Tensorflow benchmark gpu 2021. ua/voeojq/talgai-forest-fossicking.
From nvidia-smi utility it is visible that Pytorch uses only about 800MB of GPU memory, while Tensorflow essentially uses whole memory. Cats dataset. Some charts and tables may be missing if you run TensorBoard entirely offline on your local machine, behind a corporate firewall, or in a datacenter. normal([1000, 1000])))" If a tensor is returned, you've installed TensorFlow successfully. 정상 상태의 추론 시간. Pour simplifier l'installation et éviter les conflits de bibliothèques, nous vous recommandons d'utiliser une image Docker TensorFlow compatible avec les GPU (Linux uniquement). 초기화 시간 동안의 메모리 사용량. This version of TensorFlow is usually easier to install, so even if you have an NVIDIA GPU, we recommend installing this version first. Horovod discrepancies on eval_during_training_every_epochs. 2. Mar 26, 2024 · Based on OpenBenchmarking. Cats competition was an early Kaggle competition to demonstrate the power of convnets to solve computer vision recognition problems as winning entries reached … Continue reading "Compare GPU and CPU Training Jan 17, 2024 · This guide demonstrates how to use the tools available with the TensorFlow Profiler to track the performance of your TensorFlow models. bat Troubleshooting. 0以上、tf1. distribute. The table shows the training speed for the two models using 32-bit floats. The performance of TensorFlow Serving is highly dependent on the application it runs, the environment in which it is deployed and other software with which it shares access to the underlying hardware resources. Oct 8, 2018 · As of February 8, 2019, the NVIDIA RTX 2080 Ti is the best GPU for deep learning. To run iOS benchmarks, the benchmark app was modified to include the appropriate model and benchmark_params. You will learn how to understand how your model performs on the host (CPU), the device (GPU), or on a combination of both the host and device(s). Verify the GPU This Docker image is based on the latest tensorflow/tensorflow image with python and gpu support. Discover TensorFlow's flexible ecosystem of tools, libraries and community resources. 12以下版本共存)CUDA Toolkit、cuDNN、Pycharm的安裝經驗分享” is published by Johnny Liao. M1 has 8 cores (4 performance and 4 efficiency), while Ryzen has 6: Image 3 - Geekbench multi-core performance (image by author) M1 is negligibly faster - around 1. 0, however cudnn 8. This will show you a screen like so, that updates every three seconds. 0 is not in anaconda as of 16/12/2020) DigitalCommons@UMaine | The University of Maine Research Dec 27, 2017 · That’s whooping ~ 1190 examples/sec, which is decent for an old-timer (940MX). Performance benchmark. Its a single input, 2 output model. 94735 s. Here’s where they drift apart. For those not needing the full compute power of A100, you should consider the A30 as an option. RunOptions. This allows some image classification models to be executed within the container with GPUs by passing the corresponding arguments to the docker run command. Aug 28, 2022 · Tensorflow runs best on a high end GPU and the M1 contains a great GPU. 09-10-2021 01:30 PM. XLA was used to optimize the graph for GPU execution to further improve the performance of the V100 GPUs. This version of ResNet-50 utilizes mixed-precision FP16 to maximize the utilization of Tensor Cores on the NVIDIA Tesla V100. Enable mixed precision (with fp16 (float16)) and optionally enable XLA. py and PyTorch Hub Oct 26, 2021 · The following are the training results and Activity Monitor screenshots of the CNN model trained with tensorflow (CPU) and tensorflow-metal (GPU). Dec 18, 2019 · If you want to check the performance of Nvidia graphic cards, run the following commands: pip install tensorflow-gpu. コレクションでコンテンツを整理 必要に応じて、コンテンツの保存と分類を行います。. tf. Note 2: For running the benchmark on Nvidia GPUs, NVIDIA CUDA and cuDNN libraries should be installed first. La compatibilité GPU de TensorFlow nécessite un ensemble de pilotes et de bibliothèques. unzip benchmarks-cnn_tf_v1. x, PyTorch. cudatoolkit=11. Prepare virtual environments. nvidia-smi. 0 uses cuda 11. 99x (99% efficiency) for InceptionV3 and 7. 