Run any open-source LLMs, such as Llama, Mistral, as OpenAI compatible API endpoint in the cloud.

CLIP as a service - Embed image and sentences, object recognition, visual reasoning, image classification and reverse image search

Resources of our survey paper "Optimizing Edge AI: A Comprehensive Survey on Data, Model, and System Strategies"

EmbeddedLLM: API server for Embedded Device Deployment. Currently support CUDA/OpenVINO/IpexLLM/DirectML/CPU

Streamlining the process for seamless execution of PyCoral in running TensorFlow Lite models on an Edge TPU USB.

Генерация описаний к изображениям с помощью различных архитектур нейронных сетей

Image Classifiers are used in the field of computer vision to identify the content of an image and it is used across a broad variety of industries, from advanced technologies like autonomous vehicles and augmented reality, to eCommerce platforms, and even in diagnostic medicine.

The primary objective of this project was to build and deploy an image classification model for Scones Unlimited, a scone-delivery-focused logistic company, using AWS SageMaker.

Example distributed system for ML model inference by using Kafka, including spring boot REST+JPA server with Java consumer program

This project is a web-based application that uses a pre-trained Mask R-CNN model to detect and classify car damage types (scratch, dent, shatter, dislocation) from images. Users can upload an image of a car, and the application will highlight damaged areas with bounding boxes and masks, providing a clear visual representation of the detected damage

Successfully established a text summarization model using Seq2Seq modeling with Luong Attention, which can give a short and concise summary of the global news headlines.

Successfully established a Seq2Seq with attention model which can perform English to Spanish language translation up to an accuracy of almost 97%.

Successfully developed a multiclass text classification model by fine-tuning pretrained DistilBERT transformer model to classify various distinct types of luxury apparels into their respective categories i.e. pants, accessories, underwear, shoes, etc.

The primary objective of this project was to build and deploy an image classification model for Scones Unlimited, a scone-delivery-focused logistic company, using AWS SageMaker.

Successfully established an image classification model using PyTorch to classify the images of several distinct natural sceneries such as mountains, glaciers, forests, seas, streets and buildings with an accuracy of 86%.

Successfully established an ANN model which can classify wine cultivators based on several characteristics of distinct wines.

Successfully fine-tuned a pretrained DistilBERT transformer model that can classify social media text data into one of 4 cyberbullying labels i.e. ethnicity/race, gender/sexual, religion and not cyberbullying with a remarkable accuracy of 99%.

Successfully developed an image classification model using PyTorch to classify the species of grapevine leaves based on their corresponding images.

POC of image classification using scikit-learn.