Yolov5. , ChatGPT) is banned. anchor_t=7. 1) is a powerful object detection algorithm developed by Ultralytics. Although YOLOv5 shows good performance in the field of object detection, it is not suitable for deployment on embedded devices due to its large model size and large number of parameters. Currently operates on CPU, future updates will implement GPU support. 由于超 Nov 12, 2023 · YOLOv5 (v6. yaml or hyp dictionary. Author. Feb 25, 2023 · I was wondering whether there is a way of getting information about how an existing custom yolov5 model was trained. 4%. Sep 12, 2021 · Now you got what is needed, a dataset annotated in the correct way and a YAML file describing the paths and names. Weighted Boxes fusion is a method to dynamically fuse the boxes either before training (which cleans up the data set) or after training (making the Jan 28, 2021 · 👋 Hello @Adriel-Cabral, thank you for your interest in 🚀 YOLOv5!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. 7k. Jul 13, 2023 · Train On Custom Data. Feb 26, 2024 · YOLOv5 is an object detection model that can better meet the accuracy and speed requirements of real-time detection tasks. Each cell in the grid is responsible for detecting objects within itself. 🔧 Version and easily access your custom training data with the integrated ClearML Data Versioning Tool. 网格搜索等传统方法很快就会变得难以处理，原因在于：1 TLDR. 1. The answer is, " Hyperparameters are defined as the parameters that are explicitly defined by the user to control the learning process. We first initialize 3 options for all of hyperparameters in train. from publication: Plastic Contaminant Detection in Aerial Imagery of Cotton Fields Using Deep Learning | Plastic shopping Nov 12, 2023 · Comet is now fully integrated with YOLOv5. py --batch 32 --cfg yolov5m6. To optimize and fine-tune the hyperparameters of the YOLOv5 network, the system employed a genetic programming algorithm (GA) based on evolution (EGA) [38]. load to load the model to run inferences. So, your evolve. Now we are ready to get started training. In this article, we will be fine tuning the YOLOv7 object detection model on a real-world pothole detection dataset. We have provided two methods for running training on a single GPU or multi-GPU system. This would make an interesting PR btw, others have also expressed interest in additional augmentations. Images are never presented twice in the same way. py: error: unrecognized arguments: --hyp. 2 to 0. 2%, which makes it advantageous over general target detection models and fit for use in the complex underwater environment. yaml --weights yolov5s. Nov 12, 2023 · Hyperparameter tuning is not just a one-time set-up but an iterative process aimed at optimizing the machine learning model's performance metrics, such as accuracy, precision, and recall. Mar 11, 2023 · I have searched the YOLOv5 issues and discussions and found no similar questions. py --resume. That's it! Comet will automatically log your hyperparameters, command line arguments, training, and validation metrics. 0 license # Hyperparameters for high-augmentation COCO training from scratch # python train. Contribute to gagan3012/yolov5 by creating an account on DagsHub. The optimal YOLOv5 model hyperparameters may be quickly found using Optuna's TPE method and a Mar 30, 2023 · @ztarsasw absolutely, I understand the desire to keep the YOLOv5 source code unmodified. 0 Oct 23, 2023 · Status. Yolov5 Evolution. I wanted to know for example if i set degrees to 15 on yolov5/data/hyp YOLOv5 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, instance segmentation and image classification tasks. We hope that the resources here will help you get the most out of YOLOv5. Despite the benefits of AI in MASS, its potential security threats must be considered. This article dives deep into the YOLOv5 architecture, data augmentation strategies, training methodologies, and loss computation techniques. In other words, in the project folder and not the cloned folder. yaml --weights '' --data coco. py --batch 64 --cfg yolov5n6. 