0-rc1 and tensorflow-gpu==2. Does someone have a clue where I can find the problem. As many machine learning algorithms rely to matrix multiplication (or at least can be implemented using matrix multiplication) to test my GPU is I plan to create matrices a , b , multiply them and record time it takes for computation to complete. VM Image Information. Jul 17, 2020 · GPU and CPU utilisation stats as well as corresponding code for both frameworks is found below. These benchmarks allow you to measure Horovod’s performance and scalability in your environment, as well as try advanced Horovod features like gradient compression: $ horovodrun -np 4 -H server1:2 Jul 4, 2021 · TensorFlow 2 in Anaconda Installation: Open an Anaconda Prompt (Anaconda3) Terminal ← search “Anaconda Prompt” in the Start menu and window will pop up and read (base) C:\Users\name>. Tensorflow 블로그 에 따르면 CPU Jan 23, 2017 · 8. Tensorflow Lite 는 모바일 딥러닝을 지원하는 딥러닝 프레임워크입니다. yml under your repository. The API, featured in 2019, introduced essential primitives for pruning, and enabled researchers throughout the world with new optimization techniques. pip install tensorflow-gpu==2. If you want to check the performance of AMD graphic cards: follow these instructions. RunMetadata() sess. 11 and later no longer support GPU on Windows. 91x (98% efficiency) for ResNet-50, compared to using a single GPU. list_physical_devices('GPU')" Ignore the warning successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero Also to draft benchmark your GPU you still (repo is not 4 days ago · Achieving peak performance requires an efficient input pipeline that delivers data for the next step before the current step has finished. To use the GPU delegate, "use_gpu" : "1" and "gpu_wait_type" : "aggressive" options were also added to benchmark Jun 28, 2019 · If you want to check the performance of Nvidia graphic cards: 2. Of course, it would be better if you could fully utilize your GPU, but writing software that does this is challenging and becomes virtually impossible when you are talking about complex applications like GANs. Jan 28, 2021 · Performance Guide. Aug 23, 2021 · Hi community, I am doing inference of faster rcnn-resnet, having 24 GB titan rtx GPU, GPU memory utilisation is 100% but GPU utilisation is around 50%, and I am getting around 10 fps and the benchmark of that same model … Jan 17, 2021 · This will give you access to the M1 GPU in Tensorflow. ) Performance Comparison. can we expect, once multi-GPU is available for the M1, an increase in performance - maybe close to 8x if the 8 GPU cores become available, and would the GPU cores will be seen as a single GPU as the NVIDIA cards or we will need to use a distribution strategy to be able to use them in parallel? Sep 24, 2021 · Starting from TensorFlow v2. Run pip install ai-benchmark from the 5 days ago · TensorFlow-DirectML broadens the reach of TensorFlow beyond its traditional Graphics Processing Unit (GPU) support, by enabling high-performance training and inferencing of machine learning models on any Windows devices with a DirectX 12-capable GPU through DirectML, a hardware accelerated deep learning API on Windows. 13x to 3. TensorFlow 1. can we expect, once multi-GPU is available for the M1, an increase in performance - maybe close to 8x if the 8 GPU cores become available, and would the GPU cores will be seen as a single GPU as the NVIDIA cards or we will need to use a distribution strategy to be able to use them in parallel? Sep 10, 2021 · AMD GPUs Support GPU-Accelerated Machine Learning with Release of TensorFlow-DirectML by Microsoft. Android NNAPI is the convenient way to access additional ML accelerators on Android devices. Verify the CPU setup: python3 -c "import tensorflow as tf; print(tf. #396 opened on Jun 25, 2019 by taipin. Sep 27, 2021 · 1. This repository contains various TensorFlow benchmarks. As expected, GPU training is two times faster than CPU training in M1. conda install tensorflow-gpu=2. Synthetic data We have observed speedups ranging from 1. Install benchmarks scripts The scripts and tf-nightly-gpu should be paired. 