文件列表:

.dockerignore (3610, 2021-02-23)
Dockerfile (1720, 2021-02-23)
LICENSE (35126, 2021-02-23)
data (0, 2021-02-23)
data\coco.yaml (1727, 2021-02-23)
data\coco128.yaml (1534, 2021-02-23)
data\hyp.finetune.yaml (846, 2021-02-23)
data\hyp.scratch.yaml (1566, 2021-02-23)
data\images (0, 2021-02-23)
data\images\bus.jpg (487438, 2021-02-23)
data\images\zidane.jpg (168949, 2021-02-23)
data\scripts (0, 2021-02-23)
data\scripts\get_coco.sh (935, 2021-02-23)
data\scripts\get_voc.sh (4388, 2021-02-23)
data\voc.yaml (735, 2021-02-23)
detect.py (8079, 2021-02-23)
hubconf.py (4556, 2021-02-23)
models (0, 2021-02-23)
models\__init__.py (0, 2021-02-23)
models\common.py (12569, 2021-02-23)
models\experimental.py (5047, 2021-02-23)
models\export.py (3844, 2021-02-23)
models\yolo.py (12560, 2021-02-23)
models\yolov3-spp.yaml (1531, 2021-02-23)
... ...

  BRANCH NOTICE: The [ultralytics/yolov3](https://github.com/ultralytics/yolov3) repository is now divided into two branches: * [Master branch](https://github.com/ultralytics/yolov3/tree/master): Forward-compatible with all [YOLOv5](https://github.com/ultralytics/yolov5) models and methods (**recommended**). ```bash $ git clone https://github.com/ultralytics/yolov3 # master branch (default) ``` * [Archive branch](https://github.com/ultralytics/yolov3/tree/archive): Backwards-compatible with original [darknet](https://pjreddie.com/darknet/) *.cfg models ( no longer maintained). ```bash $ git clone -b archive https://github.com/ultralytics/yolov3 # archive branch ``` ** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. ## Pretrained Checkpoints | Model | AP^val | AP^test | AP₅₀ | Speed_GPU | FPS_GPU || params | FLOPS | |---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: | | [YOLOv3](https://github.com/ultralytics/yolov3/releases) | 43.3 | 43.3 | 63.0 | 4.8ms | 208 || 61.9M | 15***B | [YOLOv3-SPP](https://github.com/ultralytics/yolov3/releases) | **44.3** | **44.3** | *****.6** | 4.9ms | 204 || 63.0M | 157.0B | [YOLOv3-tiny](https://github.com/ultralytics/yolov3/releases) | 17.6 | 34.9 | 34.9 | **1.7ms** | **588** || 8.9M | 13.3B ** AP^test denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy. ** All AP numbers are for single-model single-scale without ensemble or TTA. **Reproduce mAP** by `python test.py --data coco.yaml --img ***0 --conf 0.001 --iou 0.65` ** Speed_GPU averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes image preprocessing, FP16 inference, postprocessing and NMS. NMS is 1-2ms/img. **Reproduce speed** by `python test.py --data coco.yaml --img ***0 --conf 0.25 --iou 0.45` ** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). ** Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) runs at 3 image sizes. **Reproduce TTA** by `python test.py --data coco.yaml --img 832 --iou 0.65 --augment` ## Requirements Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov3/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run: ```bash $ pip install -r requirements.txt ``` ## Tutorials * [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data)  RECOMMENDED * [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)  NEW * [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) * [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)  NEW * [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251) * [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303) * [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318) * [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304) * [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607) * [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)  NEW * [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx) ## Environments YOLOv3 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): - **Google Colab Notebook** with free GPU: - **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov3](https://www.kaggle.com/ultralytics/yolov3) - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) - **Docker Image** https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart)  ## Inference detect.py runs inference on a variety of sources, downloading models automatically from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/releases) and saving results to `runs/detect`. ```bash $ python detect.py --source 0 # webcam file.jpg # image file.mp4 # video path/ # directory path/*.jpg # glob rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream rtmp://192.168.1.105/live/test # rtmp stream http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream ``` To run inference on example images in `data/images`: ```bash $ python detect.py --source data/images --weights yolov3.pt --conf 0.25 Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=***0, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov3.pt']) Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB) Downloading https://github.com/ultralytics/yolov3/releases/download/v1.0/yolov3.pt to yolov3.pt... 100% 118M/118M [00:05<00:00, 24.2MB/s] Fusing layers... Model Summary: 261 layers, 61922845 parameters, 0 gradients image 1/2 /content/yolov3/data/images/bus.jpg: ***0x480 4 persons, 1 buss, Done. (0.014s) image 2/2 /content/yolov3/data/images/zidane.jpg: 384x***0 2 persons, 3 ties, Done. (0.014s) Results saved to runs/detect/exp Done. (0.133s) ``` ### PyTorch Hub To run **batched inference** with YOLO3 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36): ```python import torch from PIL import Image # Model model = torch.hub.load('ultralytics/yolov3', 'yolov3', pretrained=True).autoshape() # for PIL/cv2/np inputs and NMS # Images img1 = Image.open('zidane.jpg') img2 = Image.open('bus.jpg') imgs = [img1, img2] # batched list of images # Inference prediction = model(imgs, size=***0) # includes NMS ``` ## Training Download [COCO](https://github.com/ultralytics/yolov3/blob/master/data/scripts/get_coco.sh) and run command below. Training times for YOLOv3/YOLOv3-SPP/YOLOv3-tiny are 6/6/2 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices). ```bash $ python train.py --data coco.yaml --cfg yolov3.yaml --weights '' --batch-size 24 yolov3-spp.yaml 24 yolov3-tiny.yaml *** ``` ## Citation [](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.

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