0x0f0f0f0f

0x0f0f0f0f's Stars

• ultralytics/yolov5

  YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

  Language:Python48.3k3649.2k15.9k

• z1069614715/objectdetection_script

  一些关于目标检测的脚本的改进思路代码，详细请看readme.md

  Language:Python4.7k2047434

• UX-Decoder/Segment-Everything-Everywhere-All-At-Once

  [NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"

  Language:Python4.2k58141353

• lucasjinreal/yolov7_d2

  🔥🔥🔥🔥 (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥

  Language:Python3.1k42157484

• iscyy/yoloair

  🔥🔥🔥 专注于YOLOv5，YOLOv7、YOLOv8、YOLOv9改进模型，Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀

  Language:Python2.4k26149425

• salesforce/ALBEF

  Code for ALBEF: a new vision-language pre-training method

  Language:Python1.4k11139193

• foolwood/DaSiamRPN

  [ECCV2018] Distractor-aware Siamese Networks for Visual Object Tracking

  Language:Python1.3k560364

• BangguWu/ECANet

  Code for ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

  Language:Python1.2k1568197

• researchmm/SiamDW

  [CVPR'19 Oral] Deeper and Wider Siamese Networks for Real-Time Visual Tracking

  Language:Python74724117179

• microsoft/RegionCLIP

  [CVPR 2022] Official code for "RegionCLIP: Region-based Language-Image Pretraining"

  Language:Python669109748

• mlfoundations/wise-ft

  Robust fine-tuning of zero-shot models

  Language:Python59262562

• foolwood/pytorch-slimming

  Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.

  Language:Python565111695

• monatis/clip.cpp

  CLIP inference in plain C/C++ with no extra dependencies

  Language:C415174624

• Unity-Technologies/SynthDet

  SynthDet - An end-to-end object detection pipeline using synthetic data

  Language:C#352181354

• Davonter/embedded-doc

  嵌入式软件开发文档整理

  2515090

• YOLOonMe/EMA-attention-module

  Implementation Code for the ICCASSP 2023 paper " Efficient Multi-Scale Attention Module with Cross-Spatial Learning" and is available at: https://arxiv.org/abs/2305.13563v2

  1454118

• bubbliiiing/clip-pytorch

  这是一个clip-pytorch的模型，可以训练自己的数据集。

  Language:Python14212021

• shonenkov/CLIP-ODS

  CLIP Object Detection, search object on image using natural language #Zeroshot #Unsupervised #CLIP #ODS

  Language:Python1313514

• salman-h-khan/PL-ZSD_Release

  This repository provides training and evaluation code for paper titled "Polar Loss for Zero-Shot Object Detection." (Arxiv version) and "Improved Visual-Semantic Alignment for Zero-Shot Object Detection" (accepted in AAAI 2020)

  Language:Python11962829

• jianjieluo/OpenAI-CLIP-Feature

  An easy to use, user-friendly and efficient code for extracting OpenAI CLIP (Global/Grid) features from image and text respectively.

  Language:Python99226

• xiaofeng94/VL-PLM

  Exploiting unlabeled data with vision and language models for object detection, ECCV 2022

  Language:Python852177

• HPL123/RRFS

  Language:Python84155

• Hi-FT/ERD

  Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation

  Language:Python7821610

• llrtt/Zero-Shot-Detection-via-Vision-and-Language-Knowledge-Distillation

  Language:Python758

• Went-Liang/UnSniffer

  [CVPR 2023] Official Pytorch code for Unknown Sniffer for Object Detection: Don’t Turn a Blind Eye to Unknown Objects

  Language:Python683239

• FanZhichen/Awesome-Incremental-Few-Shot-Object-Detection

  A paper list for incremental few-shot object detection.

  24302

• pengkaizhu/zsd_dataset

  Zero Shot Detection Dataset

  Language:Python22627

• witnessai/Awesome-Zero-Shot-Object-Detection

  A curated list of papers, datasets and resources pertaining to zero-shot object detection.

  22300

• aclearboy7/-Structured-Model-Pruning

  This project prunes the convolution neural network through the scaling factor γ of the BN layer, which is a typical structured pruning work. It is my own reproduction and modification of the paper, the effect is very good. （本项目通过BN层的缩放因子对神经网络进行剪枝，是自己对于Network Slimming论文的复现和修改，效果很好，是一个典型的结构化剪枝工作）

  Language:Python50

• xiapeng1110/finetune_clip

  Fine-tuning the pre-trained model CLIP trained by OpenAI on your custom dataset.

  Language:Python40