0 license # Hyperparameters for low-augmentation COCO training from scratch # python train. If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. Fork 14. We hope that the resources in this notebook will help you get the most out of YOLOv5. This code ensure no duplicate hyperparameters if I understand correctly then why in my evolve. ML의 하이퍼파라미터는 학습의 다양한 측면을 제어하며, 이에 대한 최적의 Apr 1, 2021 · yolov5提供了一种超参数优化的方法–Hyperparameter Evolution，即超参数进化。. Architecture: no changes; Hyperparameters: minor change hyp-scratch-large. yaml) file that was used to train the model. In the context of object detection, hyperparameter tuning refers to the process of selecting the optimal values for the various parameters and settings that are used in the training of an object detection model. scratch文件呢？另外，标注红色方框的这两个yaml文件又有什么区别？ Additional Jul 12, 2022 · When compared to YOLOv5-s, the accuracy is raised by 3. yaml lrf reduced from 0. Question CPU times: user 2 µs, sys: 0 ns, total: 2 µs Wall time: 6. For more information on using --evolve and hyperparameter evolution in YOLOv5, you can check out this May 21, 2023 · 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. 1 Hyperparameters Our training process for the YOLOv5 model adhered to a set of meticulously chosen hyperparameters, as delineated in Table 6. YOLOv5에는 대략 25가지가 넘는 하이퍼 파라미터가 존재합니다. Oct 4, 2023 · YOLOv8, EfficientDet, Faster R-CNN or YOLOv5 for remote sensing. YOLOv5 is getting the hyperparameters from a hyp yaml file where sweeps executes the train's script by passing the hyperparameters as parameters, therefore I get the following error: train. csv will have 15000 rows. YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. Grid Search Cross-Validation is a powerful technique for fine-tuning the hyperparameters of machine learning models. Download scientific diagram | YOLOv5 hyperparameters used for this study. scratch这4个yaml文件有什么区别？各自是在什么样的情况下使用？程序应该在哪里选择使用哪个hyp. This hyperparameter optimization algorithm was Jan 14, 2021 · If you wanted to add additional hyperparameters you could add them in the hyp file and then access them here in the dataloader (i. Mar 17, 2023 · You can also manually plot the results by calling plot_evolution(yaml_file). yaml --img 640 --epochs 300 --linear # See tutorials for hyperparameter evolution https://github. Publish your model insights with interactive plots for performance metrics, predictions, and hyperparameters. The choice of a learning rate is critical in determining how quickly the network converges to the optimal solution and how well it generalizes to new data. The YAML file should be in yolov5/data folder. The Dec 15, 2021 · Resuming YOLOv5 🚀 evolution is a bit different than resuming a normal training run with python train. It’s time to introduce some more tricks to boost performance. # Ultralytics YOLOv5 🚀, AGPL-3. @g-hano hi there! It looks like the issue is with the directory runs\train\exp3, which is not being detected as a directory. 61 KB. jpg plotting your results. Augmentation Hyperparameters Feb 2, 2022 · Hyperparameters. 作者您好，请问hyp. 🔨 Track every YOLOv5 training run in the experiment manager. g. You can modify these parameters here, and, additionally, you can modify the hyperparameters for YOLOv7 at 'data/hyp. Additionally, you can also refer to the following brief summary of the YOLO v5 — small model. Make sure path of dataset in data. The folder with the dataset files should be located next to the yolov5 folder. When utilizing TRT FP16 batchsize=32 for inference on T4, YOLOv6-s can obtain 520FPS speed, which is 2. Oct 6, 2021 · Hyperparameters. Use the largest --batch-size possible, or pass --batch-size -1 for YOLOv5 AutoBatch. Jun 29, 2021 · I am trying to use sweeps with YOLOv5 but apparently, it is not compatible. hub. Such hyperparameters include learning rate, momentum, various parameters related Jul 25, 2023 · In YOLOv5, the augmentations specified in the hyperparameters or implemented with Albumentations are applied to the input images during training. 🔬 Get the very best mAP Jan 13, 2022 · Search before asking I have searched the YOLOv5 issues and discussions and found no similar questions. I've addressed this a bit at the end of this article. The value of the Hyperparameter is selected and set by the machine learning Jan 1, 2021 · There are multiple hyperparameters that we can specify which are: img: define input image size; batch: determine batch size; epochs: define the number of training epochs. py --weights yolov5s. This study optimized the latest YOLOv5 framework, including its subset models, with training on different datasets that differed in image contrast and cloudiness to assess model May 15, 2023 · In YOLOv5, you can change the default learning rate scheduler in train. 