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Jul 26, 2021 - Python
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coco
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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
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Oct 26, 2019 - Python
Mask RCNN in TensorFlow
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Jan 5, 2021 - Python
Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch
demo
computer-vision
detection
pytorch
nms
coco
object-detection
pascal-voc
multibox
focalloss
efficientnet
efficientdet-d0
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Jul 5, 2021 - Python
machine-learning
computer-vision
deep-learning
image-annotation
label
detection
coco
datasets
image-segmentation
image-labeling
annotate-images
coco-format
coco-annotator
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Jun 30, 2021 - Vue
Visual Question Answering in Pytorch
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Dec 11, 2019 - Python
Helper functions to create COCO datasets
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Mar 22, 2021 - Python
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)
pytorch
coco
eval
ccnet
cityscapes
mobilenet
xception
deeplabv3plus
deeplab-v3-plus
fast-scnn
hrnet
pointrend
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Nov 6, 2020 - Python
Computer vision based ML training data generation tool 🚀
machine-learning
image
computer-vision
annotation
tensorflow
model
ml
vision
vgg
yolo
coco
labelling
object-detection
create
annotation-tool
training-data
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Jun 25, 2021 - JavaScript
FoveaBox: Beyond Anchor-based Object Detector
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Jul 17, 2020 - Python
Implementation of various human pose estimation models in pytorch on multiple datasets (MPII & COCO) along with pretrained models
deep-learning
pytorch
coco
human-pose-estimation
pretrained-models
pose-estimation
prm
mpii
stacked-hourglass-networks
keypoints-detector
hourglass-network
pytorch-implmention
coco-dataset
deeppose
chained-prediction
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Jul 29, 2019 - Python
PyTorch-based modular, configuration-driven framework for knowledge distillation. 🏆 19 methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.
nlp
natural-language-processing
glue
pytorch
transformer
imagenet
image-classification
coco
object-detection
semantic-segmentation
knowledge-distillation
cifar10
pascal-voc
cifar100
google-colab
colab-notebook
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Jul 26, 2021 - Python
various cv tools, such as label tools, data augmentation, label conversion, etc.
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Jul 7, 2021 - Jupyter Notebook
Object detection on multiple datasets with an automatically learned unified label space.
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Mar 25, 2021 - Python
The official homepage of the (outdated) COCO-Stuff 10K dataset.
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Mar 24, 2018 - MATLAB
[CVPR2021 Oral] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
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Jul 19, 2021 - Python
Deformable Convolutional Networks + MST + Soft-NMS
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Dec 2, 2017 - Python
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection.
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Sep 6, 2020 - Jupyter Notebook
Image Captions Generation with Spatial and Channel-wise Attention
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Apr 4, 2018 - Python
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
raspberry-pi
opencv
iot
computer-vision
tensorflow
keras
coco
aws-ec2
ec2-instance
aws-iot
opencv-library
generative-adversarial-networks
raspberry-pi-3
opencv3
cyclegan
haarcascade
sagemaker
yolo-model
ubuntucore
aws-sagemaker
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Feb 3, 2021 - Jupyter Notebook
This is a tensorflow re-implementation of Faster R-CNN: Towards Real-Time ObjectDetection with Region Proposal Networks.
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Jan 8, 2020 - Jupyter Notebook
Open
yolov4 support
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Fetulhak
commented
Jun 17, 2021
@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.
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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