198 lines
10 KiB
Markdown
Executable File
198 lines
10 KiB
Markdown
Executable File
<table style="width:100%">
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<tr>
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<td>
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<img src="https://user-images.githubusercontent.com/26833433/61591130-f7beea00-abc2-11e9-9dc0-d6abcf41d713.jpg">
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</td>
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<td align="center">
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<a href="https://www.ultralytics.com" target="_blank">
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<img src="https://storage.googleapis.com/ultralytics/logo/logoname1000.png" width="160"></a>
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<img src="https://user-images.githubusercontent.com/26833433/61591093-2b4d4480-abc2-11e9-8b46-d88eb1dabba1.jpg">
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<a href="https://itunes.apple.com/app/id1452689527" target="_blank">
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<img src="https://user-images.githubusercontent.com/26833433/50044365-9b22ac00-0082-11e9-862f-e77aee7aa7b0.png" width="180"></a>
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</td>
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<td>
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<img src="https://user-images.githubusercontent.com/26833433/61591100-55066b80-abc2-11e9-9647-52c0e045b288.jpg">
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</td>
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</tr>
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</table>
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## Introduction
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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/.
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## Requirements
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Python 3.7 or later with all `requirements.txt` dependencies installed, including `torch >= 1.5`. To install run:
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```bash
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$ pip install -U -r requirements.txt
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```
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## Tutorials
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* <a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) < highly recommended!!
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* [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
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* [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart)
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* [A TensorRT Implementation of YOLOv3 and YOLOv4](https://github.com/wang-xinyu/tensorrtx/tree/master/yolov3-spp)
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## Training
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**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.
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**Resume Training:** `python3 train.py --resume` to resume training from `weights/last.pt`.
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**Plot Training:** `from utils import utils; utils.plot_results()`
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<img src="https://user-images.githubusercontent.com/26833433/78175826-599d4800-7410-11ea-87d4-f629071838f6.png" width="900">
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### Image Augmentation
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`datasets.py` applies OpenCV-powered (https://opencv.org/) augmentation to the input image. We use a **mosaic dataloader** to increase image variability during training.
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<img src="https://user-images.githubusercontent.com/26833433/80769557-6e015d00-8b02-11ea-9c4b-69310eb2b962.jpg" width="900">
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### Speed
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https://cloud.google.com/deep-learning-vm/
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**Machine type:** preemptible [n1-standard-8](https://cloud.google.com/compute/docs/machine-types) (8 vCPUs, 30 GB memory)
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**CPU platform:** Intel Skylake
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**GPUs:** K80 ($0.14/hr), T4 ($0.11/hr), V100 ($0.74/hr) CUDA with [Nvidia Apex](https://github.com/NVIDIA/apex) FP16/32
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**HDD:** 300 GB SSD
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**Dataset:** COCO train 2014 (117,263 images)
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**Model:** `yolov3-spp.cfg`
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**Command:** `python3 train.py --data coco2017.data --img 416 --batch 32`
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GPU | n | `--batch-size` | img/s | epoch<br>time | epoch<br>cost
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--- |--- |--- |--- |--- |---
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K80 |1| 32 x 2 | 11 | 175 min | $0.41
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T4 |1<br>2| 32 x 2<br>64 x 1 | 41<br>61 | 48 min<br>32 min | $0.09<br>$0.11
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V100 |1<br>2| 32 x 2<br>64 x 1 | 122<br>**178** | 16 min<br>**11 min** | **$0.21**<br>$0.28
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2080Ti |1<br>2| 32 x 2<br>64 x 1 | 81<br>140 | 24 min<br>14 min | -<br>-
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## Inference
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```bash
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python3 detect.py --source ...
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```
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- Image: `--source file.jpg`
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- Video: `--source file.mp4`
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- Directory: `--source dir/`
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- Webcam: `--source 0`
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- RTSP stream: `--source rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa`
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- HTTP stream: `--source http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8`
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**YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights yolov3.pt`
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<img src="https://user-images.githubusercontent.com/26833433/64067835-51d5b500-cc2f-11e9-982e-843f7f9a6ea2.jpg" width="500">
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**YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights yolov3-tiny.pt`
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<img src="https://user-images.githubusercontent.com/26833433/64067834-51d5b500-cc2f-11e9-9357-c485b159a20b.jpg" width="500">
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**YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights yolov3-spp.pt`
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<img src="https://user-images.githubusercontent.com/26833433/64067833-51d5b500-cc2f-11e9-8208-6fe197809131.jpg" width="500">
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## Pretrained Checkpoints
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Download from: [https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0](https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0)
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## Darknet Conversion
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```bash
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$ git clone https://github.com/ultralytics/yolov3 && cd yolov3
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# convert darknet cfg/weights to pytorch model
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$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')"
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Success: converted 'weights/yolov3-spp.weights' to 'weights/yolov3-spp.pt'
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# convert cfg/pytorch model to darknet weights
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$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.pt')"
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Success: converted 'weights/yolov3-spp.pt' to 'weights/yolov3-spp.weights'
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```
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## mAP
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<i></i> |Size |COCO mAP<br>@0.5...0.95 |COCO mAP<br>@0.5
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--- | --- | --- | ---
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |320 |14.0<br>28.7<br>30.5<br>**37.7** |29.1<br>51.8<br>52.3<br>**56.8**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |416 |16.0<br>31.2<br>33.9<br>**41.2** |33.0<br>55.4<br>56.9<br>**60.6**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |512 |16.6<br>32.7<br>35.6<br>**42.6** |34.9<br>57.7<br>59.5<br>**62.4**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |608 |16.6<br>33.1<br>37.0<br>**43.1** |35.4<br>58.2<br>60.7<br>**62.8**
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- mAP@0.5 run at `--iou-thr 0.5`, mAP@0.5...0.95 run at `--iou-thr 0.7`
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- Darknet results: https://arxiv.org/abs/1804.02767
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```bash
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$ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 640 --augment
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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
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Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
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Class Images Targets P R mAP@0.5 F1: 100%|█████████| 313/313 [03:00<00:00, 1.74it/s]
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all 5e+03 3.51e+04 0.375 0.743 0.64 0.492
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.647
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.496
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.501
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.596
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.361
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.597
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.666
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.492
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.719
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.810
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Speed: 17.5/2.3/19.9 ms inference/NMS/total per 640x640 image at batch-size 16
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```
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<!-- Speed: 11.4/2.2/13.6 ms inference/NMS/total per 608x608 image at batch-size 1 -->
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## Reproduce Our Results
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Run commands below. Training takes about one week on a 2080Ti per model.
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```bash
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$ python train.py --data coco2014.data --weights '' --batch-size 16 --cfg yolov3-spp.cfg
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$ python train.py --data coco2014.data --weights '' --batch-size 32 --cfg yolov3-tiny.cfg
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```
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<img src="https://user-images.githubusercontent.com/26833433/80831822-57a9de80-8ba0-11ea-9684-c47afb0432dc.png" width="900">
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## Reproduce Our Environment
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To access an up-to-date working environment (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled), consider a:
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- **GCP** Deep Learning VM with $300 free credit offer: See our [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
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- **Google Colab Notebook** with 12 hours of free GPU time. <a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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- **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart)
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## Citation
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[](https://zenodo.org/badge/latestdoi/146165888)
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## About Us
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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:
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- **Cloud-based AI** surveillance systems operating on **hundreds of HD video streams in realtime.**
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- **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.**
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- **Custom data training**, hyperparameter evolution, and model exportation to any destination.
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For business inquiries and professional support requests please visit us at https://www.ultralytics.com.
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## Contact
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**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.
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