Hanoona Rasheed's Projects
Collect some papers about transformer for detection and segmentation. Awesome Detection Transformer for Computer Vision (CV)
Deformable DETR: Deformable Transformers for End-to-End Object Detection.
Detectron2 is FAIR's next-generation platform for object detection, segmentation and other visual recognition tasks.
Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".
Official implementation of the paper "DETReg: Unsupervised Pretraining with Region Priors for Object Detection".
Official repository of paper titled "EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications".
Starter app for fastai v3 model deployment on Render
Library for Deep Learning & Machine Learning, providing functions for various use cases like loading datasets, data visualization, data representation, training models, optimization and testing models.
All the assignments for capsule degree
A task-agnostic vision-language architecture as a step towards General Purpose Vision
[CVPR 2024 š„] Grounding Large Multimodal Model (GLaMM), the first-of-its-kind model capable of generating natural language responses that are seamlessly integrated with object segmentation masks.
A collection of succinct guides - Public Domain
A beginners guide to Hangar
š„š„ LLaVA++: Extending LLaVA with Phi-3 and LLaMA-3 (LLaVA LLaMA-3, LLaVA Phi-3)
Official repository of paper titled "MaPLe: Multi-modal Prompt Learning".
[NeurIPS 2022] Official repository of paper titled "Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection".
Replicates results of OWOD
Vision-language conversation in 10 languages including English, Chinese, French, Spanish, Russian, Japanese, Arabic, Hindi, Bengali and Urdu.
Helps beginners to get started with PyTorch, by giving a brief introduction to tensors, basic torch operations, and building a neural network model from scratch.
Video-ChatGPT is a video conversation model capable of generating meaningful conversation about videos. It combines the capabilities of LLMs with a pretrained visual encoder adapted for spatiotemporal video representation.
PG-Video-LLaVA: Pixel Grounding in Large Multimodal Video Models