https://github.com/michi-3000/eyeclip
Learning Transferable Visual Models From Natural Language Supervision
Papers with Code 아카이브
Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 124
- 보존된 평가 기록
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/microsoft/FairMOT
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/microsoft/Oscar
VinVL: Revisiting Visual Representations in Vision-Language Models
https://github.com/microsoft/TokenMixers
https://github.com/microsoft/UDA
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/microsoft/VisionLongformerForObjectDetection
Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding
https://github.com/microsoft/distance-learner
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/microsoft/fb.resnet.torch
https://github.com/microsoft/klite
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
https://github.com/microsoft/metric-transfer.pytorch
Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination
https://github.com/microsoft/unicl
https://github.com/middleprince/YOLOX-SS
https://github.com/midnight4ever/yolov3
https://github.com/miguelard/cloth_point_cloud_segmentation
https://github.com/miguelvr/dropblock
DropBlock: A regularization method for convolutional networks
https://github.com/mihirp1998/EmbLang
Embodied Language Grounding with 3D Visual Feature Representations
https://github.com/mikel-brostrom/Yolov3_DeepSort_Pytorch
https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch
https://github.com/mikel-brostrom/boxmot
https://github.com/mikel-brostrom/yolov7
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/mikel-brostrom/yolov7_strongsort_osnet
https://github.com/mikuhatsune/dap
https://github.com/milesial/Pytorch-UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/millanp95/OFTracker
https://github.com/milliondegree/semantic-segmentation-tensorflow
https://github.com/milongo/CASENet
https://github.com/minar09/yolov3-pytorch
https://github.com/mindlab-ai/mindcv/blob/main/examples/train_cifar_dynamic.py
https://github.com/mindlab-ai/mindcv/blob/main/mindcv/models/resnet.py
https://github.com/mindlab-ai/mindcv/blob/main/mindcv/models/shufflenetv1.py
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/mindlab-ai/mindcv/blob/main/mindcv/models/shufflenetv2.py
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
https://github.com/mindspore-ai/contrib/tree/master/application/Efficient%20ConvBN%20Blocks%20for%20Transfer%20Learning%20and%20Beyond
https://github.com/mindspore-ai/contrib/tree/master/application/dropblock-a-regularization-method-for-convolutional-networks
DropBlock: A regularization method for convolutional networks
https://github.com/mindspore-ai/models/blob/master/official/cv/retinanet/
https://github.com/mindspore-ai/models/blob/master/official/cv/shufflenetv1
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/mindspore-ai/models/blob/master/research/cv/SPPNet
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.