34 lines (33 loc) · 1. Dec 19, 2023 · Can i hyperparameter tune on a yolov5 n or s model then adapt those hyperparameters to the x model ? It may not translate well to the increase in model size but getting closer to great hyperparameters would be at least a good start. GA. These settings and hyperparameters can affect the model's behavior at various stages of the model development process, including training, validation, and prediction. Nov 12, 2023 · 이 가이드에서는 YOLOv5 🚀에 대한 하이퍼파라미터 진화에 대해 설명합니다. 6% faster than YOLOX-s. For instance, if the original input size is Nov 14, 2020 · Hello, I was looking for ways to do data augmentation and i found that yolov5 has some hyperparameters that appear to do that. py --img 640 --batch 16 --epochs 5 --data coco128. I found same issue when i trained the Yolov5 model on custom dataset using google colab, I did the following to resolve this. Models and datasets download automatically from the latest YOLOv5 release. 001838. # Train YOLOv5s on COCO128 for 5 epochspython train. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with Comet Custom Panels! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! Dec 7, 2021 · While this link may answer the question, it is better to include the essential parts of the answer here and provide the link for reference. Then train 10 epochs, and choose 2 of them depending on fintess metric. Los hiperparámetros en el ML controlan varios aspectos del entrenamiento, y encontrar . Cloning Repository and Installing Dependencies for YOLOv5; Clone the YOLOv5 PyTorch repository from GitHub, an open-source project. Please browse the YOLOv5 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and Nov 17, 2020 · YOLOv5 🚀 applies online imagespace and colorspace augmentations in the trainloader (but not the val_loader) to present a new and unique augmented Mosaic (original image + 3 random images) each time an image is loaded for training. It allows I'm super excited to announce our new YOLOv5 🚀 + Albumentations integration!! Now you can train the world's best Vision AI models even better with custom Albumentations automatically applied 😃! Aug 20, 2022 · I have searched the YOLOv5 issues and discussions and found no similar questions. 超参数进化是一种利用 遗传算法 (GA) 进行超参数优化的方法，我们可以通过该方法选择更加合适自己的超参数。. Notifications. Mar 12, 2021 · The issue is due to not found actual dataset path. 6 recommended for YOLOv5 training. Some of the hyperparameters have more Feb 20, 2024 · Create a virtual environment to manage dependencies and isolate project-specific packages, essential for controlling hyperparameters and ensuring compatibility, with Python 3. Made by Jorge Fernández Penalva using W&B. 🔦 Remotely train and monitor your YOLOv5 training runs using ClearML Agent. La evolución hiperparamétrica es un método de Optimización Hiperparamétrica que utiliza un Algoritmo Genético (AG) para la optimización. Aug 23, 2022 · It comes with a bunch of improvements which include state-of-the-art accuracy and speed. 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. 00194. scratch-high. 9% AP and the speed is increased by 29. py script and automatically logs your hyperparameters, command line arguments, training and validation metrics. py. 1 (#6525 by @glenn Jan 19, 2022 · I have searched the YOLOv5 issues and discussions and found no similar questions. (#6613 by @glenn-jocher). 225289531655344 --hyp. Expand. History. Sep 23, 2023 · However, picking the best hyperparameters for YOLOv5 might be difficult, so Optuna is a helpful tool. Hyperparameter Evolution: You can use the hyperparameter evolution feature to automatically find the best hyperparameters for your dataset. Link-only answers can become invalid if the linked page changes. Mar 21, 2021 · Okay, now that you have tweaked the hyperparameters, upgraded your model, tested with multiple image sizes and cross-validation. Question if pretrained: with torch_distributed_zero_first(rank): attempt_download(weights) # download if not found locally ckpt = torc Genetic Algorithm. Apr 21, 2023 · In this answer, we will discuss the different hyperparameters in YOLOs and YOLOv8 and their meanings. It seems to be related to the TensorBoard logging. 