JumpServer 是全球首款开源的堡垒机,是符合 4A 的专业运维安全审计系统。
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Updated
Sep 18, 2021 - Python
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JumpServer 是全球首款开源的堡垒机,是符合 4A 的专业运维安全审计系统。
Tensorflow Faster RCNN for Object Detection
Mask RCNN in TensorFlow
Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch
Visual Question Answering in Pytorch
Helper functions to create COCO datasets
Support PointRend, Fast_SCNN, HRNet, Deeplabv3_plus(xception, resnet, mobilenet), ContextNet, FPENet, DABNet, EdaNet, ENet, Espnetv2, RefineNet, UNet, DANet, HRNet, DFANet, HardNet, LedNet, OCNet, EncNet, DuNet, CGNet, CCNet, BiSeNet, PSPNet, ICNet, FCN, deeplab)
Computer vision based ML training data generation tool
PyTorch-based modular, configuration-driven framework for knowledge distillation.
FoveaBox: Beyond Anchor-based Object Detector
Implementation of various human pose estimation models in pytorch on multiple datasets (MPII & COCO) along with pretrained models
various cv tools, such as label tools, data augmentation, label conversion, etc.
[CVPR2021 Oral] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
Object detection on multiple datasets with an automatically learned unified label space.
The official homepage of the (outdated) COCO-Stuff 10K dataset.
Deformable Convolutional Networks + MST + Soft-NMS
@fcakyon I have trained and YOLOV4 model using AlexyAB's repo and I have generated a weight file for my custom dataset which was sliced using your repo. Now I want to check the test results for predicted YOLOV4 results. How can I integrate YOLOV4 weight file to your test folder so that I can use the YOLOV4 like you have used the YOLOV5 model which was from from ultralytics repo.
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection.
Image Captions Generation with Spatial and Channel-wise Attention
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
Hey,
do you know if somebody is already working on support for Mapilary and if no, could somebody give me a quick start what I need to know to contribute this format?
Thanks
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I found a new tool makesense who is trying to do the same thing that you're already doing. Probably with some new thoughts in mind. I recently asked the author how that tool is doing differently: SkalskiP/make-sense#23
The author responded that we need multiple clicks while labeling in imglab. Though I didn't understand it well as I can control most of the things with