Naive Bees Deep Learning with Images
Can a machine identify a bee as a honey bee or a bumble bee? These bees have different behaviors and appearances, but given the variety of backgrounds, positions, and image resolutions, it can be a challenge for machines to tell them apart.
Being able to identify bee species from images is a task that ultimately would allow researchers to more quickly and effectively collect field data. Pollinating bees have critical roles in both ecology and agriculture, and diseases like colony collapse disorder threaten these species. Identifying different species of bees in the wild means that we can better understand the prevalence and growth of these important insects.
This notebook walks through building a simple deep learning model that can automatically detect honey bees and bumble bees and then loads a pre-trained model for evaluation.
ASL Recognition
American Sign Language (ASL) is the primary language used by many deaf individuals in North America, and it is also used by hard-of-hearing and hearing individuals. The language is as rich as spoken languages and employs signs made with the hand, along with facial gestures and bodily postures.
A lot of recent progress has been made towards developing computer vision systems that translate sign language to spoken language. This technology often relies on complex neural network architectures that can detect subtle patterns in streaming video. However, as a first step, towards understanding how to build a translation system, we can reduce the size of the problem by translating individual letters, instead of sentences.
In this notebook, we will train a convolutional neural network to classify images of American Sign Language (ASL) letters. After loading, examining, and preprocessing the data, we will train the network and test its performance.
Eye Gender Prediction
The anthropometric analysis of the human face is an essential study for performing craniofacial plastic and reconstructive surgeries. Facial anthropometrics are affected by various factors such as age, gender, ethnicity, socioeconomic status, environment, and region.
Plastic surgeons who undertake the repair and reconstruction of facial deformities find the anatomical dimensions of the facial structures useful for their surgeries. These dimensions are a result of the Physical or Facial appearance of an individual. Along with factors like culture, personality, ethnic background, age; eye appearance and symmetry contributes majorly to the facial appearance or aesthetics.
Our objective is to build a model to scan the image of an eye of a patient and find if the gender of the patient is male or female.