📚 Esta guía explica la evolución de hiperparámetros para YOLOv5 🚀. These model architectures and hyperparameters are passed in as the parameter space for the sweep. 1 percent AP accuracy on COCO val. 该 YAML 文件包含调整过程中发现的性能最佳的超参数。您可以使用该文件，用这些优化设置来初始化未来的训练。 格式： YAMLYAML; 使用方法超参数结果; Example: This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure. Hyperparameter Value Batch Size 64 Learning Rate 0. scratchcustom. Hyperparameter evolution is a method of Hyperparameter Optimization using a Genetic Algorithm (GA) for optimization. optim. This table summarizes hyperparameters specific to the yolov5 architecture. Trains YOLOv5 model with given hyperparameters, options, and device, managing datasets, model architecture, loss computation, and optimizer steps. Comet integrates directly with the Ultralytics YOLOv5 train. Star 42k. Nov 27, 2022 · Hyperparameters involved in neural networks (NNs) have a significant impact on the accuracy of model predictions. In the context of Ultralytics YOLO, these hyperparameters could range from learning rate to architectural details, such as the number of layers or types of Aug 2, 2020 · 📚 This guide explains hyperparameter evolution for YOLOv5 🚀. box=0. The improved network model of YOLOv5 (You Only Look Once) is effective in detecting underwater targets, with the mean average precision (mAP) reaching 87. YOLO an acronym for 'You only look once', is an object detection algorithm that divides images into a grid system. 62 KB. Please, tell me if I understand correctly. Hyperparameters in ML control various aspects of training, and finding optimal values for them can be a challenge. Additionally, please note that any hyperparameters that were changed during training can be found in the configuration ( . Add a comment. 21. Augmentation Hyperparameters Aug 18, 2023 · glenn-jocher commented on Nov 14, 2023. 5)%, which means in the training stage, the image size of each input batch will be randomly upsized or downsized by 50%. Feb 2, 2023 · The default parameter is (+- 0. We recommend you train with default hyperparameters first before thinking of modifying any. Fortunately, there are ways to set hyperparameters directly from the command line interface (CLI) without modifying the source code. If you are running W&B sweeps, which are different, and have questions there, you can ask our W&B expert @AyushExel. You can override specific hyperparameters by passing arguments to the training commands. This helps establish a performance baseline. The proposed modified model is then compared against default YOLOv5 trained using the same hyperparameters. yaml --img 1280 --epochs 300 # See tutorials for Nov 12, 2023 · Configuration. Considering that 6 days ago · Hyperparameter Tuning: Default Hyperparameters: Ensure you start with the default hyperparameters before making any changes. Aug 26, 2020 · Determining hyperparameters for data augmentation · Issue #852 · ultralytics/yolov5 · GitHub. Mar 30, 2023 · The development of artificial intelligence (AI) technologies, such as machine learning algorithms, computer vision systems, and sensors, has allowed maritime autonomous surface ships (MASS) to navigate, detect and avoid obstacles, and make real-time decisions based on their environment. Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU (Multi-GPU times faster). com Nov 16, 2023 · Similarly, the VisDrone dataset is also used in this work as it is an aerial imagery dataset but presents a different challenge, the images in DOTA are satellite images, whereas images in VisDrone are low-altitude images. data: set the path to our Nov 6, 2023 · Search before asking I have searched the YOLOv5 issues and discussions and found no similar questions. " Here the prefix "hyper" suggests that the parameters are top-level parameters that are used in controlling the learning process. The commands below reproduce YOLOv5 COCO results. If you started an evolution run which was interrupted, or finished normally, and you would like to continue for additional generations where you left off, then you pass --resume and specify the --name of the evolution you want to Feb 28, 2023 · In Yolov5 detection