tommyngx

tommyngx's Stars

• markoleptic/SpeedyRecs

  Speedy Recs is an anime recommender system devolped in RStudio using UBCF, IBCF, SVD, ALS, and a hybrid model.

  Language:R11

• icey-zhang/SuperYOLO

  SuperYOLO is accepted by TGRS

  Language:Python30751

• jonbarron/website

  Language:HTML2.5k2k

• NVlabs/FasterViT

  [ICLR 2024] Official PyTorch implementation of FasterViT: Fast Vision Transformers with Hierarchical Attention

  Language:Python76662

• THU-MIG/yolov10

  YOLOv10: Real-Time End-to-End Object Detection [NeurIPS 2024]

  Language:Python9.5k906

• PouriaRouzrokh/RadRotator

  This repository contains the GitHub Pages for RadRotator.

  Language:JavaScript11

• jianglindong93/AUTOSurv

  Language:R115

• linafaik08/survival_analysis

  Language:Jupyter Notebook129

• GKantidakis/SM-vs-ML-for-CRs

  This repository stores the R-code of a research project that compared statistical models (SM) with machine learning (ML) techniques for competing risks in private extremity soft-tissue sarcoma (eSTS) data, and a comprehensive example in publicly available R data for Follicular Cell Lymphoma (data "follic"). The aim was to develop and validate prognostic clinical prediction models for competing risks (CRs) with small/medium sample size and in a low-dimensional setting. Two SM (cause-specific Cox, Fine-Gray) and three ML techniques (PLANNCR original, PLANNCR extended, RSFCR) were compared in terms of discrimination and calibration. -Notes- PLANNCR, Partial Logistic Artificial Neural Network for Competing Risks; RSFCR, Random Survival Forests for Competing Risks.

  Language:R31

• krashr-ds/framingham-ms

  Framingham data progressive unidirectional multi-state model using four states: No disease, Hypertension, Cardiovascular disease, and Death (absorbing).

  Language:HTML21

• buutrg/PRSmix

  Language:R113

• WongKinYiu/yolov9

  Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

  Language:Python8.9k1.4k

• sovit-123/vision_transformers

  Vision Transformers for image classification, image segmentation, and object detection.

  Language:Python407

• obss/sahi

  Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

  Language:Python4k579

• fcakyon/small-object-detection-benchmark

  icip2022 paper: sahi benchmark on visdrone and xview datasets using fcos, vfnet and tood detectors

  Language:Python15723

• thieu1995/permetrics

  Artificial intelligence (AI, ML, DL) performance metrics implemented in Python

  Language:Python6817

• ultralytics/yolov5

  YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

  Language:Python49.9k16.1k

• Sense-X/Co-DETR

  [ICCV 2023] DETRs with Collaborative Hybrid Assignments Training

  Language:Python980107

• DataXujing/detr_transformer

  transformer used in object detection [DETR训练自己的数据集]

  Language:Python4610

• NielsRogge/Transformers-Tutorials

  This repository contains demos I made with the Transformers library by HuggingFace.

  Language:Jupyter Notebook9.1k1.4k

• csm-kr/detr_pytorch

  🌼 re-implementation of DETR (DEtectionTRansformer) (ECCV2020)

  Language:Python7

• Code-keys/yolov5-6.0-RetinaNet-MultiLabel

  yolov5+doublehead + MultiLabel+detection

  Language:Python246

• Rapisurazurite/Sparse_rcnn

  Sparse-RCNN inplementation

  Language:Python8

• Alperenclk/MULTI-VIEW-BREAST-CANCER-CLASSIFICATION

  Language:Jupyter Notebook1

• dangnh0611/kaggle_rsna_breast_cancer

  1st place solution of RSNA Screening Mammography Breast Cancer Detection competition on Kaggle: https://www.kaggle.com/competitions/rsna-breast-cancer-detection

  Language:Python7525

• xingbpshen/nested-diffusion

  The official implementation of the paper "Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles".

  Language:Python41

• huggingface/pytorch-image-models

  The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

  Language:Python31.6k4.7k

• sovit-123/fasterrcnn-pytorch-training-pipeline

  PyTorch Faster R-CNN Object Detection on Custom Dataset

  Language:Jupyter Notebook20868

• IDEA-Research/detrex

  detrex is a research platform for DETR-based object detection, segmentation, pose estimation and other visual recognition tasks.

  Language:Python2k206

• machingclee/2022-09-20-Swin-Transformer-backbone-Cascade-RCNN

  Modification of my Faster RCNN with FPN architecture with SwinTransformer as backbone in place of ResNet50.

  Language:Python42