Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Deep Learning With Keras Notebooks | 1,868 | 5 years ago | 1 | Jupyter Notebook | ||||||
Jupyter notebooks for using & learning Keras | ||||||||||
Efficientdet | 1,306 | a year ago | 2 | apache-2.0 | Python | |||||
EfficientDet (Scalable and Efficient Object Detection) implementation in Keras and Tensorflow | ||||||||||
Yolo3 Keras | 523 | a year ago | 45 | mit | Python | |||||
这是一个yolo3-keras的源码,可以用于训练自己的模型。 | ||||||||||
Keras_cv_attention_models | 499 | 1 | 4 days ago | 94 | November 21, 2023 | 1 | mit | Python | ||
Keras beit,caformer,CMT,CoAtNet,convnext,davit,dino,efficientdet,edgenext,efficientformer,efficientnet,eva,fasternet,fastervit,fastvit,flexivit,gcvit,ghostnet,gpvit,hornet,hiera,iformer,inceptionnext,lcnet,levit,maxvit,mobilevit,moganet,nat,nfnets,pvt,swin,tinynet,tinyvit,uniformer,volo,vanillanet,yolor,yolov7,yolov8,yolox,gpt2,llama2, alias kecam | ||||||||||
Yolov4 Keras | 498 | 5 months ago | 64 | mit | Python | |||||
这是一个YoloV4-keras的源码,可以用于训练自己的模型。 | ||||||||||
Nocs_cvpr2019 | 346 | a year ago | 20 | other | Python | |||||
[CVPR2019 Oral] Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation on Python3, Tensorflow, and Keras | ||||||||||
Keras Maskrcnn | 330 | 6 | 1 | 4 years ago | 2 | June 20, 2019 | 15 | apache-2.0 | Python | |
Keras implementation of MaskRCNN object detection. | ||||||||||
Pose Residual Network | 316 | 5 years ago | 11 | Python | ||||||
Code for the Pose Residual Network introduced in 'MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network (ECCV 2018)' paper | ||||||||||
Keras Yolov4 | 309 | 3 years ago | 33 | Python | ||||||
yolov4 42.0% mAP.ppyolo 45.1% mAP. | ||||||||||
Repo 2018 | 166 | 2 years ago | Jupyter Notebook | |||||||
Deep Learning Summer School + Tensorflow + OpenCV cascade training + YOLO + COCO + CycleGAN + AWS EC2 Setup + AWS IoT Project + AWS SageMaker + AWS API Gateway + Raspberry Pi3 Ubuntu Core |
This is an implementation of EfficientDet for object detection on Keras and Tensorflow. The project is based on the official implementation google/automl, fizyr/keras-retinanet and the qubvel/efficientnet.
Thanks for their hard work. This project is released under the Apache License. Please take their licenses into consideration too when use this project.
Updates
python3 train.py --snapshot imagenet --phi {0, 1, 2, 3, 4, 5, 6} --gpu 0 --random-transform --compute-val-loss --freeze-backbone --batch-size 32 --steps 1000 pascal|coco datasets/VOC2012|datasets/coco
to start training. The init lr is 1e-3.python3 train.py --snapshot xxx.h5 --phi {0, 1, 2, 3, 4, 5, 6} --gpu 0 --random-transform --compute-val-loss --freeze-bn --batch-size 4 --steps 10000 pascal|coco datasets/VOC2012|datasets/coco
to start training when val mAP can not increase during STEP1. The init lr is 1e-4 and decays to 1e-5 when val mAP keeps dropping down.PASCAL VOC
python3 eval/common.py
to evaluate pascal model by specifying model path there.phi | 0 | 1 |
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w/o weighted | 0.8029 | |
w/ weighted | 0.7892 |
MSCOCO
python3 eval/coco.py
to evaluate coco model by specifying model path there.phi | mAP |
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0 | 0.334 weights, results |
1 | 0.393 weights, results |
2 | 0.424 weights, results |
3 | 0.454 weights, results |
4 | 0.483 weights, results |
python3 inference.py
to test your image by specifying image path and model path there.