model, there are about 30 hyperparameters can be directory customized for data augmentation to improve performance rate. Blame. These augmentations can modify the appearance of the images by applying transformations such as blurring, brightness/contrast adjustments, or color shifts. The definition of generation in GA which has crossover + mutation (like my GA) is kind of different from GA that only has mutation (like yolo GA). Batch sizes shown for V100-16GB. Here is an example of how to implement the CosineAnnealingLR learning rate scheduler: In train. I understand that the current state of the repository might be confusing due to shared code and structure between YOLOv8 and YOLOv5, but rest assured that YOLOv8 is the primary focus here. # Hyperparameters for VOC finetuning Nov 12, 2023 · best_hyperparameters. The command clearml-data sync is actually a shorthand command. Run set. Feb 15, 2024 · Search before asking I have searched the YOLOv5 issues and discussions and found no similar questions. train, test, and valid key should contain path Feb 1, 2024 · Each of the YOLOv5 models showcased distinct architectural differences, with the complexity progressively increasing from YOLOv5n to YOLOv5x. 46816e+06 parameters, 7. Policy: Generative AI (e. By executing this cell, training will begin using the desired hardware. csv file with your results, and once training completes also an evolve. yaml should be be corrected. I was hoping for something like this: Nov 7, 2021 · If you are running YOLOv5 hyperparameter evolution, these can be tracked by W&B but the main output is an evolve. Nov 12, 2023 · 超参数演变. hi @glenn-jocher I writing a scientific paper and I need to be sure of the functions used in yolov5 so please if you could confirm with my information that: Activation function = [Leaky ReLU ] optimization function = [SGD] Cost Function =[Binary Cross Run The Training Script. May 19, 2023 · Training hyperparameters generally have a greater impact on the experimental results. Cannot retrieve latest commit at this time. 하이퍼파라미터 진화는 최적화를 위해 유전 알고리즘 (GA)을 사용하는 하이퍼파라미터 최적화 방법입니다. 📚 This guide explains hyperparameter evolution for YOLOv5 🚀. Lastly i couldnt find Information about how i can train only specific Hyperparameters. Nov 15, 2021 · I have searched the YOLOv5 issues and discussions and found no similar questions. You could also run these commands one after the other: YOLOv5 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, instance segmentation and image classification tasks. To this end, we designed a set of ablation experiments and compared the two sets of default training hyperparameters of YOLOv5, low-augment hyperparameters group (LAHG) and medium-augment hyperparameters group (MAHG), as shown in Table 5. However, the values of the hyperparameters need to be manually preset, and finding the best hyperparameters has always puzzled researchers. UPDATED 28 March 2023. yaml ファイルを /data/hyps ディレクトリにある。より良い初期推測はより良い最終結果を生むので、進化させる前にこれらの値を適切に初期化することが重要である。 📚 This guide explains hyperparameter evolution for YOLOv5 🚀. While many of the hyperparameters exposed are model-agnostic, there are instances where hyperparameters are model-specific or task-specific. to place an albumentations call to the more exotic augmentations). The detection speed of YOLOv3 was faster compared to YOLOv4 and YOLOv5 Nov 19, 2021 · hyperparameters; yolov5; or ask your own question. py by modifying the create_scheduler function. To address this, make sure that the runs\train directory exists before you start the training. Make sure provide correct path of data. Nov 18, 2022 · Hyperparameters. An adversarial attack Check this: #11124 (reply in thread) If your evolve value is 300 and pop size is 50, you need to train 300*50 models. 0/6. 