Hey everyone and welcome to the second part of the YOLOv5 series! In this video (Part 2) we'll learn about collecting and labelling data in order to train a YOLOv5 model on a custom dataset. We'll start by asking if object detection is the right approach to begin with. If it is, we'll talk about creating your own dataset, using labelling tools to draw bounding boxes, and using Weights & Biases Artifacts to upload data to the cloud, so that we can train on a virtual machine like Google Colab. Links: 📍 Example Colab for training: https://wandb.me/yolo-colab 📍 ModifiedOpenLabelling: https://github.com/ivangrov/ModifiedOpenLabelling 📍 OpenLabelling: https://github.com/Cartucho/OpenLabeling 📍 COCO dataset: https://cocodataset.org/#home 📍Video I made about downloading Google Images: https://www.youtube.com/watch?v=Yt6Gay8nuy0 Upload dataset command (different than what I show in the video, slightly updated): python utils/loggers/wandb/log_dataset.py --project custom_yolov5 --data data/custom_dataset.yaml --- You can watch the whole YOLOv5 Series here: 🚀Part 0 - Overview of the YOLOv5 and W&B integration: https://www.youtube.com/watch?v=yyecuhBmLxERocket 🚀Part 1 - Install YOLOv5 on Windows and Google Colab: https://www.youtube.com/watch?v=gDoMYuyY_qwRocket 🚀Part 2 - Collect & Label a Custom Dataset: https://www.youtube.com/watch?v=a9Bre0YJ8L8Rocket 🚀Part 3 - Train a Custom YOLOv5 Model to Detect Bus Numbers: https://www.youtube.com/watch?v=5h5UtLau3Vc --- Follow Ivan: 👉 Twitter: https://twitter.com/Ivangrov 👉 YouTube: null --- ⏳ Timestamps ⏳ 0:00 Intro & overview 1:00 Is object detection right for your problem? 2:58 Creating a dataset 5:01 The importance of true negatives 6:13 The YOLOv5 label format 8:03 Labelling tools and labelling 12:05 Using W&B Artifacts to upload a dataset to the cloud 14:51 Using W&B Tables to visualize a dataset in the browser 16:51 Outro --- Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected -- Credits 📍 VBTI (stuffed animal inside a bounding box): https://www.linkedin.com/posts/carlolepelaars_vbti-ai-deeplearning-activity-6694260638857867264-eMfK/ 📍 3D Object Detection: https://www.youtube.com/watch?v=VnbeIwJCHlk 📍 Image segmentation: https://www.youtube.com/watch?v=qWl9idsCuLQ