall 6,474 clips from 1,697 unique source videos. In this work, we propose a global video descriptor for classification of realistic videos as the ones in Figure 1. We used single frame classification, which trains fast, to get a baseline result. The dataset contains 51 HMDB51 (class in torchvision. txt trained_models/ HMDB51/ . Join the PyTorch developer community to contribute, learn, and get your questions answered [ICCV 2019 (Oral)] Temporal Attentive Alignment for Large-Scale Video Domain Adaptation (PyTorch) - cmhungsteve/TA3N Torchvision provides many built-in datasets in the torchvision. - pytorch-video-recognition/train. , 2011) dataset for a few of 51 classes. clip_sampler (ClipSampler): Defines how clips should be sampled from each video. Reload to refresh your session. UCF101 represents average 183 frames per each action, while HMDB51 shows average 93 frames per each action. γ (weight parameter γ) and (b) mAP vs. To extract only frames from videos You signed in with another tab or window. datasets) ImageNet (class in torchvision. The result has been obtained in 10 Classes which are brush_hair, climb_stairs, cartwheel, catch, chew, clap, climb, dive, draw_sword and driddle. Moreover the classes of actions can be grouped into: general facial Download scientific diagram | Top-25 most confused classes for HMDB-51. root (string) – Root directory of dataset where directory caltech101 exists or will be saved to if download is set to True. Moreover the classes of actions can be grouped into: body movements for human interaction such as kissing. Kinetics is a popular action recognition dataset and used With nearly one billion online videos viewed everyday, an emerging new frontier in computer vision research is recognition and search in video. Join the PyTorch developer community to contribute, learn, and get your questions answered Tools. We train the model on 51 classes in the initial task, and the remaining 50 classes are divided into groups of 10, 5, and 2 classes for each incremental task. The dataset contains 400 human action classes, with at least 400 video clips for each action. The HMDB51 has 51 classes, and 100 videos are selected for training(70/100 videos) and testing(30/100 videos) of each class, but how to split the training dataset into train and val is not mentioned. T (iteration parameter T). e. 101 logical motion perception and recognition [22]. This dataset consider every video as a collection of video clips of fixed size, specified by ``frames_per_clip``, where the step in frames between HMDB51 is an action recognition video dataset. Join the PyTorch developer community to contribute, learn, and get your questions answered HMDB-AD is the simpler dataset among the two introduced in this paper. Use ls -a if you are under a Linux environment. g. Each observation corresponds to one video, for a total of 6849 clips. In the computer vision, literature action and activity are often used interchangeably, but there might be some disputes on that. __getitem__(i) class_counts[label] += 1 When using the HTTPS protocol, the command line will prompt for account and password verification as follows. The videos are firstly split into training and test videos, and then 13 frames per video are extracted from each set respectively so that the frames in the test set are Dataset repository of "MetaVD: A Meta Video Dataset for enhancing human action recognition datasets. The database consists of realistic user-uploaded videos containing camera motion and cluttered HMDB51, Breakfast, and Something-Something V1 and V2, where we compare favorably to existing heuristic frame sampling methods. After the whole data process for HMDB51 preparation, you will get the rawframes (RGB + Flow), videos and annotation files for HMDB51. ext_feat_hmdb51. Figure 1. v2. We considered 20 object classes which include the 16 classes categorizing the 49 components, the two tools (screwdriver and wrench), the instructions booklet and a partial_model class. Download scientific diagram | Examples from HMDB51 (Kuehne et al. 3 % (57. Each video has associated one of 51 possible classes, each of which identifies a specific human behavior. I think that file causes confusion to PyTorch dataset. Join the PyTorch developer community to contribute, learn, and get your questions answered hi mmaction2, First of all, thank you for your contribution. Accuracy is The Action Recognition Models seems doesn't contain a pretrained model for HMDB51, will you add this model in the future ? Or do you now where i can get the pretrained model for HMDB51, i want to run demo with its action classes ~ BTW, i Benefit from the development of unsupervised neural language model [2,7,51], most learned semantic space based methods construct semantic space through the embedding of class labels [12,46,50,56 Compared to image classification, action recognition using videos is challenging to model because of the inaccurate ground truth data for video data sets, the variety of gestures that actors in a video can perform, the heavily class imbalanced datasets, and the large amount of data required to train a robust classifier from scratch Example action classes from the (a) KTH, (b) UCF50 and (c) HMDB51 datasets. 