12 with XLA. Debug the performance of one GPU. 96% as fast as the Titan V with FP32, 3% faster Nov 15, 2021 · November 15, 2021 — Posted by Valentin Bazarevsky,Ivan Grishchenko, Eduard Gabriel Bazavan, Andrei Zanfir, Mihai Zanfir, Jiuqiang Tang,Jason Mayes, Ahmed Sabie, Google Today, we're excited to share a new version of our model for hand pose detection, with improved accuracy for 2D, novel support for 3D, and the new ability to predict keypoints on both hands simultaneously. Also the performance of multi GPU setups like a quad RTX 3090 configuration is evaluated. list_physical_devices ('GPU') を使用して The top part of the environment file contains some useful commands. An end-to-end open source machine learning platform for everyone. Train times under above mentioned conditions: TensorFlow: 7. Fail to install Visual Studio Integration in cuda installation progress. js pose-detection API supports two runtimes: TensorFlow. 44318 s PyTorch: 27. So, even if one GPU is in use, it will consume the memory of all available GPUs. Jul 27, 2021 · This article compares the training times for fitting a Tensorflow 2 convolutional neural network (CNN or convnet) using a GPU or CPU on the Kaggle Dogs vs. Oct 28, 2021 · Results. TensorFlow. The tensorflow/benchmarks repository is cloned and used as an entrypoint for the container. 16. If your system does not have a NVIDIA® GPU, you must install this version. my code uses GPU but it is not using 100%, and when I run benchmark - GitHub - lambdal/lambda-tensorflow-benchmark. Then run. GPU training/inference speeds using PyTorch®/TensorFlow for computer vision (CV), NLP, text-to-speech (TTS), etc. MirroredStrategy already does all the job for us and model trains quickly on a single GPU. Today, we are extending the XNNPACK backend to Jul 20, 2019 · Win10上的tensorflow安裝. A year ago TensorFlow Lite increased performance for floating-point models with the integration of XNNPACK backend. run(res, options=run_options, run_metadata=run_metadata) # Create the Jan 20, 2024 · NVIDIA's RTX 3090 is the best GPU for deep learning and AI in 2020 2021. Here is code that will generate two matrices of dimensions 300000,20000 and multiply them : GPU を使用する. TensorFlow Lite enables the use of GPUs and other specialized processors through hardware driver called delegates. By default this test profile is set to run at least 3 times but may increase if the standard deviation exceeds pre-defined defaults or other calculations deem Aug 23, 2021 · Hi community, I am doing inference of faster rcnn-resnet, having 24 GB titan rtx GPU, GPU memory utilisation is 100% but GPU utilisation is around 50%, and I am getting around 10 fps and the benchmark of that same model … Aug 23, 2021 · Hi community, I am doing inference of faster rcnn-resnet, having 24 GB titan rtx GPU, GPU memory utilisation is 100% but GPU utilisation is around 50%, and I am getting around 10 fps and the benchmark of that same model … Jul 20, 2019 · Win10上的tensorflow安裝. The run_op_benchmark is passed in the Feb 8, 2021 · When you import tensorflow, a large log is produced in the terminal, and it literally has all the information about missing libraries and GPU support, please include that, as text. 0-rc1. 13 onwards this has been simplified to: Create a new virtual environment using conda/venv/etc. keras モデルは、コードを変更することなく単一の GPU で透過的に実行されます。. Save the following as environment. Chart 1: Bar graph showing performance on ResNet50v1 training with synthetic data, comparing TensorFlow v1. That’s almost ~ 2. It adds TensorRT, Edge TPU and OpenVINO support, and provides retrained models at --batch-size 128 with new default one-cycle linear LR scheduler. Let’s compare the multi-core performance next. The tf. A benchmark framework for Tensorflow. One GPU: 888 images/sec without XLA, 1,401 images/sec with. Especially the multi-GPU support is not working yet reliable (December 2022). To solve the world’s most profound challenges, you need powerful and accessible machine learning (ML) tools that are designed to work across a broad spectrum of hardware. 04 Window Manager: XFCE @article{hu2019randla, title={RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds}, author={Hu, Qingyong and Yang, Bo and Xie, Linhai and Rosa, Stefano and Guo, Yulan and Wang, Zhihua and Trigoni, Niki and Markham, Andrew}, journal={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, year={2020} } @article{hu2021learning, title={Learning Semantic Sep 17, 2021 · Check this question at Apple Developer Forums. RunOptions(trace_level=tf. 