本指南解释了YOLOv5 🚀 的 超参数演化 。. Dec 23, 2022 · YOLOv5 has about 25 hyperparameters that play a critical role in generating high-performance astrocyte detection. Augmentation Hyperparameters Jan 6, 2023 · To look closely at weights, biases, shapes, and parameters at each layer in the YOLOv5-small model, refer to the following information. Hi, I'm interested in the principle of hyperparameter evolution. Sep 17, 2021 · Hyperparameters. ultralytics / yolov5 Public. Introducing YOLOv8 🚀 We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀! From the results, it is concluded that YOLOv5 outperforms YOLOv7in terms of training time (17 minutes, 22 seconds). Nov 12, 2023 · ClearML is an open-source toolbox designed to save you time ⏱️. scratch-low. Where people create machine learning projects. Model Summary: 191 layers, 7. hyp. py, add the following import statement: from torch. Question. yaml of dataset. Hyperparameter Evolution. ML 中的超参数控制着训练的各个方面，而为它们找到最佳值是一项挑战。. Featured on Meta Upcoming sign-up experiments related to tags. yaml'. pt. yaml. For example, I would like to know the model version / release, on which image size the model was trained, and the hyperparameters used. 여기서 사용하는 하이퍼 파라미터 수치들은 기존에 모델의 수치를 바꾼다는 의미를 가지는 하이퍼 파라미터와는 다르게, 증강 & warm-up & loss 등 다양한 수치를 변경할 수 May 28, 2022 · YOLOv5 🚀 applies online imagespace and colorspace augmentations in the trainloader (but not the val_loader) to present a new and unique augmented Mosaic (original image + 3 random images) each time an image is loaded for training. 1. 6% AP and 38. Creating a custom model to detect your objects is an iterative process of collecting and organizing images, labeling your objects of interest, training a model, deploying it into the wild to make predictions, and then using that deployed model to collect examples of edge cases to repeat and improve. For now, I'd simply say that I'm referring to the algorithm as YOLOv5 since it is what the name of the code repository is. csv, I have many hyperparameters having the same values in different generations. This comprehensive understanding will help improve your practical application of object detection in various fields Nov 12, 2023 · YOLOv5 ハイパーパラメータ進化ガイド; レイチューンとハイパーパラメータの効率的なチューニングYOLOv8; より深い洞察については Tuner クラスのソースコードと付属のドキュメントを提供しています。ご質問、機能リクエスト、その他サポートが必要な場合は Before we begin, let me acknowledge that YOLOv5 attracted quite a bit of controversy when it was released over whether it's right to call it v5. Apr 24, 2023 · YOLOv5 Hyperparameters: lr0, lrf — The learning rate is a hyperparameter that determines the step size at which a neural network’s parameters are updated during training. Further, the F1 score is found to increase by 2. clearml-data sync --project YOLOv5 --name coco128 --folder . Nov 12, 2023 · Evolución de los hiperparámetros. Jan 1, 2022 · Abstract. It was found that YOLOv5 outperforms YOLOv4 and YOLOv3 in terms of accuracy. 48% when compared to YOLOv7 Dec 11, 2022 · YOLOv5 🚀 applies online imagespace and colorspace augmentations in the trainloader (but not the val_loader) to present a new and unique augmented Mosaic (original image + 3 random images) each time an image is loaded for training. Start Logging¶ Setup the YOLOv5 repository¶ Export Benchmarks: Benchmark (mAP and speed) all YOLOv5 export formats with python utils/benchmarks. Benchmarked on the COCO dataset, the YOLOv7 tiny model achieves more than 35% mAP and the YOLOv7 (normal) model achieves more than 51% mAP. 6 days ago · Ultralytics YOLOv5 is a family of object detection architectures and models pretrained on the COCO dataset. It also achieved 43. Default hyperparameters are in hyp. Code. Question I am trying to use yolov5 for semantic segmentation and I wanted to use torch. 提供的默认参数也是通过在COCO数据集上使用超参数进化得来的。. Jul 2, 2023 · To clarify, this repository is focused solely on YOLOv8, and any references or requirements related to YOLOv5 should not be applicable to this repo. lr_scheduler import CosineAnnealingLR. `hyp` argument is path/to/hyp. 46816e+06 gradients 3. 0. Nov 12, 2023 · YOLOv5 には、さまざまな学習設定に使われる約30のハイパーパラメータがある。これらは *. scratch. 超参数演化是一种使用 遗传算法 （GA）进行优化的 超参数优化 方法。. Model-specific hyperparameters. In general, increasing augmentation hyperparameters will reduce and delay overfitting, allowing for longer trainings and higher final mAP. 001 Number of Epochs 10 Optimizer Adam Weight Decay 1×10−4 Input Image Size 416x416 Table 5: Hyperparameters employed for training YOLOv5. e. Watch: Mastering Ultralytics YOLOv8: Configuration. 68 µs 2024-02-15 09:33:27. In order to improve the accuracy and speed of target recognition by a neural network, an improved genetic algorithm is proposed to optimize To get this dataset into ClearML as a versioned dataset, go to the dataset root folder and run the following command: cd coco128. avdhwxkoqbalxdsayoce