Papers with Code 아카이브

2D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/albertopolito/CarSNN

Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks

https://github.com/albertotestoni/ndq_visual_objects

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

https://github.com/albertsokol/pneumothorax-detection-unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/albertsokol/yolov3-tf2

YOLOv3: An Incremental Improvement

https://github.com/albumentations-team/albumentations

Random Erasing Data Augmentation

https://github.com/alcaster/Binary-yolo

YOLO9000: Better, Faster, Stronger

https://github.com/alcunha/iwildcam2021ufam

Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax

https://github.com/aldipiroli/semantic_spray_dataset

SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface Conditions

https://github.com/aldipiroli/yolo-v1

You Only Look Once: Unified, Real-Time Object Detection

https://github.com/alecwangcq/KFAC-Pytorch

Deep Residual Learning for Image Recognition

https://github.com/alegonz/kdsb17

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

https://github.com/alejandrodebus/SegNet

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/aleksispi/drl-rpn-tf

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/alessandrozamberletti/docdetect

Holistically-Nested Edge Detection

https://github.com/alessiomora/unlearning_fl

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/alex-f1tor/Image-Caption

Deep Residual Learning for Image Recognition

https://github.com/alexa/wqa-cascade-transformers

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/alexalm4190/Mask_RCNN-Vizzy_Hand

Mask R-CNN

https://github.com/alexander-g/inbd

Automatic Wood Pith Detector: Local Orientation Estimation and Robust Accumulation

https://github.com/alexander-pv/maskrcnn_tf2

Mask R-CNN

https://github.com/alexanderrubinstein/occam

ImageNet Large Scale Visual Recognition Challenge

https://github.com/alexandrelewin/FollowMe

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/alexattia/simpsonrecognition

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/alexeyab/yolor

You Only Learn One Representation: Unified Network for Multiple Tasks

https://github.com/alexgkendall/SegNet-Tutorial

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/alexgkendall/caffe-segnet

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/alexionby/Endoscopy.ai

LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation

https://github.com/alexisbdr/warehouse-anomaly

Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

https://github.com/alexismailov2/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/alexklibisz/deep-calcium

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/alexmehta/nac-tcn-tcns-with-causal-na

Dilated Neighborhood Attention Transformer

https://github.com/alexromano/nerve-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/alexssanchez/unet-app-pucp

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/alexwdong/IncubatorCVProject

Deep Residual Learning for Image Recognition

https://github.com/alexwitt2399/rexnet

Rethinking Channel Dimensions for Efficient Model Design

https://github.com/alia21/VechicleDetection-and-Traccking

You Only Look Once: Unified, Real-Time Object Detection

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.