Generalized-YOLOv5 is a modified version of YOLOv5. Two main contributions have been made to Generalized-YOLOv5. First, an extension to train on non-natural images and second a cross-validation extension. ### Non-Natural image extension The non-natural image extension enables YOLOv5 to handle images with arbitrary intensity scales, so it can be trained on 2D non-natural images. Furthermore, this extension also includes preprocessing scripts to convert non-natural image datasets to a natural intensity scale. Training on natural images is faster as OpenCV, which is highly optimized on natural images, can be used during training for preprocessing and augmentation. However, this can lead in some scenarios to a decreased performance as the intensity scale is often reduced to a fraction of its original scale. The slow-down on non-natural images can be somewhat mitigated by increasing worker threads (until they block each other), but one should expect an increased training time of 1.5x. ### N-fold cross-validation The cross-validation extension is a dataset preprocessing script that processes a dataset into N folds with corresponding dataset configuration files that are understood by YOLOv5. On each fold a YOLOv5 model can be trained and YOLOv5's built-in ensembling method can be used to run inference with the models of all folds. The built-in ensembling method itself has no cross-validation functionality and was only designed for ad-hoc ensembling of YOLOv5 models of different scales, thus the preprocessing script is required for cross-validation. ##

See the documentation and usage of the Generalized-YOLOv5 in the Generalized-YOLOv5 README. The official YOLOv5 documentation can be found in the YOLOv5 README.

Quick Start Examples

Install

Clone repo and install [requirements.txt](https://github.com/MIC-DKFZ/generalized_yolov5/blob/master/requirements.txt) in a [**Python>=3.7.0**](https://www.python.org/) environment, including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). ```bash git clone https://github.com/MIC-DKFZ/generalized_yolov5 # clone cd yolov5 pip install -r requirements.txt # install ```

Inference

YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). ```python import torch # Model model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom # Images img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list # Inference results = model(img) # Results results.print() # or .show(), .save(), .crop(), .pandas(), etc. ```

Inference with detect.py

`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. ```bash python detect.py --source 0 # webcam img.jpg # image vid.mp4 # video path/ # directory 'path/*.jpg' # glob 'https://youtu.be/Zgi9g1ksQHc' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ```

Training

The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB. ```bash python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128 yolov5s 64 yolov5m 40 yolov5l 24 yolov5x 16 ```

Contact

For Generalized-YOLOv5 bugs and feature requests please visit GitHub Issues. For business inquiries or professional support requests please visit https://helmholtz-imaging.de/contact.