전체 메모리 사용량. May 4, 2020 · The problem is that by using 2x larger total batch size with 2 GPUs (16), the training time becomes 3x slower per epoch. Aug 7, 2017 · The easiest way to check the GPU usage is the console tool nvidia-smi. (2. Feb 10, 2024 · Run GPU Setup Test, and optionally the Benchmark shown above to prove it’s working. Today we are happy to announce experimental updates to the API that Deep Learning GPU Benchmarks. 5, the standard Adam optimizer tf. The NVIDIA H100 just became available in late 2022 and therefore the integration in Deep Learning frameworks (Tensorflow / Pytorch) is still lacking. On the latter, Apple used to insinuate on their website by the graphic mentioned in this Macrumors article , that the M1 Ultra (the most powerful M1 GPU at the time of writing, 27 Aug 2022), has the same maximum performance as the most performant discrete graphics card . Dec 27, 2017 · That’s whooping ~ 1190 examples/sec, which is decent for an old-timer (940MX). Run pip install tensorflow-gpu from the command line. Download and install cuDNN. reduce_sum(tf. optimizers. 11 without XLA vs TensorFlow v1. We can see that training on both tensorflow and tensorflow-metal achieved similar training and validation accuracy. TensorFlow programs usually run much faster on a GPU instead of a CPU. 워밍업 상태의 추론 시간. As suggested in the comments, you can use something like watch -n1 nvidia-smi to re-run the program continuously (in this case every second). The following performance benchmark aims to show an overall comparison of the single-machine eager mode performance of PyTorch by comparing it to the popular graph-based deep learning Framework TensorFlow. 04x on a variety of internal models. 3. TensorFlow Lite 벤치마크 도구는 현재 다음과 같은 중요한 성능 지표에 대한 통계를 측정하고 계산합니다. So, a Benchmark object can be made and used to execute a benchmark on part of a tensorflow graph. Contribute to tensorflow/benchmarks development by creating an account on GitHub. js provides the flexibility and wider adoption of JavaScript, optimized for several backends including WebGL (GPU), WASM (CPU), and Node. Included are the latest offerings from NVIDIA: the Ampere GPU generation. Open a windows command prompt and navigate to that directory. Aug 23, 2021 · Hi community, I am doing inference of faster rcnn-resnet, having 24 GB titan rtx GPU, GPU memory utilisation is 100% but GPU utilisation is around 50%, and I am getting around 10 fps and the benchmark of that same model … Horovod synthetic benchmarks ¶. TensorFlow with GPU support. data API helps to build flexible and efficient input pipelines. The Dogs vs. Apr 11, 2024 · The GPU acceleration is automated in TensorFlow meaning there is no control over memory usage. It uses CUDA to specify the usage of CPU or GPU. From TensorFlow 2. May 11, 2017 · 3. ## filename: environment. TensorFlow のコードと tf. js and MediaPipe. To make performance benchmarking you need a PC with Nvidia GPU and installed nvidia drivers. Run Aug 23, 2021 · Hi community, I am doing inference of faster rcnn-resnet, having 24 GB titan rtx GPU, GPU memory utilisation is 100% but GPU utilisation is around 50%, and I am getting around 10 fps and the benchmark of that same model … Sep 17, 2021 · Check this question at Apple Developer Forums. Whether you're a data scientist, researcher, or developer, the RTX 3090 will help you take your projects to the next level. 벤치마크 도구는 Android 및 iOS용 벤치 TensorFlow GPU 지원에는 다양한 드라이버와 라이브러리가 필요합니다. We can conclude that both should perform about the same. yml. 14~tf1. Aug 23, 2021 · Hi community, I am doing inference of faster rcnn-resnet, having 24 GB titan rtx GPU, GPU memory utilisation is 100% but GPU utilisation is around 50%, and I am getting around 10 fps and the benchmark of that same model … An overview of current high end GPUs and compute accelerators best for deep and machine learning tasks. 87 times quicker than respective CPU for the laptop, which gives justification to having a GPU Sep 15, 2022 · Optimize and debug the performance on one GPU: Check if the input pipeline is a bottleneck. zip and remember where tf_cnn_benchmarks. Currently, it consists of two projects: PerfZero: A benchmark framework for TensorFlow. # For GPU users pip install tensorflow[and-cuda] # For CPU users pip install tensorflow 4. 注意: tf. So currently the RTX 4090 GPU is only recommendable as a single GPU system. 