# Introduction This directory contains PyTorch YOLOv3 software and an iOS App developed by Ultralytics LLC, and **is freely available for redistribution under the GPL-3.0 license**. For more information please visit https://www.ultralytics.com. # Description The https://github.com/ultralytics/yolov3 repo contains inference and training code for YOLOv3 in PyTorch. The code works on Linux, MacOS and Windows. Training is done on the COCO dataset by default: https://cocodataset.org/#home. **Credit to Joseph Redmon for YOLO:** https://pjreddie.com/darknet/yolo/. # Requirements Python 3.7 or later with the following `pip3 install -U -r requirements.txt` packages: - `numpy` - `torch >= 1.1.0` - `opencv-python` - `tqdm` # Tutorials * [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) * [Transfer Learning](https://github.com/ultralytics/yolov3/wiki/Example:-Transfer-Learning) * [Train Single Image](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Image) * [Train Single Class](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class) * [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) # Jupyter Notebook Use our Jupyter [notebook](https://github.com/ultralytics/yolov3/blob/master/ultralytics_YOLOv3.ipynb) to quickly get started with training, inference and testing examples. # Training **Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`. **Resume Training:** `python3 train.py --resume` to resume training from `weights/last.pt`. Each epoch trains on 117,263 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. Default training settings produce loss plots below, with **training speed of 0.25 s/batch on a V100 GPU (almost 50 COCO epochs/day)**. Here we see training results from `coco_1img.data`, `coco_10img.data` and `coco_100img.data`, 3 example files available in the `data/` folder, which train and test on the first 1, 10 and 100 images of the coco2014 trainval dataset. `from utils import utils; utils.plot_results()`  ## Image Augmentation `datasets.py` applies random OpenCV-powered (https://opencv.org/) augmentation to the input images in accordance with the following specifications. Augmentation is applied **only** during training, not during inference. Bounding boxes are automatically tracked and updated with the images. 416 x 416 examples pictured below. Augmentation | Description --- | --- Translation | +/- 10% (vertical and horizontal) Rotation | +/- 5 degrees Shear | +/- 2 degrees (vertical and horizontal) Scale | +/- 10% Reflection | 50% probability (horizontal-only) H**S**V Saturation | +/- 50% HS**V** Intensity | +/- 50% ## Speed https://cloud.google.com/deep-learning-vm/ **Machine type:** n1-standard-8 (8 vCPUs, 30 GB memory) **CPU platform:** Intel Skylake **GPUs:** K80 ($0.20/hr), T4 ($0.35/hr), V100 ($0.80/hr) CUDA with Nvidia Apex FP16/32 **HDD:** 100 GB SSD **Dataset:** COCO train 2014 (117,263 images) GPUs | `batch_size` | batch time | epoch time | epoch cost --- |---| --- | --- | --- 1 K80 | 64 (32x2) | 2.9s | 175min | $0.58 1 T4 | 64 (32x2) | 0.8s | 49min | $0.29 1 2080ti | 64 (32x2) | - | - | - 1 V100 | 64 (32x2) | 0.38s | 23min | $0.31 2 V100 | 64 (64x1) | 0.38s | 23min | $0.62 # Inference `detect.py` runs inference on all images **and videos** in the `data/samples` folder: **YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.weights` **YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.weights` **YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights weights/yolov3-spp.weights` ## Webcam `detect.py` with `webcam=True` shows a live webcam feed. # Pretrained Weights - Darknet `*.weights` format: https://pjreddie.com/media/files/yolov3.weights - PyTorch `*.pt` format: https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI ## Darknet Conversion ```bash git clone https://github.com/ultralytics/yolov3 && cd yolov3 # convert darknet cfg/weights to pytorch model python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')" Success: converted 'weights/yolov3-spp.weights' to 'converted.pt' # convert cfg/pytorch model to darknet weights python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.pt')" Success: converted 'weights/yolov3-spp.pt' to 'converted.weights' ``` # mAP - `test.py --weights weights/yolov3.weights` tests official YOLOv3 weights. - `test.py --weights weights/last.pt` tests most recent checkpoint. - `test.py --weights weights/best.pt` tests best checkpoint. - Compare to darknet published results https://arxiv.org/abs/1804.02767. | [ultralytics/yolov3](https://github.com/ultralytics/yolov3) | [darknet](https://arxiv.org/abs/1804.02767) --- | --- | --- `YOLOv3 320` | 51.8 | 51.5 `YOLOv3 416` | 55.4 | 55.3 `YOLOv3 608` | 58.2 | 57.9 `YOLOv3-spp 320` | 52.4 | - `YOLOv3-spp 416` | 56.5 | - `YOLOv3-spp 608` | 60.7 | 60.6 ``` bash # install pycocotools git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3 cd yolov3 python3 test.py --save-json --img-size 608 Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights') Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB) Class Images Targets P R mAP F1: 100% 313/313 [07:40<00:00, 2.34s/it] all 5e+03 3.58e+04 0.117 0.788 0.595 0.199 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.367 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607 <-- Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.387 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.208 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.392 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.297 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.465 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.495 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.332 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.518 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621 python3 test.py --save-json --img-size 416 Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights') Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB) Class Images Targets P R mAP F1: 100% 313/313 [07:01<00:00, 1.41s/it] all 5e+03 3.58e+04 0.105 0.746 0.554 0.18 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.336 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.565 <-- Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.350 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.361 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.494 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.281 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.433 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.459 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.256 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.495 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.622 ``` # Citation [](https://zenodo.org/badge/latestdoi/146165888) # Contact Issues should be raised directly in the repository. For additional questions or comments please email Glenn Jocher at glenn.jocher@ultralytics.com or visit us at https://contact.ultralytics.com.