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I'm an AI researcher with a passion centered on Machine Learning and Computer Vision

  • 💻 I am presently employed as a Machine Learning Engineer, focusing on cutting-edge algorithms and innovative data-driven solutions
  • 📝 Actively studying and conducting in-depth research in the fields of Machine Learning and Computer Vision
  • 🤖 Deeply engaged in research spanning reinforcement learning, advanced biomedical applications, state-of-the-art robotics, generative AI techniques, and the intricacies of LLM

Connect :


LahiRumesh | LinkedIn LahiRumesh | Medium LahiRumesh | Twitter



Tools and Technology :

Python R C++ PyTorch TensorFlow Keras scikit-learn NumPy Pandas nVIDIA

📕 Medium Posts


Top Langs

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Lahiru Rumesh Rathnayake's Projects

detectron2 icon detectron2

Detectron2 is FAIR's next-generation platform for object detection and segmentation.

detr icon detr

End-to-End Object Detection with Transformers

kmeans-anchor-boxes icon kmeans-anchor-boxes

k-means clustering with the Intersection over Union (IoU) metric as described in the YOLO9000 paper

llm icon llm

An ecosystem of Rust libraries for working with large language models

models icon models

A collection of pre-trained, state-of-the-art models in the ONNX format

simple_cnn icon simple_cnn

Simple CNN is a library that can be used to train and infer CNN models by use of PyTorch and ONNX.

tensorflow-yolov4-tflite icon tensorflow-yolov4-tflite

YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite

vision-transformer icon vision-transformer

Tensorflow implementation of the Vision Transformer (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)

voc2coco icon voc2coco

How to create custom COCO data set for object detection

yolact icon yolact

A simple, fully convolutional model for real-time instance segmentation.

yolor icon yolor

implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)

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