The most popular 68 facial landmarks detection in real-world datasets of facial images captured in the wild. Reannotated versions of the LFPW, AFW, HELEN, and XM2VTS datasets along with an additional 135 images.

300-W (300 Faces in the Wild)

Provides an accessible image database that's organized relative to the WordNet hierarchy. WordNet has over 100,000 synsets (synonym rings) which is a group of words that are semantically equivalent. ImageNet provides 1,000 images to illustrate each synset. All of this totals to over 10 million cleanly sorted images with the WordNet concepts (more than just labeling it an image a word).

ImageNet

Offers visual relationship annotations indicating pairs of objects in particular relations, object properties, and human actions. All object bounding boxes are manually drawn by professional annotators and every scene has diverse and often complex scenes with multiple objects. What makes Open Images different from other object classifiers is it has relations in its labels such as "woman playing guitar", "table is wooden", etc instead of single word classifications of objects.

Open Images

Contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities. Includes annotations for 5,000 frames at high-quality pixel level and 20,000 weakly annotated frames. It is mainly used for training and benchmarking neural networks pertaining to semantic urban scene understanding.

Cityscapes

Consists of labeled subsets of tiny images (32x32 pixels) labeled in 10 different image classes. The dataset is divided into five training batches and one test batch. All the classes are mutually exclusive meaning there is no overlap in their meaning.

CIFAR-10

Consists of 1593 handwritten digits from around 80 people. Each person wrote all the digits between 0 and 9 twice and were instructed to write it normally then quickly.

Semeion Handwritten Digit Data set

Consists of Zalando's article images with 60k examples for training and 10k for testing. It was created because the MNIST dataset has become to simple for modern CNNs and it's one of the most popular datasets to benchmarks algorithms on. Fashion-MNIST seeks to provide a new benchmark.

Fashion-MNIST

Large-scale dataset of videos that include a diverse range of human focused actions. The dataset consists of around 650,000 videos covering 700 human action classes with a least 600 videos for each. The clips last around 10 seconds and are labelled with a single class.

Kinetics-700

Large scale object detection, segmentation, and captioning dataset. Abbreviation is Common Objects in COntext.

MS COCO

25K images with over 40K people with annotated body joints. All images are extracted from a YouTube video and provided with preceding and following un-annotated frames.

MPII Human Pose Dataset

In computer vision there is many established datasets used for training and benchmarking different algorithms. Unlike other machine learning areas, computer vision datasets are much more standardized because it takes significantly more effort to create them.

University of Michigan - Ann Arbor

Computer Vision

1- Rapid advances in computer vision is enabling brand new applications the were not possible a few years ago.

2- Since the community is creative, it can inspire applications in other areas.

Why computer vision is interesting?

What is the problem of using deep learning on images?

1- Image classification

2- Image localization

3- Object detection

4- Neural style transfer

Types of Computer Vision Problems

Acquired data is usually messy and can come from different sources, angles, color scales, etc. In order to use machine and deep learning techniques they need to be standardized. Usually the goal is to increase the accuracy while reducing the complexity of the model used. There is no single answer to preprocessing but there are a group of techniques commonly chosen from.

Computer Vision Pre-Processing

Computer Vision Datasets

Computer vision has tons of applications but the methods used have become relatively standardized so it's often useful to use established libraries/tools. Some examples are:
- OpenCV
- Scipy ndimage
- TensorFlow
- PyTorch Vision
- MATLAB Computer Vision Toolbox

Computer Vision Libraries

- Human computer interaction (HCI)
- Interaction design
- Collaborative and social computing
- Ubiquitous and mobile computing
- Data Visualization
- Accessibility

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