Kiril Cvetkov's repositories
Semantic-Segmentation-BiSeNet
Keras BiseNet architecture implementation
Instagram-Crawler
Scraping instagram profile information
Self-Driving-Car-Behavior-Deep-Learning
End-to-End Deep Learning and Behavior cloning for Self-Driving Cars
awesome-document-understanding
A curated list of resources for Document Understanding (DU) topic
awesome-transfer
A curated list of Awesome Transfer resources
ControlNet-Style-GPT
Let us control diffusion models!
DALLE2-pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
deep-image-prior
Image restoration with neural networks but without learning.
deepmind-research
This repository contains implementations and illustrative code to accompany DeepMind publications
kubernetes-series
kubernetes series code
latent-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
llama-qrlhf
Implementation of the Llama architecture with RLHF + Q-learning
mslearn-dp100
Lab files for Azure Machine Learning exercises
PlotNeuralNet
Latex code for making neural networks diagrams
PRNet
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network (ECCV 2018)
pybadges
A Python library for creating Github-style badges
segment-anything-annotator
We developed a python UI based on labelme and segment-anything for pixel-level annotation. It support multiple masks generation by SAM(box/point prompt), efficient polygon modification and category record. We will add more features (such as incorporating CLIP-based methods for category proposal and VOS methods for video datasets
Swin-Transformer-Semantic-Segmentation
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.
THE-SPARKS-FOUNDATION
📌 This repo. Contains Basic - Advance level Machine learning / business analysis Projects. 👨💻
unilm
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
yolov9
Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information