greenhouse/README.md

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&nbsp

<a href="https://github.com/ultralytics/yolov3/actions"><img src="https://github.com/ultralytics/yolov3/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>

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Backwards-compatible with original [darknet](https://pjreddie.com/darknet/) *.cfg models (⚠️ no longer maintained).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
```

<img src="https://user-images.githubusercontent.com/26833433/100382066-c8bc5200-301a-11eb-907b-799a0301595e.png" width="1000">** 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<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | FLOPS |
|---------- |------ |------ |------ | -------- | ------| ------ |------  |  :------: |
| [YOLOv3](https://github.com/ultralytics/yolov3/releases)      | 43.3   | 43.3     | 63.0     | 4.8ms     | 208     || 61.9M  | 156.4B
| [YOLOv3-SPP](https://github.com/ultralytics/yolov3/releases)  | **44.3**   | **44.3**     | **64.6**     | 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<sup>test</sup> 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 640 --conf 0.001 --iou 0.65`  
** Speed<sub>GPU</sub> 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 640 --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)&nbsp; 🚀 RECOMMENDED
* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)&nbsp; 🌟 NEW
* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)&nbsp; ⭐ 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)&nbsp; ⭐ 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: <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>
- **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) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker)


## 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=640, 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: 640x480 4 persons, 1 buss, Done. (0.014s)
image 2/2 /content/yolov3/data/images/zidane.jpg: 384x640 2 persons, 3 ties, Done. (0.014s)
Results saved to runs/detect/exp
Done. (0.133s)
```
<img src="https://user-images.githubusercontent.com/26833433/100375993-06b37900-300f-11eb-8d2d-5fc7b22fbfbd.jpg" width="500">  

### 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=640)  # 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                      64
```
<img src="https://user-images.githubusercontent.com/26833433/100378028-af170c80-3012-11eb-8521-f0d2a8d021bc.png" width="900">


## 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.