FIRE Capital One Machine Learning at the University of Maryland
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2022-t1-convolutional
PublicWe have created a song recommendation system based on user history. Our product takes in a user’s playlist(s) and recommends songs based on the playlist(s). The product uses the Spotify API to extract the features of a song (11 features in total), these range from danceability to tempo to instrumentalness. Using an aggregation function, the featureumd-fire-coml.github.io
Public- User Uploads Soundtrack file. Uploaded track is run through Librosa functions to extract music features. Feature data is run through our Vector Quantization model to find the closest soundtrack in the dataset. Display Name of most "similar" soundtrack.
example-scrum-project
Public2022-t2-transformer
Public- Audio generation when given a genre as natural language input. User inputs a genre tag into frontend. This tag is passed to the semantic similarity NLP model to determine the nearest tag within training the space, and implicitly coerces to (outputs) the found tag. This tag is passed to the audio generation model as input, which produces generated
2022-t5-deep-q-learning
PublicThis project trains a DQN model to play a variety of Atari Games, including Q*bert. It includes a random agent, which generates gameplay based on the machine making random actions, as well as a trained model that attempts to make desired actions to win the game. Reinforcement learning is an area of machine learning that is focused on training agent2021-Image-Colorization
PublicThis image colorization model takes an input image, convert it to greyscale, then creates a realistic colorization of the image based off of the trained model. The model architecture utilizes the YCbCr colorspace in order to colorize the image, because Y is equivalent to grayscale, so the model has to predict only the Cb and Cr channels. The mod…Rainbow
Public2021-Speaker-Recognition
PublicThe purpose of this project is to assess whether a given audio recording has a male or female speaker, which is decided through the recording's frequency. The application of this model can be used to decide if a user is male or female, this could enable call centers to know the gender of the speaker and with that information better sell their pr…2021-3D-Object-Detection
PublicThis product identifies and labels 3D Objects in images of every day settings, such as cars, trees, bikes, pedestrians, etc. This product makes use of a UNet, which is a Convolutional Neural Network, to identify objects, given voxel data. Our product first takes point cloud data from the SemanticKITTI dataset, and converts it to voxels. For the …- The product predicts the face and gender of a person. The use of age and gender recognition has a myriad of applications including, security and the advertising industry. Machine learning facial age and gender recognition uses a model trained with a dataset of images to predict one's age and gender. Our model uses a series of convolutional layer…
- Malware detection is an important process in modern computing to help protect various systems from getting infected. The goal for any project, program, or system that aims to detect malware is to prevent any malicious software from running on a user’s computer. With our project, we have aimed to assist in the battle against malicious software by…
2021-Game-Playing
PublicOur model learns to play any level of Super Mario Bros. Its architecture is based off of the DQN research paper's model architecture. More specifically; however, this architecture connects a reinforcement model to a deep neural network. For our RGB input for our model, we took a 256x240 pixel screen capture from our Super Mario Bros emulator and…- When an RGB image is inputted to the model, it produces a depth map that displays the predicted depth of each pixel. It is similar to that of a person's ability to percieve perspective, distinguishing what is far away and what is nearby. It does this by evaluating the darkness of each pixel; something closer is generally lighter and something fu…
2021-Music-Generation
PublicOur project was to utilize an input of MIDI files to generate new music. We utilized a type of recurrent neural network called an LTSM model, which stands for Long Short-Term Memory network. Essentially, this type of model can efficiently learn and recognize long-term patterns. This sort of recognition is incredibly useful with music generation.- We take a low resolution image as input. The image passes through a series of layers that extract features from the image. At the very end, the image with its extracted features is put into a special function called depth to space, which turns all those features into actual pixles. After this, we have our super-resolved image.
example-automated-tests
Publicgym-duckietown
Publicspring2021san
Publicdetr
Public- This repo detects rotated and cluttered objects in aerial images. This can then be used to detect thing like traffic on satellite maps or for disaster relief. The model itself is a convoultional neural network using several groups of convolutional/deconvolutional and maxpooling layers. We use rotation augmentation to further account for the vari…
2020-3D-Object-Detection
PublicThe purpose of this research is to do 3D object detection from a photo using machine learning. The goal is a working model that can detect multiple 3D objects and provide their dimensions and orientation given a photo. There are many ways to implement this, but this project uses center-based 3D object detection and tracking, where the model pred…