1 - Device: CPU - Batch Size: 1 - Model: ResNet-50) has an average run-time of 3 minutes. It gives ,me 218 images / sec processing and this takes 95-99% Gpu utilisation, Bhack August 23, 2021, 11:48am #16. 0. With 8 NVIDIA Tesla P100s, we report a speedup of 7. This is Aug 30, 2023 · GPU delegates for TensorFlow Lite. Here we can see various information about the state of the GPUs and what they are doing. Aug 1, 2023 · Here’s how you can verify GPU usage in TensorFlow: Check GPU device availability: Use the `tf. Tensorflow Keras ai benchmark. Sep 13, 2022 · These performance benchmark numbers were generated with the iOS benchmark app. It is fairly normal for utilization to be erratic like for any kind of parallelized software, including training GANs. 0 and cudnn 8. 설치를 단순화하고 라이브러리 충돌을 방지하려면 GPU를 지원하는 TensorFlow Docker 이미지 를 사용하는 것이 좋습니다 (Linux만 해당). In this section, we showcase the performance of the HugeCTR TensorFlow embedding plugin through synthetic and real use cases. Jan 27, 2019 · Introduction. pip install ai-benchmark. The following are results comparing Especially the multi-GPU support is not working yet reliable (December 2022). This leads to nine hours six minutes for hundred-thousand images. 35% faster than the 2080 with FP32, 47% faster with FP16, and 25% more costly. Dec 15, 2015 · I have used the Timeline object to get the time of execution for each node in the graph: Here is an example program that measures the performance of a matrix multiplication: run_options = tf. list_physical_devices ('GPU') を使用して Dec 4, 2023 · Comparing PyTorch vs. Cette configuration ne nécessite que les pilotes de GPU NVIDIA®. org data, the selected test / test configuration ( TensorFlow 2. py is; modify %PYTHON% and %SCRIPTS% variables in run. Optimize and debug the performance on the multi-GPU single host. config. 아래의 설치 Note: The TensorFlow Profiler requires access to the Internet to load the Google Chart library. Using graphics processing units (GPUs) to run your machine learning (ML) models can dramatically improve the performance of your model and the user experience of your ML-enabled applications. “Windows安裝Tensorflow-gpu(2. In the code below, a benchmark object is instantiated and then, the run_op_benchmark method is called. 3%. The NVIDIA A30 is a well rounded GPU for most deep learning applications. x, TensorFlow 2. This page will show you how to test your VM’s GPU and access and test the software that is pre-installed on the scs-gpu-TENSORFLOW-2021 virtual machine. Do not post text as images. If you want to run TensorFlow models and Nov 2, 2021 · validation_data=ds_test, ) Now moment of the truth: For this test, M1 Max is 40% faster than Nvidia Tesla K80 (costing £3300) in total run time and 21% faster in time per epoch. Oct 27, 2019 · The test will compare the speed of a fairly standard task of training a Convolutional Neural Network using tensorflow==2. Note 1: If Tensorflow is already installed in your system, you can skip the first command. 모바일 딥러닝 개발에 필요한 모델 경량화, 벤치마크 툴 등 다양한 편의 기능을 제공하고 최근 버전 에서 모바일 GPU를 지원하는 기능을 발표했습니다. Apr 26, 2020 · Check GPU support is enabled, and you can access your GPU: > python -c "import tensorflow as tf; tf. On the contrary, PyTorch does not automate GPU usage and does not have a dedicated library for GPU users. Update as of July 2023. Aug 20, 2021 · Nitish_Jha August 23, 2021, 11:43am #15. Custom PC has a dedicated RTX3060Ti GPU Aug 16, 2021 · GPU is the most widely available accelerator and provides a decent performance boost. random. Install tensorflow: python -m pip install tensorflow; then; Install the plugin: python -m pip install tensorflow-metal. 12_compatible. Verify the installation. We use the tf_cnn_benchmarks implementation of ResNet-50 v1. 아래의 설치 Jul 17, 2020 · GPU and CPU utilisation stats as well as corresponding code for both frameworks is found below. However, unlike top or other similar programs, it only shows the current usage and finishes. Dec 13, 2020 · A solution is to install an earlier version of tensorflow, which does install cudnn and cudatoolkit, then upgrade with pip. Since the A30 is FP64 capable, it may also be well suited for other HPC applications. This document demonstrates how to use the tf. list_physical_devices (‘GPU’)` function in a Python script to check if the GPU device is available and recognized by TensorFlow. Jun 30, 2021 · Google’s submissions for the most recent MLPerf demonstrated leading top-line performance (fastest time to reach target quality), setting new performance records in four benchmarks. 