Collection of papers, datasets, code and other resources for object tracking and detection using deep learning
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Updated
May 13, 2024 - HTML
Collection of papers, datasets, code and other resources for object tracking and detection using deep learning
[ICLR 2023] "More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity"; [ICML 2023] "Are Large Kernels Better Teachers than Transformers for ConvNets?"
a cutting-edge cell segmentation model specifically designed for single-molecule resolved spatial omics datasets. It addresses the challenge of accurately segmenting individual cells in complex imaging datasets, leveraging a unique approach based on graph neural networks (GNNs).
VGG Image Annotator - 1.0.5 Version
Assignment codes for CS736 Algorithms for Medical Image Processing.
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
Use pre-trained PSPNet Segmentation Model to generate a mask image consisting of sky pixels marked in white color in the image and other pixels marked in black color
data visualization, customer segmentation, CLV and next purchase prediction
Webapp em Angular 4 - Brasal Corretora
This study consists of a comparative analysis of various image segmentation methods on cytological images
🔥 🔥 🔥 Simple, open, pure HTML image annotation tool for bounding boxes and points (detection / segmentation). Try it now! 🔥 🔥 🔥
Código fuente del proyecto de materia integradora Detección de lesiones cerebrales mediante análisis de imagenes MRI(Magnetic Resonance Imaging) mediante el uso de deep learning.
An ML model which uses Gaussian Mixture Model clustering to classify customers.
Materials from conference workshop
Projeto de segmentação de clientes com posterior construção de dashboard no Power BI.
data, metadata, tools, and LDA experiments on a corpus of Sanskrit philosophy texts
FUSE toolkit supports fluorescent cell image alignment and analysis.
2D/ 3D object detection, segmentation, depth estimation for self-driving car
This is a computer vision project for solving the problem of lane detection in autonomous driving vehicles. The project uses simple thresholding based techniques in L*a*b color space. Programming has been done in C++ using OpenCV library.
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