car-detection-bayes/README.md

<table style="width:100%">
  <tr>
    <th>v2.2<img src="https://user-images.githubusercontent.com/26833433/52743528-e6096300-2fe2-11e9-970c-5fee45769fab.jpg" width="400"></th>
        <th>v3.0<img src="https://user-images.githubusercontent.com/26833433/54523854-227d0580-4979-11e9-9801-26a3be239875.jpg" width="400"></th>
    <th><img src="https://storage.googleapis.com/ultralytics/logo/logoname1000.png" width="200">
  <br><br/>
  <p> <a href="https://itunes.apple.com/app/id1452689527">
  <img href="https://itunes.apple.com/app/id1452689527" src="https://user-images.githubusercontent.com/26833433/50044365-9b22ac00-0082-11e9-862f-e77aee7aa7b0.png" width="180"> 
  </a> </p></th> 
  </tr>
</table>


# Introduction

This directory contains python 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.

# 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 the following `pip3 install -U -r requirements.txt` packages:

- `numpy`
- `torch >= 1.0.0`
- `opencv-python`
- `tqdm`

# Tutorials

* [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)

# Training

**Start Training:** Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`.

**Resume Training:** Run `train.py --resume` resumes training from the latest checkpoint `weights/latest.pt`.

Each epoch trains on 120,000 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 0.6 s/batch on a 1080 Ti (18 epochs/day)** or 0.45 s/batch on a 2080 Ti.

`from utils import utils; utils.plot_results()`
![Alt](https://user-images.githubusercontent.com/26833433/53494085-3251aa00-3a9d-11e9-8af7-8c08cf40d70b.png "train.py results")

## 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%

<img src="https://user-images.githubusercontent.com/26833433/50525037-6cbcbc00-0ad9-11e9-8c38-9fd51af530e0.jpg">

## Speed

https://cloud.google.com/deep-learning-vm/  
**Machine type:** n1-standard-8 (8 vCPUs, 30 GB memory)  
**CPU platform:** Intel Skylake  
**GPUs:** 1-4x P100 ($0.493/hr), 1-8x V100 ($0.803/hr)  
**HDD:** 100 GB SSD  
**Dataset:** COCO train 2014 

GPUs | `batch_size` | batch time | epoch time | epoch cost
--- |---| --- | --- | --- 
<i></i> |  (images)  | (s/batch) |  |
1 P100 | 16 | 0.39s  | 48min  | $0.39
2 P100 | 32 | 0.48s | 29min | $0.47
4 P100 | 64 | 0.65s | 20min | $0.65
1 V100 | 16 | 0.25s  | 31min | $0.41
2 V100 | 32 | 0.29s | 18min | $0.48
4 V100 | 64 | 0.41s | 13min | $0.70
8 V100 | 128 | 0.49s | 7min | $0.80

# Inference

Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder:

**YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.weights`
<img src="https://user-images.githubusercontent.com/26833433/50524393-b0adc200-0ad5-11e9-9335-4774a1e52374.jpg" width="600">

**YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.weights`
<img src="https://user-images.githubusercontent.com/26833433/50374155-21427380-05ea-11e9-8d24-f1a4b2bac1ad.jpg" width="600">

**YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights weights/yolov3-spp.weights`
<img src="https://user-images.githubusercontent.com/26833433/54747926-e051ff00-4bd8-11e9-8b5d-93a41d871ec7.jpg" width="600">

## Webcam

Run `detect.py` with `webcam=True` to show a live webcam feed.

# Pretrained Weights

- Darknet `*.weights` format: https://pjreddie.com/media/files/yolov3.weights
- PyTorch `*.pt` format: https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI

# mAP

- Use `test.py --weights weights/yolov3.weights` to test the official YOLOv3 weights.
- Use `test.py --weights weights/latest.pt` to test the latest training results.
- Compare to official darknet results from https://arxiv.org/abs/1804.02767.

<i></i> | ultralytics/yolov3 | darknet  
--- | ---| ---   
YOLOv3-320 | 51.3 | 51.5  
YOLOv3-416 | 54.9 | 55.3  
YOLOv3-608 | 57.9 | 57.9  

``` bash
sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
# bash yolov3/data/get_coco_dataset.sh
sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
cd yolov3

python3 test.py --save-json --conf-thres 0.001 --img-size 416
Namespace(batch_size=32, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights')
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.308
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.549
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.310
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.454
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.267
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.403
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.428
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.585
 
python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16
Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights')
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.328
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.579
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.335
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.279
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.429
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.456
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.299
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.483
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.572
```

# Contact

For questions or comments please contact Glenn Jocher at glenn.jocher@ultralytics.com or visit us at https://contact.ultralytics.com.
Update README.md 2019-02-13 21:15:30 +00:00			`<table style="width:100%">`
			`<tr>`
Update README.md 2019-03-18 10:33:31 +00:00			`<th>v2.2<img src="https://user-images.githubusercontent.com/26833433/52743528-e6096300-2fe2-11e9-970c-5fee45769fab.jpg" width="400"></th>`
			`<th>v3.0<img src="https://user-images.githubusercontent.com/26833433/54523854-227d0580-4979-11e9-9801-26a3be239875.jpg" width="400"></th>`
Update README.md 2019-02-13 21:15:30 +00:00			`<th><img src="https://storage.googleapis.com/ultralytics/logo/logoname1000.png" width="200">`
			`<br><br/>`
			`<p> <a href="https://itunes.apple.com/app/id1452689527">`
			`<img href="https://itunes.apple.com/app/id1452689527" src="https://user-images.githubusercontent.com/26833433/50044365-9b22ac00-0082-11e9-862f-e77aee7aa7b0.png" width="180">`
			`</a> </p></th>`
			`</tr>`
			`</table>`
Update README.md 2019-02-13 21:01:58 +00:00
Update README.md 2019-03-18 10:33:31 +00:00
Initial commit 2018-08-26 08:51:39 +00:00			`# Introduction`

