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https://github.com/david8862/keras-yolov3-model-set

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Related CK components (help)
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 lib   Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
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 lib   Weighted Boxes Fusion: ensembling boxes for object detection models
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 lib   YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection
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 lib   YOLOv4: Optimal Speed and Accuracy of Object Detection
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 package   Artifact from https://github.com/david8862/keras-YOLOv3-model-set
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 package   Tags: tensorflow
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 package   Tags: tf
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 program   Artifact pipeline from https://github.com/david8862/keras-YOLOv3-model-set
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 program   Tags: tensorflow
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 program   Tags: tf
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 result   SOTA: CIFAR-10 (image classification) [computer vision] in the open CK format
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 result   SOTA: CIFAR-10, 250 Labels (semi-supervised image classification) [computer vision] in the open CK format
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 result   SOTA: CIFAR-100 (image classification) [computer vision] in the open CK format
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 result   SOTA: COCO (real-time object detection) [computer vision] in the open CK format
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 result   SOTA: COCO test-dev (object detection) [computer vision] in the open CK format
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 result   SOTA: Caltech-101 (fine-grained image classification) [computer vision] in the open CK format
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 result   SOTA: FGVC Aircraft (fine-grained image classification) [computer vision] in the open CK format
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 result   SOTA: ImageNet-A (domain generalization) [methodology] in the open CK format
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 result   SOTA: Kuzushiji-MNIST (image classification) [computer vision] in the open CK format
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 result   SOTA: Oxford 102 Flowers (fine-grained image classification) [computer vision] in the open CK format
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 result   SOTA: Oxford-IIIT Pets (fine-grained image classification) [computer vision] in the open CK format
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 result   SOTA: SVHN (image classification) [computer vision] in the open CK format
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 result   SOTA: SVHN, 250 Labels (semi-supervised image classification) [computer vision] in the open CK format
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 result   SOTA: Stanford Cars (fine-grained image classification) [computer vision] in the open CK format
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 soft   Artifact from https://github.com/david8862/keras-YOLOv3-model-set
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Related research papers, code, artifacts and results
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https://arxiv.org/abs/1910.01271v1
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https://arxiv.org/abs/1910.13302v2
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https://arxiv.org/abs/1911.08287v1
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https://arxiv.org/abs/2004.10934v1
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https://arxiv.org/pdf/1910.01271v1.pdf
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https://arxiv.org/pdf/1910.13302v2.pdf
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https://arxiv.org/pdf/1911.08287v1.pdf
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https://arxiv.org/pdf/2004.10934v1.pdf
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https://github.com/AIForMobility/models
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https://github.com/CertaintyLab/darknet
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https://github.com/Dodant/ANPR-with-Yolov4
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https://github.com/Jonashellevang/Weapons_Detection_YOLOv3
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https://github.com/LdDl/go-darknet
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https://github.com/LdDl/odam
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https://github.com/LiaoSteve/Drone-GCS-and-AI
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https://github.com/Lornatang/YOLOv4-PyTorch
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https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-4
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https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-64-bit
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https://github.com/SpanoChristian/darknet
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https://github.com/SravanChittupalli/COVID-19-Mask_detector
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https://github.com/VCasecnikovs/Yet-Another-YOLOv4-Pytorch
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https://github.com/ZFTurbo/Weighted-Boxes-Fusion
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https://github.com/amusi/YOLO-Reproduce-Summary
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https://github.com/david8862/keras-yolov3-model-set
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https://github.com/hunglc007/tensorflow-yolov4-tflite
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https://github.com/issaiass/FacialMaskDetector
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https://github.com/lingtengqiu/Yolo_Nano
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https://github.com/maudzung/Complex-YOLOv4-Pytorch
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https://github.com/maudzung/YOLO3D-YOLOv4-PyTorch
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https://github.com/michhar/azureml-keras-yolov3-custom
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https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch
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https://github.com/miki998/YoloV4_PyTorch
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https://github.com/ojkk371/Yolo_v4
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https://github.com/otamajakusi/darknet-yolov4
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https://github.com/pixiedust18/darknet_mask_classification
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https://github.com/rechardchen123/YOLOv4_traffic_sign_detection
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https://github.com/robbebluecp/tf2-yolov4
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https://github.com/xungeer29/yolov4.pytorch
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https://github.com/youchangxin/YOLOv4_tensorflow2
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https://github.com/zzj403/attack-pytorch-YOLOv4-1
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https://paperswithcode.com/paper/distance-iou-loss-faster-and-better-learning
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https://paperswithcode.com/paper/weighted-boxes-fusion-ensembling-boxes-for
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https://paperswithcode.com/paper/yolo-nano-a-highly-compact-you-only-look-once
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https://paperswithcode.com/paper/yolov4-optimal-speed-and-accuracy-of-object
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