https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/vgg.py
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
Activity Recognition In Videos
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 1
- 보존된 평가 기록
- 1
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mjhucla/TF-mRNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mjhucla/mRNN-CR
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mjoannou/audiovisual-moments-in-time
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mnecipkurt/tsg19
Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach
https://github.com/moabitcoin/ig65m-pytorch
Large-scale weakly-supervised pre-training for video action recognition
https://github.com/modelhub-ai/vgg-19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/modestyachts/ImageNetV2
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mohamedameen93/German-Traffic-Sign-Classification-Using-TensorFlow
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mohsenzand/reschunk
Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction
https://github.com/motional/nuplan-devkit
NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
https://github.com/mr-bulb/DL_basic_github
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mryoo/pooled_time_series
https://github.com/musifahamran/FYP
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nagadomi/kaggle-cifar10-torch7
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nageshpindi/human-motion-prediction-master
https://github.com/neerajvashistha/vgg16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ngonthier/multiresolution_texture
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nicaogr/multiresolution_texture
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nicku-a/Weather_Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nicolalandro/Padam
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nidolow/image-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nikankind/Reproduce-Article-Representation-Flow-for-Action-Recognition-with-PaddlePaddle
https://github.com/nikeke19/safe-mult-rl
Motion Transformer with Global Intention Localization and Local Movement Refinement
https://github.com/nikolaevs92/DL-articles-implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/niranjana98/Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nmhaddad/python-machine-learning-youtube
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nsom/Basic-Models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nsom/VGG16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/oellop/facial_expression
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/okeer/nst-tele-bot
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/open-mmlab/mmaction2
Large-scale weakly-supervised pre-training for video action recognition
https://github.com/open-mmlab/mmclassification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/open-mmlab/mmpose
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/open3da/ll3da
https://github.com/openmeshlab/meshxl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.