Take a look at the to quickly find common resources and/or assets for a given dataset and a specific task, in this case dataset=COCO, task=object detection. We are building a dataset-first marketplace focusing on the end-to-end machine learning pipeline, one where data and assets can be shared and traded. The marketplace will contain all that the contains (and much more for a lot more datasets) report report I’m open to suggestions, questions, and criticism — let’s start a conversation. I have broken up the report into the following blogs: Part 1 (this one): COCO Summary Card. Each link will take you to the longer report where you can learn more. The next 3 parts represent a specific section in the report. Part 2: This part is about COCO and examples and tutorials of tools and platforms used to work with COCO (or object detection tasks). Part 3: Process: This part is about the tools and platforms that can be used for different phases of data preparate or data processing involved in vision, object detection, and specifically COCO-related tasks. It will also discuss synthetic data and data quality. Part 4: Models: This part is about a quick introduction to some pre-trained models and some corresponding readings. COCO Summary Card (Common Objects in Context) COCO COCO is a large-scale object detection, segmentation, and captioning dataset. The dataset classes include 80 pre-trained objects. Website: https://cocodataset.org/ Github: GitHub — cocodataset/cocodataset.github.io Paper: Microsoft COCO: Common Objects in Context dataset with evaluation metric Average Precision (AP) Benchmark : Object Detection, Panoptic Semantic Segmentation, Keypoint Detection, DensePose Tasks Tools and platforms used to work with COCO (or object detection tasks) : Explore with tutorials FiftyOne, DataTorch, Know Your Data (KYD), OpenCV, OpenVINO, CVAT, Roboflow, SuperAnnotate, OpenMMLab, Coral, Amazon, Facebook, Google, Microsoft, NVIDIA, Weights and Biases. Open-source tools and Paid platforms to perform the following steps. : Processing Data collection : CVAT, LabelImg, Label Studio, Makesense.AI, OpenLabeling, Dataloop, Hive Data, Labelbox, Scale AI, SuperAnnotate, V7, Ximilar Data labeling and annotation Data cleaning : Albumentations, AugLy, Augmentor, autoalbument, DALI, Data Augmentation for Object Detection, Imgaug, Kornia, MXNet, Tensorflow, Transforms (PyTorch) Data transformations Data visualization : Cvedia, Neurolabs, Synthesis AI, Unity, UnrealROX Synthetic data Data quality To answer what objects are in image X and where are they? (You’ll find a brief summary and readings.) : Models RCNN SPPNet Fast RCNN and Faster RCNN Pyramid Networks / FPN Mask RCNN YOLOv1 SSD RetinaNet YOLOv3 YOLOv4 YOLOv5 DINO (and DETR) Swin Transformer V2 Vision Transformer Adapter YOLOR If you have feedback please review this link ( ) and email me at samin@reasonets.com. Looking forward to starting a conversation. Marketplace — Coming Soon | ReasoNets Next, Part 2

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70-Page Report on the COCO Dataset and Object Detection [Part 1]

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