![]() Colab comes preinstalled with torch and cuda. The GPU will allow us to accelerate training time. Here is what we received: torch 1.5.0+cu101 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', major=6, minor=0, total_memory=16280MB, multi_processor_count=56) It is likely that you will receive a Tesla P100 GPU from Google Colab. Print('torch %s %s' % (torch._version_, _device_properties(0) if _available() else 'CPU')) import torchįrom IPython.display import Image # for displaying imagesįrom utils.google_utils import gdrive_download # for downloading models/datasets Then, we can take a look at our training environment provided to us for free from Google Colab. !pip install -U -r yolov5/requirements.txt # install dependencies This will set up our programming environment to be ready to running object detection training and inference commands. To start off we first clone the YOLOv5 repository and install dependencies. Learn more about YOLOv8 in the Roboflow Models directory and in our " How to Train YOLOv8 Object Detection on a Custom Dataset" tutorial. In January 2023, Ultralytics released YOLOv8, defining a new state-of-the-art in object detection. DistributedDataParallel (DDP) Frameworkĭownloading /root/.mxnet/models/faster_rcnn_resnet50_v1b_coco-5b4690fb.zip from. Computing FLOPS, latency and fps of a model Extracting video features from pre-trained models Fine-tuning SOTA video models on your own dataset Getting Started with Pre-trained I3D Models on Kinetcis400 ![]() Distributed training of deep video models Train classifier or detector with HPO using GluonCV Auto task Train Image Classification with Auto Estimator Load web datasets with GluonCV Auto Module Prepare your dataset in ImageRecord format.Prepare the 20BN-something-something Dataset V2.Prepare custom datasets for object detection.Testing PoseNet from image sequences with pre-trained Monodepth2 Pose models Predict depth from an image sequence or a video with pre-trained Monodepth2 models Predict depth from a single image with pre-trained Monodepth2 models Multiple object tracking with pre-trained SMOT models Single object tracking with pre-trained SiamRPN models Inference on your own videos using pre-trained models Dive Deep into Training SlowFast mdoels on Kinetcis400 Getting Started with Pre-trained SlowFast Models on Kinetcis400 Dive Deep into Training I3D mdoels on Kinetcis400 Dive Deep into Training TSN mdoels on UCF101 Introducing Decord: an efficient video reader Getting Started with Pre-trained TSN Models on UCF101 Dive deep into Training a Simple Pose Model on COCO Keypoints Predict with pre-trained AlphaPose Estimation models Predict with pre-trained Simple Pose Estimation models Test with ICNet Pre-trained Models for Multi-Human Parsing Getting Started with FCN Pre-trained Models Predict with pre-trained Mask RCNN models Run an object detection model on NVIDIA Jetson module Predict with pre-trained CenterNet models Skip Finetuning by reusing part of pre-trained model Run an object detection model on your webcam Train Faster-RCNN end-to-end on PASCAL VOC ![]() Deep dive into SSD training: 3 tips to boost performance Predict with pre-trained Faster RCNN models Transfer Learning with Your Own Image Dataset Getting Started with Pre-trained Models on ImageNet Getting Started with Pre-trained Model on CIFAR10
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