4. Adam, which now comes with a GPU implementation, can be used with similar accuracy and performance. Jul 3, 2024 · Then, install TensorFlow with pip. The following are results comparing May 10, 2017 · Our benchmarks show that TensorFlow has nearly linear scaling on an NVIDIA® DGX-1™ for training image classification models with synthetic data. 이 설정에는 NVIDIA® GPU 드라이버 만 있으면 됩니다. The training speed is 328 milliseconds for a greyscale image with the size of 512x512 pixel. Windows Specific Steps TensorFlow 2. keras. The screen movie shows M1 were using the full range of its GPU power. 1. Download and install CUDA from Nvidia website. This can range from datacenter applications for The NVIDIA A30 exhibits near linear scaling up to 8 GPUs. data API to build highly performant TensorFlow input pipelines. 2. conda env create -f environment. Tensorflow includes an abstract class that provides helpers for TensorFlow benchmarks: Benchmark. exe. json was modified to set num_threads to 2. This repository provides code to compare the performance of the following frameworks: TensorFlow 1. 13及1. 1. 87 times quicker than respective CPU for the laptop, which gives justification to having a GPU May 4, 2020 · The problem is that by using 2x larger total batch size with 2 GPUs (16), the training time becomes 3x slower per epoch. Choose the FP16 quantized models if you want to leverage GPUs. TensorFlow GPU 지원에는 다양한 드라이버와 라이브러리가 필요합니다. Horovod also comes with out-of-the-box benchmarking support for TensorFlow v1 , TensorFlow v2, and PyTorch. It has exceptional performance and features make it perfect for powering the latest generation of neural networks. FULL_TRACE) run_metadata = tf. Synthetic data May 10, 2017 · Our benchmarks show that TensorFlow has nearly linear scaling on an NVIDIA® DGX-1™ for training image classification models with synthetic data. scripts/tf_cnn_benchmarks (no longer maintained): The TensorFlow CNN benchmarks contain TensorFlow 1 benchmarks for several convolutional neural networks. And consider pinning the following three libraries: An example: tensorflow-gpu=2. We achieved this by scaling up to 3,456 of our next-gen TPU v4 ASICs with hundreds of CPU hosts for the multiple benchmarks. Enable GPU memory growth: TensorFlow automatically allocates all GPU memory by default. Moreover, the CNN model takes on average 40ms/step on CPU as compared to 19ms/step Sep 24, 2021 · Starting from TensorFlow v2. Jul 20, 2021 · TensorFlow has long standing support for neural network pruning via TensorFlow Model Optimization Toolkit (TF MOT) Pruning API. exe -l 3. MediaPipe capitalizes on WASM with GPU accelerated processing and provides faster out-of-the-box Sep 9, 2021 · September 09, 2021 — Posted by Marat Dukhan and Frank Barchard, software engineers Quantization is among the most popular methods to speedup neural network inference on CPUs. NVIDIA H100. For single-GPU training, the RTX 2080 Ti will be 37% faster than the 1080 Ti with FP32, 62% faster with FP16, and 25% more costly. Aug 19, 2021 · Nitish_Jha August 23, 2021, 11:43am #15. May 19, 2021 · The new TensorFlow. The neural network has ~58 million parameters and I will benchmark the performance by running it for 10 epochs on a dataset with ~10k 256x256 images loaded via generator with image Oct 8, 2019 · C:\Program Files\NVIDIA Corporation\NVSMI\nvidia-smi. 초기화 시간. As such, tuning its performance is somewhat case-dependent and there are very few universal rules that are Aug 7, 2018 · TensorFlow with CPU support only. GPU を使用する. cudnn=8. 5 training for the GPU benchmark. Image Name: scs-gpu-TENSORFLOW-2021 Creation Date: January 26, 2021 Operating System: Ubuntu 20. We have observed speedups ranging from 1. 3. This release incorporates new features and bug fixes (271 PRs from 48 contributors) since our last release in October 2021. YOLOv5 now officially supports 11 different formats, not just for export but for inference (both detect. ov tr fn zh fn he il qs oq lt