张量流中的约洛夫_约洛夫_约洛夫_约洛米尼_Yolov5_Yolov4_ Yolov3_ Yolo_tiny in te

# YoloVx(yolov5/yolov4/yolov3/yolo_tiny) ## Tensorflow 1. Install NVIDIA driver 2. Install CUDA10.1 and cudnn7.5 3. Install Anaconda3, download [website](https://repo.anaconda.com/archive/Anaconda3-2020.07-Linux-x86_64.sh) 4. Install tensorflow, such as "sudo pip install tensorflow>=1.15 or tensorflow > 2.0" etc. ## Introduction A tensorflow implementation of YOLOv5 inspired by [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5). A tensorflow implementation of YOLOv4 inspired by [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet). Frame code from [https://github.com/YunYang1994/tensorflow-yolov3](https://github.com/YunYang1994/tensorflow-yolov3). Backbone: Darknet53; CSPDarknet53[[1]](https://arxiv.org/pdf/1911.11929.pdf), Mish[[2]](https://arxiv.org/abs/1908.08681); MobileNetV2; MobileNetV3(large and small) Neck: SPP[[3]](https://arxiv.org/abs/1406.4729), PAN[[4]](https://arxiv.org/abs/1803.01534); Head: YOLOv5/YOLOv4(Mish), YOLOv3(Leaky_ReLU)[[10]](https://arxiv.org/abs/1804.02767); Loss: DIOU CIOU[[5]](https://arxiv.org/pdf/1911.08287v1.pdf), Focal_Loss[[6]](https://arxiv.org/abs/1708.02002); Other: Label_Smoothing[[7]](https://arxiv.org/pdf/1906.02629.pdf); ## Environment Python 3.6.8 Tensorflow 1.13.1 or Tensorflow 2.0 up ## Quick Start 1. Download YOLOv5 weights from [yolov5.weights](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J). 2. Download YOLOv4 weights from [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT). 2. Convert the Darknet YOLOv4 model to a tf model. 3. Train Yolov5/Yolov4/Yolov3/Yolo_tiny. 3. Run Yolov5/Yolov4/Yolov3/Yolo_tiny detection. ### Convert weights Running from_darknet_weights_to_ckpt.py will get tf yolov4 weight file yolov4_coco.ckpt. ``` python scripts/from_darknet_weights_to_ckpt.py ``` Running ckpt2pb.py will get tf yolov4 weight file yolov4.pb. ``` python scripts/ckpt2pb.py ``` Or running from_darknet_weights_to_pb.py directly. ``` python scripts/from_darknet_weights_to_pb.py ``` ### Train In core/config.py add your own path. usage: python train.py gpu_id net_type(yolov5/yolov4/yolov3/tiny) ``` python train.py 0 yolov5 ``` ### Usage Inference ``` python test.py ``` ``` python demo.py ``` ## Reference [[1] Cross Stage Partial Network (CSPNet)](https://arxiv.org/pdf/1911.11929.pdf) [[2] A Self Regularized Non-Monotonic Neural Activation Function](https://arxiv.org/abs/1908.08681) [[3] Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://arxiv.org/abs/1406.4729) [[4] Path Aggregation Network for Instance Segmentation](https://arxiv.org/abs/1803.01534) [[5] Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://arxiv.org/pdf/1911.08287v1.pdf) [[6] Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002) [[7] When Does Label Smoothing Help?](https://arxiv.org/pdf/1906.02629.pdf) [[8] Convolutional Block Attention Module](https://arxiv.org/abs/1807.06521) [[9] YOLOv4: Optimal Speed and Accuracy of Object Detection](https://arxiv.org/abs/2004.10934) [[10] YOLOv3: An Incremental Improvement](https://arxiv.org/abs/1804.02767) [[11] Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431) ### Acknowledgment keras_yolov3 [https://github.com/qqwweee/keras-yolo3](https://github.com/qqwweee/keras-yolo3). keras_yolov4 [https://github.com/Ma-Dan/keras-yolo4](https://github.com/Ma-Dan/keras-yolo4).