文件列表:

.dockerignore (3664, 2020-02-09)
Dockerfile (2595, 2020-02-09)
LICENSE (35126, 2020-02-09)
cfg (0, 2020-02-09)
cfg\csresnext50-panet-spp.cfg (10622, 2020-02-09)
cfg\yolov3-1cls.cfg (8332, 2020-02-09)
cfg\yolov3-spp-1cls.cfg (8592, 2020-02-09)
cfg\yolov3-spp-3cls.cfg (8592, 2020-02-09)
cfg\yolov3-spp-matrix.cfg (13427, 2020-02-09)
cfg\yolov3-spp-pan-scale.cfg (10538, 2020-02-09)
cfg\yolov3-spp.cfg (8605, 2020-02-09)
cfg\yolov3-spp3.cfg (8959, 2020-02-09)
cfg\yolov3-tiny-1cls.cfg (1911, 2020-02-09)
cfg\yolov3-tiny-3cls.cfg (1911, 2020-02-09)
cfg\yolov3-tiny.cfg (1915, 2020-02-09)
cfg\yolov3-tiny3-1cls.cfg (2451, 2020-02-09)
cfg\yolov3-tiny3.cfg (2457, 2020-02-09)
cfg\yolov3.cfg (8338, 2020-02-09)
cfg\yolov3s.cfg (8605, 2020-02-09)
data (0, 2020-02-09)
data\coco.names (621, 2020-02-09)
data\coco1.data (75, 2020-02-09)
data\coco1.txt (42, 2020-02-09)
data\coco16.data (77, 2020-02-09)
data\coco16.txt (672, 2020-02-09)
data\coco1cls.data (80, 2020-02-09)
data\coco1cls.txt (672, 2020-02-09)
data\coco2014.data (85, 2020-02-09)
data\coco2017.data (87, 2020-02-09)
data\coco64.data (77, 2020-02-09)
data\coco64.txt (2688, 2020-02-09)
data\coco_paper.names (702, 2020-02-09)
data\get_coco2014.sh (824, 2020-02-09)
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# Introduction This directory contains PyTorch YOLOv3 software 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 all of the `pip install -U -r requirements.txt` packages including: - `torch >= 1.4` - `opencv-python` - `Pillow` All dependencies are included in the associated docker images. Docker requirements are: - Nvidia Driver >= 440.44 - Docker Engine - CE >= 19.03 # 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 Our Jupyter [notebook](https://colab.research.google.com/github/ultralytics/yolov3/blob/master/examples.ipynb) provides quick training, inference and testing examples. # Training **Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco_dataset.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()` plots training results from `coco_16img.data`, `coco_***img.data`, 2 example datasets available in the `data/` folder, which train and test on the first 16 and *** images of the COCO2014-trainval dataset. ## 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:** preemptible [n1-standard-16](https://cloud.google.com/compute/docs/machine-types) (16 vCPUs, 60 GB memory) **CPU platform:** Intel Skylake **GPUs:** K80 ($0.20/hr), T4 ($0.35/hr), V100 ($0.83/hr) CUDA with [Nvidia Apex](https://github.com/NVIDIA/apex) FP16/32 **HDD:** 1 TB SSD **Dataset:** COCO train 2014 (117,263 images) **Model:** `yolov3-spp.cfg` **Command:** `python3 train.py --img 416 --batch 32 --accum 2` GPU |n| `--batch --accum` | img/s | epoch
time | epoch
cost --- |--- |--- |--- |--- |--- K80 |1| 32 x 2 | 11 | 175 min | $0.58 T4 |1
2| 32 x 2
*** x 1 | 41
61 | 48 min
32 min | $0.28
$0.36 V100 |1
2| 32 x 2
*** x 1 | 122
**178** | 16 min
**11 min** | **$0.23**
$0.31 2080Ti |1
2| 32 x 2
*** x 1 | 81
140 | 24 min
14 min | -
- # Inference `detect.py` runs inference on any sources: ```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://wmccpinetop.axiscam.net/mjpg/video.mjpg` To run a specific models: **YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights yolov3.weights` **YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights yolov3-tiny.weights` **YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights yolov3-spp.weights` # Pretrained Weights Download from: [https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i***cqEUZD7e0](https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i***cqEUZD7e0) ## 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 ```bash python3 test.py --weights ... --cfg ... ``` - mAP@0.5 run at `--iou-thr 0.5`, mAP@0.5...0.95 run at `--iou-thr 0.7` - YOLOv3-SPP ultralytics is `ultralytics68.pt` with `yolov3-spp.cfg` - Darknet results: https://arxiv.org/abs/1804.02767 |Size |COCO mAP
@0.5...0.95 |COCO mAP
@0.5 --- | --- | --- | --- YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**YOLOv3-SPP ultralytics** |320 |14.0
28.7
30.5
**35.5** |29.1
51.8
52.3
**55.4** YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**YOLOv3-SPP ultralytics** |416 |16.0
31.2
33.9
**39.2** |33.0
55.4
56.9
**59.9** YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**YOLOv3-SPP ultralytics** |512 |16.6
32.7
35.6
**40.5** |34.9
57.7
59.5
**61.4** YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**YOLOv3-SPP ultralytics** |608 |16.6
33.1
37.0
**41.1** |35.4
58.2
60.7
**61.5** ```bash $ python3 test.py --img-size 608 --iou-thr 0.6 --weights ultralytics68.pt --cfg yolov3-spp.cfg Namespace(batch_size=32, cfg='yolov3-spp.cfg', conf_thres=0.001, data='data/coco2014.data', device='', img_size=608, iou_thres=0.6, save_json=True, task='test', weights='ultralytics68.pt') Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB) Class Images Targets P R mAP@0.5 F1: 100% 157/157 [03:30<00:00, 1.16it/s] all 5e+03 3.51e+04 0.0353 0.891 0.606 0.0673 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.409 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.615 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.437 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.242 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.448 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.519 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.337 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.557 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.612 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.438 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.658 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.746 ``` # Reproduce Our Results This command trains `yolov3-spp.cfg` from scratch to our mAP above. Training takes about one week on a 2080Ti. ```bash $ python3 train.py --weights '' --cfg yolov3-spp.cfg --epochs 273 --batch 16 --accum 4 --multi --pre ``` # 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: [Google Colab Notebook](https://colab.research.google.com/drive/1G8T-VFxQkjDe4idzN8F-hbIBqkkkQnxw) - **Docker Image** from https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) # 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.

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