2 Experiment on 10 Classes of HMDB51. This is an official pytorch implementation of our paper "No Time to Waste: Squeeze Time into Channel for Mobile Video Understanding". py at main · MCG-NJU/VideoMAE {"payload":{"allShortcutsEnabled":false,"fileTree":{"master/_modules/torchvision/datasets":{"items":[{"name":"celeba. Join the PyTorch developer community to contribute, learn, and get your questions answered classes (None): a string or list of strings specifying required classes to load. Additionally, we provide baseline action recognition results on this new dataset using standard bag of words approach with overall performance of 44. functional) I. This code includes training, fine-tuning and testing on Kinetics, ActivityNet, UCF-101, and HMDB-51. 32% for HMDB51 (26 training and 25 unseen test classes) and of 46. download Download free PDF View PDF HMDB51 torchvision utility is mislabeling the gross majority of videos in the dataset. Sampler]): Sampler for the internal video container. annotation_path – Path to the folder containing the split files. py at master · jfzhang95/pytorch-video-recognition It consists of 101 action classes, over 13k clips and 27 hours of video data. Dungeons and Dragons (D&D) Fifth Edition (5e) Classes. These are mapping files that go between class IDs to class names. Contributions. Each clip lasts around 10s and is taken from a different YouTube video. These are generated from the training CSV files from each dataset by collecting the unique classes, sorting them, and then numbering them from 0 upwards. The above architecture only adds a simple Dense layer with 51 output nodes for Each row represents an individual relation from an action class, called as source action class to another action class, called as target action class. Learn about the tools and frameworks in the PyTorch Ecosystem. We use variants to distinguish between results evaluated on slightly different versions of the same dataset. Normal activities within this dataset are running and walking, aligning with their respective HMDB51 classes. Computing descriptors for videos is a crucial task in computer vision. train_split_file, self. In the feature mode, this code outputs features of 512 dims (after global average pooling) for each 16 frames. Serre in the year 2011. fold (int, optional) – Which fold to use. Download scientific diagram | Samples for the 51 action classes from the HMDB51 dataset [34] from publication: Tell me what you see: A zero-shot action recognition method based on natural language You signed in with another tab or window. In the training and testing process, total 1150 video have been used. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object UCF101 - Action Recognition Data Set There will be a workshop in ICCV'13 with UCF101 as its main competition benchmark: The First International Workshop on Action Recognition with Large Number of Classes. Built-in datasets ¶ All datasets are subclasses of torch. pth files) Datasets. You switched accounts on another tab or window. This database, that we call HMDB51, comprises 51 distinct human action categories, with overall 6,474 clips from 1,697 unique source videos. pth: --model resnext Download scientific diagram | Heatmap for per-class accuracy for each method for the HMDB51 dataset. Image (class in torchvision. rar rars/ for a in $(ls rars); do unrar x "rars/${a}" videos/; done; Parameters. Some of the key challenges are large variations in camera vie wpoint and motion, the cluttered background, and changes in the position, scale, and appearances of the actors. 3(c) can easily be * For a directory, the directory structure defines the classes (i. Here we present some benchmark results of previous action recognition models: (Across three splits) HMDB51¶ class torchvision. UCF101 dataset contains 13. UCF101 is an action recognition data set of realistic action videos, collected from YouTube, having 101 action categories. The HMDB51 dataset includes videos of 51 action classes with more than 101 This code includes training, fine-tuning and testing on Kinetics, Moments in Time, ActivityNet, UCF-101, and HMDB-51. Download scientific diagram | HMDB51 dataset. 5%. If you want to finetune the models on your dataset, you should specify the following options. For security reasons, Gitee recommends configure and use personal access tokens instead of login passwords for cloning, pushing, and other operations. '/HMDB_annotations/', 1, step_between_clips=10000) class_counts = defaultdict(int) for i in range(len(hmdb)): vid, aud, label = hmdb. 21 seconds at 25 FPS at a resolution of 320 × 240 pixels. Abnormal activities are climbing and performing a cartwheel. This code uses videos as inputs and outputs class names and predicted class scores for each 16 frames in the score mode. We further normalized all video frame rates (by HMDB51 ¶ class torchvision. Any of ucf101, hmdb51, activitynet, stair_actions, charades, kinetics700. You can disable this in Notebook settings HMDB-51 is an human motion recognition dataset with 51 activity classifications, which altogether contain around 7,000 physically clarified cuts separated from an assortment of sources going from digitized motion pictures to YouTube. The dataset is composed of 6,766 video clips from 51 action categories (such as “jump”, “kiss” and “laugh”), with HMDB51 ¶ class torchvision. The UCF101 is an action recognition dataset collected on the wild from YouTube videos. For example, the actions classes ApplyEyeMakeup and Typing from UCF101 can be recognized by analyzing the first video frame only. HMDB51 (root, annotation_path, frames_per_clip, step_between_clips=1, frame_rate=None, fold=1, train=True, transform=None, _precomputed_metadata=None, num_workers=1, _video_width=0, _video_height=0, _video_min_dimension=0, _audio_samples=0) [source] ¶. r3d18_K_200ep. tv_tensors) ImageFolder (class in torchvision. See a full comparison of 76 papers with code. With nearly one billion online videos viewed everyday, an emerging new frontier in computer vision research is recognition and search in video. (a) mAP vs. While much effort has been devoted to the collection and annotation of large scalable static image datasets containing thousands of image categories, human action datasets lag far behind. dataset/ HMDB51/ . In the same figure, the first graph is HMDB51¶ class torchvision. py at line 38 (change class index Create a R(2+1)D Video Classifier by specifying the classes for the HMDB51 dataset and the network input size. The benchmarks section lists all benchmarks using a given dataset or any of its variants. Community. To enhance In this paper, we propose a global descriptor for videos, to be used for the action classification of challenging datasets such as UCF50 and HMDB51 with a large number of action classes, some of Tools. It consists of 995 video clips, divided into 680 training videos and 315 testing videos. Learn about PyTorch’s features and capabilities. HMDB51 is an HMDB51¶ class torchvision. utils. frames_per_clip – Number of frames in a clip. " - STAIR-Lab-CIT/metavd Compared to image classification, action recognition using videos is challenging to model because of the inaccurate ground truth data for video data sets, the variety of gestures that actors in a video can perform, the heavily class imbalanced datasets, and the large amount of data required to train a robust classifier from scratch Our best results are achieved with the maximum embeddings fusion approach, with average accuracy of 36. You signed out in another tab or window. Kuehne, H. While much effort has been devoted to the collection and annotation of large scalable static image datasets containing thousands of image categories, human action See more The **HMDB51** dataset is a large collection of realistic videos from various sources, including movies and web videos. hmdb51_ClassInd. Experimental results on the UCF50, UCF101, and HMDB51 action datasets demonstrate that TS is comparable to state-of-the-arts, and outperforms many other The selected dataset is named 'HMDB - Human Emotion DB'. HMDB51 [2] is another popular action recognition dataset. 2) The videos are recorded in unrealistically controlled About. Sample frames from the proposed HMDB51 [1] (from top left to lo wer right, actions are: hand-w aving, drinking, sw ord ghting, diving, runni ng and kicking). 52% for UCF101 (51 training HMDB51: the HMDB51 video archive has two-level of packaging. frames) HMDB51¶ class torchvision. rgb_path, self. hmdb51_input_filename. On the HMDB the mean classification rate for the ten categories is at 54. i want to train hmdb51 dataset using TSN, following tutorials, i do something as: data processing: following preparing_hmdb51. The row is composed of the following information. Video segments are between 2 to 5 seconds at train_steps, train_batches = self. from publication: Improved Motion Description for Action Classification | Even though the Official implementation of "Amplifying action-context greater: Image segmentation-guided intraoperative active bleeding detection" (MICCAI workshop 2022). Gluon CV Toolkit. Download scientific diagram | Example frames from (a) HMDB51 (b) Hollywood2 (c) UCF101 and (d) UCF-sports datasets from different action and activity classes. HMDB51 contains 51 action classes with around 7,000 samples, mostly extracted from movies. from publication: Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. datasets. Classes are listed along the horizontal axis, with methods along the vertical. VLMs have exhibited impressive zero-shot capabilities, i. The width of the clips was scaled accordingly so as to maintain the original aspect ratio. I am working on action recognition on HMDB51. HMDB51 [1] 2011 51 min. We provide the details regarding classes, the number of videos, and the frame rate. Additionally, we provide baseline action recognition results on this new dataset using standard bag of Benchmark. Contribute to dmlc/gluon-cv development by creating an account on GitHub. For this dataset was implemented benchmark with accuracy: 0. Explore the ecosystem of tools and libraries Khurram Soomro, Amir Roshan Zamir and Mubarak Shah, UCF101: A Dataset of 101 Human Action Classes From Videos in The Wild, CRCV-TR-12-01, November, 2012. Video segments average 7. UCF101. target_type (string or list, optional) – Type of target to use, category or annotation. flow_path, self. Join the PyTorch developer community to contribute, learn, and get your questions answered. Current action recognition databases HMDB51¶ class torchvision. It consists of 101 action classes, over 13k clips and 27 hours of video data. e, they have __getitem__ and __len__ methods implemented. /(. mainly dif fer in motion rather than static poses and can thus. 5. Tools & Libraries. By clicking or navigating, you agree to allow our usage of cookies. each subdirectory is a class). 