To export your own data for this tutorial, sign up for Roboflow and make a public workspace, or make a new public workspace in your existing account. You can follow along with the public blood cell dataset or upload your own dataset. In the tutorial, we train YOLOv5 to detect cells in the blood stream with a public blood cell detection dataset. In this tutorial we will download object detection data in YOLOv5 format from Roboflow. Download Custom YOLOv5 Object Detection Data If you are attempting this tutorial on local, there may be additional steps to take to set up YOLOv5. 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. We recommend following along concurrently in this YOLOv5 Colab Notebook. Export Saved YOLOv5 Weights for Future Inference.Define YOLOv5 Model Configuration and Architecture.Download Custom YOLOv5 Object Detection Data.To train our detector we take the following steps: You can also use this tutorial on your own custom data. We use a public blood cell detection dataset, which you can export yourself. In this post, we will walk through how you can train YOLOv5 to recognize your custom objects for your use case. The YOLO family of object detection models grows ever stronger with the introduction of YOLOv5.
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