## Introduction 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 all `requirements.txt` dependencies installed, including `torch >= 1.5`. To install run: ```bash $ pip install -U -r requirements.txt ``` ## Tutorials * [Notebook](https://github.com/ultralytics/yolov3/blob/master/tutorial.ipynb)

* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) << highly recommended * [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) * [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker) * [A TensorRT Implementation of YOLOv3 and YOLOv4](https://github.com/wang-xinyu/tensorrtx/tree/master/yolov3-spp) ## Training **Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco2017.sh`. Each epoch trains on 117,263 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. **Resume Training:** `python3 train.py --resume` to resume training from `weights/last.pt`. **Plot Training:** `from utils import utils; utils.plot_results()`

### Image Augmentation `datasets.py` applies OpenCV-powered (https://opencv.org/) augmentation to the input image. We use a **mosaic dataloader** to increase image variability during training.

### Speed https://cloud.google.com/deep-learning-vm/ **Machine type:** preemptible [n1-standard-8](https://cloud.google.com/compute/docs/machine-types) (8 vCPUs, 30 GB memory) **CPU platform:** Intel Skylake **GPUs:** K80 ($0.14/hr), T4 ($0.11/hr), V100 ($0.74/hr) CUDA with [Nvidia Apex](https://github.com/NVIDIA/apex) FP16/32 **HDD:** 300 GB SSD **Dataset:** COCO train 2014 (117,263 images) **Model:** `yolov3-spp.cfg` **Command:** `python3 train.py --data coco2017.data --img 416 --batch 32` GPU | n | `--batch-size` | img/s | epoch
time | epoch
cost --- |--- |--- |--- |--- |--- K80 |1| 32 x 2 | 11 | 175 min | $0.41 T4 |1
2| 32 x 2
64 x 1 | 41
61 | 48 min
32 min | $0.09
$0.11 V100 |1
2| 32 x 2
64 x 1 | 122
**178** | 16 min
**11 min** | **$0.21**
$0.28 2080Ti |1
2| 32 x 2
64 x 1 | 81
140 | 24 min
14 min | -
- ## Inference ```bash python3 detect.py --source ... ``` - Image: `--source file.jpg` - Video: `--source file.mp4` - Directory: `--source dir/` - Webcam: `--source 0` - RTSP stream: `--source rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa` - HTTP stream: `--source http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8` **YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights yolov3.pt`

**YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights yolov3-tiny.pt`

**YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights yolov3-spp.pt`

## Pretrained Checkpoints Download from: [https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0](https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0) ## 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 'weights/yolov3-spp.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 'weights/yolov3-spp.weights' ``` ## mAP |Size |COCO mAP
@0.5...0.95 |COCO mAP
@0.5 --- | --- | --- | --- YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |320 |14.0
28.7
30.5
**37.7** |29.1
51.8
52.3
**56.8** YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |416 |16.0
31.2
33.9
**41.2** |33.0
55.4
56.9
**60.6** YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |512 |16.6
32.7
35.6
**42.6** |34.9
57.7
59.5
**62.4** YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |608 |16.6
33.1
37.0
**43.1** |35.4
58.2
60.7
**62.8** - mAP@0.5 run at `--iou-thr 0.5`, mAP@0.5...0.95 run at `--iou-thr 0.7` - Darknet results: https://arxiv.org/abs/1804.02767 ```bash $ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 640 --augment Namespace(augment=True, batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='coco2014.data', device='', img_size=640, iou_thres=0.6, save_json=True, single_cls=False, task='test', weights='weight Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB) Class Images Targets P R mAP@0.5 F1: 100%|█████████| 313/313 [03:00<00:00, 1.74it/s] all 5e+03 3.51e+04 0.375 0.743 0.64 0.492 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.647 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.496 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.501 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.596 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.361 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.597 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.666 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.492 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.719 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.810 Speed: 17.5/2.3/19.9 ms inference/NMS/total per 640x640 image at batch-size 16 ``` ## Reproduce Our Results Run commands below. Training takes about one week on a 2080Ti per model. ```bash $ python train.py --data coco2014.data --weights '' --batch-size 16 --cfg yolov3-spp.cfg $ python train.py --data coco2014.data --weights '' --batch-size 32 --cfg yolov3-tiny.cfg ```

## Reproduce Our Environment To access an up-to-date working environment (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled), consider a: - **GCP** Deep Learning VM with $300 free credit offer: See our [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) - **Google Colab Notebook** with 12 hours of free GPU time.

- **Docker Image** https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker) ## Citation [![DOI](https://zenodo.org/badge/146165888.svg)](https://zenodo.org/badge/latestdoi/146165888) ## About Us Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including: - **Cloud-based AI** systems operating on **hundreds of HD video streams in realtime.** - **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.** - **Custom data training**, hyperparameter evolution, and model exportation to any destination. For business inquiries and professional support requests please visit us at https://www.ultralytics.com. ## Contact **Issues should be raised directly in the repository.** For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.