YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet ) - AlexeyAB/darknet

A complete guide to object detection using YOLO V4 and OpenCV. This collection of Google Colab-Notebooks demonstrates how to perform object detection using the YOLO V4 model. The material is seperated in two sections as listed below:

pytorch_yolo.py: it is just a basic library derived from trident , to define darknet and yolo basic block. pytorch_darknet.py: we can construction yolo v4 network and load pretrained weights here. pytorch_infer_yolo4.py: It's a demo to show how to do object detection by yolo v4 model and how trident api to make things easy.

track-yolo; 2020-11-23 - support teacher-student learning. 2020-11-17 - pytorch 1.7 compatibility. 2020-11-06 - support inference with initial weights. 2020-10-21 - fully supported by darknet. 2020-09-18 - design fine-tune methods. 2020-08-29 - support deformable kernel. 2020-08-25 - pytorch 1.6 compatibility. 2020-08-24 - support channel last ...

2024年11月6日 · Multi-camera live traffic and object counting with YOLO v4, Deep SORT, and Flask. opencv flask tracking livestream traffic yolo object-detection object-tracking traffic-monitoring real-time-analytics traffic-counter people-counter camera-stream deep-sort imagezmq yolov4 yolo-v4 traffic-counting yolov4-cloud yolov4-deepsort

2020年8月26日 · Velodyne point clouds (29 GB): input data to the Complex-YOLO model; Training labels of object data set (5 MB): input label to the Complex-YOLO model; Camera calibration matrices of object data set (16 MB): for visualization of predictions; Left color images of object data set (12 GB): for visualization of predictions

CutMix is to cover the cropped image to rectangle region of other images, and adjusts the label according to the size of the mix area. Mosaic represents a new data augmentation method that mixes 4 training images, while CutMix mixes only 2 input images. As …

├── README.md ├── dataset.py dataset ├── demo.py demo to run pytorch --> tool/darknet2pytorch ├── demo_darknet2onnx.py tool to convert into onnx --> tool/darknet2pytorch ├── demo_pytorch2onnx.py tool to convert into onnx ├── models.py model for pytorch ├── train.py train models.py ├── cfg.py cfg ...

Same logic than Yolo v4 but with only 26 layers and 2 output layers. All the steps are included in the jupyter notebooks YoloV3-tiny_tf.ipynb and YoloV3-tiny_Train_tf.ipynb The steps to create your own data for training a model are the following

YOLO v4 network architecture is comprised of three sections i.e. Backbone, Neck and Detection Head. Backbone: CSP-Darknet53(Cross-Stage-Partial Darknet53) is used as the backbone for YOLO v4 networks. This is a model with a higher input resolution (608 x 608), a larger receptive field size (725 x 725), a larger number of 3 x 3 convolutional ...