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

Take a look at the report 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 report contains (and much more for a lot more datasets)

I’m open to suggestions, questions, and criticism — let’s start a conversation.

I have broken up the report into the following blogs:

1. 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.
2. Part 2: This part is about COCO and examples and tutorials of tools and platforms used to work with COCO (or object detection tasks).
3. 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.
4. Part 4: Models: This part is about a quick introduction to some pre-trained models and some corresponding readings.

COCO Summary Card

COCO (Common Objects in Context)

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

Benchmark dataset with evaluation metric Average Precision (AP)

Tasks: Object Detection, Panoptic Semantic Segmentation, Keypoint Detection, DensePose

Explore with tutorials: Tools and platforms used to work with COCO (or object detection tasks)

• FiftyOne, DataTorch, Know Your Data (KYD), OpenCV, OpenVINO, CVAT, Roboflow, SuperAnnotate, OpenMMLab, Coral, Amazon, Facebook, Google, Microsoft, NVIDIA, Weights and Biases.

  
  

Processing: Open-source tools and Paid platforms to perform the following steps.

• Data collection

• Data labeling and annotation: CVAT, LabelImg, Label Studio, Makesense.AI, OpenLabeling, Dataloop, Hive Data, Labelbox, Scale AI, SuperAnnotate, V7, Ximilar

• Data cleaning

• Data transformations: Albumentations, AugLy, Augmentor, autoalbument, DALI, Data Augmentation for Object Detection, Imgaug, Kornia, MXNet, Tensorflow, Transforms (PyTorch)

• Data visualization

• Synthetic data: Cvedia, Neurolabs, Synthesis AI, Unity, UnrealROX

• Data quality

  
  

Models: To answer what objects are in image X and where are they? (You’ll find a brief summary and readings.)

• 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 (Marketplace — Coming Soon | ReasoNets) and email me at samin@reasonets.com. Looking forward to starting a conversation.

Next, Part 2