221 lines
11 KiB
Markdown
Executable File
221 lines
11 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/26833433/56132958-9e279d80-5f8b-11e9-8d23-c34dbb2a39e6.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="200"></a>
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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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<img src="https://user-images.githubusercontent.com/26833433/56132957-9d8f0700-5f8b-11e9-9067-0d91364c57c0.jpg">
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</td>
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<td>
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<img src="https://user-images.githubusercontent.com/26833433/56132991-b0094080-5f8b-11e9-89a1-62fb65b98d45.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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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.
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# Description
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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 the following `pip3 install -U -r requirements.txt` packages:
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- `numpy`
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- `torch >= 1.1.0`
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- `opencv-python`
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- `tqdm`
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# Tutorials
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* [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
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* [Transfer Learning](https://github.com/ultralytics/yolov3/wiki/Example:-Transfer-Learning)
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* [Train Single Image](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Image)
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* [Train Single Class](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class)
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* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data)
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# Jupyter Notebook
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A jupyter notebook with training, inference and testing examples is available at:
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https://colab.research.google.com/drive/1G8T-VFxQkjDe4idzN8F-hbIBqkkkQnxw
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# Training
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**Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`.
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**Resume Training:** `python3 train.py --resume` to resume training from `weights/last.pt`.
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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)**.
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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.
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`from utils import utils; utils.plot_results()`
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## Image Augmentation
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`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.
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Augmentation | Description
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--- | ---
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Translation | +/- 10% (vertical and horizontal)
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Rotation | +/- 5 degrees
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Shear | +/- 2 degrees (vertical and horizontal)
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Scale | +/- 10%
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Reflection | 50% probability (horizontal-only)
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H**S**V Saturation | +/- 50%
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HS**V** Intensity | +/- 50%
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<img src="https://user-images.githubusercontent.com/26833433/61579359-507b7d80-ab04-11e9-8a2a-bd6f59bbdfb4.jpg">
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## Speed
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https://cloud.google.com/deep-learning-vm/
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**Machine type:** n1-standard-8 (8 vCPUs, 30 GB memory)
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**CPU platform:** Intel Skylake
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**GPUs:** K80 ($0.198/hr), P4 ($0.279/hr), T4 ($0.353/hr), P100 ($0.493/hr), V100 ($0.803/hr)
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**HDD:** 100 GB SSD
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**Dataset:** COCO train 2014
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GPUs | `batch_size` | batch time | epoch time | epoch cost
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--- |---| --- | --- | ---
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<i></i> | (images) | (s/batch) | |
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1 K80 | 16 | 1.43s | 175min | $0.58
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1 P4 | 8 | 0.51s | 125min | $0.58
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1 T4 | 16 | 0.78s | 94min | $0.55
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1 P100 | 16 | 0.39s | 48min | $0.39
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2 P100 | 32 | 0.48s | 29min | $0.47
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4 P100 | 64 | 0.65s | 20min | $0.65
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1 V100 | 16 | 0.25s | 31min | $0.41
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2 V100 | 32 | 0.29s | 18min | $0.48
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4 V100 | 64 | 0.41s | 13min | $0.70
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8 V100 | 128 | 0.49s | 7min | $0.80
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# Inference
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`detect.py` runs inference on all images **and videos** in the `data/samples` folder:
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**YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.weights`
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<img src="https://user-images.githubusercontent.com/26833433/50524393-b0adc200-0ad5-11e9-9335-4774a1e52374.jpg" width="600">
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**YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.weights`
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<img src="https://user-images.githubusercontent.com/26833433/50374155-21427380-05ea-11e9-8d24-f1a4b2bac1ad.jpg" width="600">
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**YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights weights/yolov3-spp.weights`
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<img src="https://user-images.githubusercontent.com/26833433/54747926-e051ff00-4bd8-11e9-8b5d-93a41d871ec7.jpg" width="600">
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## Webcam
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`detect.py` with `webcam=True` shows a live webcam feed.
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# Pretrained Weights
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- Darknet `*.weights` format: https://pjreddie.com/media/files/yolov3.weights
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- PyTorch `*.pt` format: https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI
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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 'converted.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 'converted.weights'
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```
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# mAP
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- `test.py --weights weights/yolov3.weights` tests official YOLOv3 weights.
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- `test.py --weights weights/last.pt` tests most recent checkpoint.
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- `test.py --weights weights/best.pt` tests best checkpoint.
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- Compare to darknet published results https://arxiv.org/abs/1804.02767.
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<!---
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%<i></i> | ultralytics/yolov3 OR-NMS 5:52@416 (`pycocotools`) | darknet
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--- | --- | ---
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YOLOv3-320 | 51.9 (51.4) | 51.5
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YOLOv3-416 | 55.0 (54.9) | 55.3
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YOLOv3-608 | 57.5 (57.8) | 57.9
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<i></i> | ultralytics/yolov3 MERGE-NMS 7:15@416 (`pycocotools`) | darknet
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--- | --- | ---
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YOLOv3-320 | 52.3 (51.7) | 51.5
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YOLOv3-416 | 55.4 (55.3) | 55.3
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YOLOv3-608 | 57.9 (58.1) | 57.9
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<i></i> | ultralytics/yolov3 MERGE+earlier_pred4 8:34@416 (`pycocotools`) | darknet
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--- | --- | ---
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YOLOv3-320 | 52.3 (51.8) | 51.5
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YOLOv3-416 | 55.5 (55.4) | 55.3
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YOLOv3-608 | 57.9 (58.2) | 57.9
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--->
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<i></i> | [ultralytics/yolov3](https://github.com/ultralytics/yolov3) | [darknet](https://arxiv.org/abs/1804.02767)
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--- | --- | ---
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`YOLOv3 320` | 51.8 | 51.5
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`YOLOv3 416` | 55.4 | 55.3
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`YOLOv3 608` | 58.2 | 57.9
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`YOLOv3-spp 320` | 52.4 | -
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`YOLOv3-spp 416` | 56.5 | -
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`YOLOv3-spp 608` | 60.7 | 60.6
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``` bash
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# install pycocotools
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git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
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cd yolov3
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python3 test.py --save-json --img-size 608
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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')
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Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
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Class Images Targets P R mAP F1: 100% 313/313 [07:40<00:00, 2.34s/it]
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all 5e+03 3.58e+04 0.117 0.788 0.595 0.199
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.367
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607 <--
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.387
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.208
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.392
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.297
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.465
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.495
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.332
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.518
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621
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python3 test.py --save-json --img-size 416
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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')
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Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
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Class Images Targets P R mAP F1: 100% 313/313 [07:01<00:00, 1.41s/it]
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all 5e+03 3.58e+04 0.105 0.746 0.554 0.18
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.336
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.565 <--
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.350
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.361
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.494
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.281
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.433
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.459
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.256
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.495
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.622
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```
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# Citation
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[](https://zenodo.org/badge/latestdoi/146165888)
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# Contact
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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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