83 and an F1 We show that the constructed dictionaries are distinct for a large number of action classes resulting in a significant improvement in classification accuracy on the HMDB51 dataset. HMDB51 dataset contains 6. Likewise, shake_hands from HMDB51 in Fig. HMDB51 ¶ class torchvision. If provided, only samples containing at least one instance of a specified class will be loaded. Each split contains a training set of [NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training - VideoMAE/run_class_finetuning. This part is for declaring some constants and directories: # Specify the height and width to which each video frame will be resized in our dataset. The dataset is consisted of 6,766 clips from 51 action categories Proceedings of the ICCV Workshop on Action, Gesture, and Emotion Recognition, 2017. An evaluation on trimmed action datasets UCF101 and HMDB51 demonstrates the efficacy of action-vectors for action classification over state-of-the-art techniques. from publication The current state-of-the-art on HMDB-51 is VideoMAE V2-g. HMDB51¶ class torchvision. /(dirs of video names) HMDB51_labels/ results/ test. SomethingSomethingv1. datasets module, as well as utility classes for building your own datasets. data. Jhuang, E. Can also be a list to output a tuple with all specified target types. Here is my code below. HMDB51 - A Large Video Database for Human Motion Recognition 575 thus resized all extracted clips to a height of 240 pixels (using bicubic interpolation over a 4 4 neighborhood). The case happen to me is I found a hidden file called . We also collected meta-data towards a precise evaluation of the limitation of current computer vision systems. The proposed HMDB51 contains 51 dis-tinct action categories, each containing at least 101 clips for a total of 6,766 video clips extracted from a wide range of HMDB51¶ class torchvision. HMDB51 (root, annotation_path, frames_per_clip, step_between_clips=1, frame_rate=None, fold=1, train=True, transform=None, HMDB51 is an action recognition video dataset. About. html","path":"master/_modules/torchvision About. step_between_clips – Number of frames between each clip. HMDB51 (root, annotation_path, frames_per_clip) HMDB51 dataset. txt <a video as in video_dir_path/class_x> <0 or 1 or 2> where 0,1,2 corresponds to unused, train split respectively. Download scientific diagram | Parameter analysis on the CC_WEB_VIDEO, HMDB51, and UCF101 datasets. This is achieved by training a network to minimize the loss between its features and the Flow stream, along with the cross entropy loss for recognition. Garrote and T. Models (Beta) Discover, publish, and reuse pre-trained models. You signed in with another tab or window. A comprehensive list of all official character classes for Fifth Edition. The HMDB51 dataset [18] contains 6,849 videos for 51 different classes collected from movies and Youtube. KTH [], Weizmann [], UCF Sports [], IXMAS [] datasets includes only 6, 9, 9, 11 classes respectively. ) The dataset is composed of web videos which are For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0. The distribution of clips among the 11 classes is as follows: Comparisons with HMDB51(-dark) We compare our ARID dataset statistically with HMDB51/HMDB51-dark, with the results and sampled frame as shown: Benchmark Results. The training progress has been shown in Fig. Should be between 1 and 3. transforms. The datsets and splits can be downloaded from. datasets) horizontal_flip() (in module torchvision. -model resnext --model_depth 101 --resnet_shortcut B --resnext_cardinality 32 --sample_duration 64 resnext-101-kinetics-hmdb51_split1. pth: --model We introduce UCF101 which is currently the largest dataset of human actions. ipynb_checkpoints which is located parallelly to image class subfolders. 2. We also collected meta Each video has associated one of 51 possible classes, each of which identifies a specific human behavior. This dataset consider every video as a collection of video clips of fixed size, specified by frames_per_clip, where the step in frames between In this context, we describe an effort to advance the field with the design of a large video database contain-ing 51 distinct action categories, dubbed the Human Mo-tion DataBase (HMDB51), HMDB51 is an action recognition video dataset. from publication: Discriminatively This notebook is open with private outputs. mkdir rars && mkdir videos unrar x hmdb51-org. HMDB51 is an action dataset whose action cate gories. ) The dataset is composed of web videos which are And each text file in the splits directory class_x_test_split_<1 or 2 or 3>. PyTorch Foundation. Torchvision provides many built-in datasets in the torchvision. 8K videos from 51 classes. MARS is a strategy to learn a stream that takes only RGB frames as input but leverages both appearance and motion information from them. HMDB51. 3K videos from 101 classes. qnbvx ufmg avcczl mxpa lplik cwvra brbg hysmd tpsx bkvc