Update README.md 2019-03-08 12:14:55 +00:00			`This directory contains python 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.`
Initial commit 2018-08-26 08:51:39 +00:00
			`# Description`

Add collate_fn() to DataLoader (#163) Multi-GPU update with custom collate function to allow variable size target vector per image without needing to pad targets. 2019-03-25 13:59:38 +00:00			`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/.`
Initial commit 2018-08-26 08:51:39 +00:00
			`# Requirements`

updates 2018-11-27 17:14:48 +00:00			Python 3.7 or later with the following `pip3 install -U -r requirements.txt` packages:
Initial commit 2018-08-26 08:51:39 +00:00
			- `numpy`
updates 2019-01-05 15:23:17 +00:00			- `torch >= 1.0.0`
Initial commit 2018-08-26 08:51:39 +00:00			- `opencv-python`
Add collate_fn() to DataLoader (#163) Multi-GPU update with custom collate function to allow variable size target vector per image without needing to pad targets. 2019-03-25 13:59:38 +00:00			- `tqdm`
Initial commit 2018-08-26 08:51:39 +00:00
Update README.md 2019-03-05 15:23:33 +00:00			`# Tutorials`

			`* [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)`

updates 2018-08-26 09:05:13 +00:00			`# Training`
Initial commit 2018-08-26 08:51:39 +00:00
Update README.md 2019-03-18 10:45:49 +00:00			Start Training: Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`.
updates 2018-09-01 16:35:28 +00:00
Update README.md 2019-03-18 10:46:44 +00:00			Resume Training: Run `train.py --resume` resumes training from the latest checkpoint `weights/latest.pt`.
updates 2018-09-01 16:35:28 +00:00
updates 2019-02-27 13:40:41 +00:00			`Each epoch trains on 120,000 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 0.6 s/batch on a 1080 Ti (18 epochs/day) or 0.45 s/batch on a 2080 Ti.`
Update README.md 2018-12-28 18:23:35 +00:00
updates 2019-02-27 13:07:04 +00:00			`from utils import utils; utils.plot_results()`
updates 2019-02-27 13:38:57 +00:00			`![Alt](https://user-images.githubusercontent.com/26833433/53494085-3251aa00-3a9d-11e9-8af7-8c08cf40d70b.png "train.py results")`
updates 2018-09-01 11:34:05 +00:00
updates 2018-09-01 11:43:07 +00:00			`## Image Augmentation`
updates 2018-09-01 11:34:05 +00:00
updates 2018-09-01 16:57:18 +00:00			`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.
updates 2018-09-01 11:41:34 +00:00
			`Augmentation \| Description`
			`--- \| ---`
Update README.md 2018-12-28 18:23:35 +00:00			`Translation \| +/- 10% (vertical and horizontal)`
updates 2018-09-01 12:04:42 +00:00			`Rotation \| +/- 5 degrees`
Update README.md 2018-12-28 18:23:35 +00:00			`Shear \| +/- 2 degrees (vertical and horizontal)`
			`Scale \| +/- 10%`
updates 2018-09-01 12:10:06 +00:00			`Reflection \| 50% probability (horizontal-only)`
updates 2018-09-01 12:04:42 +00:00			`HSV Saturation \| +/- 50%`
			`HSV Intensity \| +/- 50%`
updates 2018-09-01 11:34:05 +00:00
Update README.md 2018-12-28 18:52:25 +00:00			`<img src="https://user-images.githubusercontent.com/26833433/50525037-6cbcbc00-0ad9-11e9-8c38-9fd51af530e0.jpg">`
Initial commit 2018-08-26 08:51:39 +00:00
Update README.md 2019-03-20 11:35:39 +00:00			`## Speed`

			`https://cloud.google.com/deep-learning-vm/`
Add collate_fn() to DataLoader (#163) Multi-GPU update with custom collate function to allow variable size target vector per image without needing to pad targets. 2019-03-25 13:59:38 +00:00			`Machine type: n1-standard-8 (8 vCPUs, 30 GB memory)`
Update README.md 2019-03-20 11:35:39 +00:00			`CPU platform: Intel Skylake`
Add collate_fn() to DataLoader (#163) Multi-GPU update with custom collate function to allow variable size target vector per image without needing to pad targets. 2019-03-25 13:59:38 +00:00			`GPUs: 1-4x P100 ($0.493/hr), 1-8x V100 ($0.803/hr)`
Update README.md 2019-03-20 11:35:39 +00:00			`HDD: 100 GB SSD`
Add collate_fn() to DataLoader (#163) Multi-GPU update with custom collate function to allow variable size target vector per image without needing to pad targets. 2019-03-25 13:59:38 +00:00			`Dataset: COCO train 2014`

			GPUs \| `batch_size` \| batch time \| epoch time \| epoch cost
			`--- \|---\| --- \| --- \| ---`
			`<i></i> \| (images) \| (s/batch) \| \|`
			`1 P100 \| 16 \| 0.39s \| 48min \| $0.39`
			`2 P100 \| 32 \| 0.48s \| 29min \| $0.47`
			`4 P100 \| 64 \| 0.65s \| 20min \| $0.65`
			`1 V100 \| 16 \| 0.25s \| 31min \| $0.41`
			`2 V100 \| 32 \| 0.29s \| 18min \| $0.48`
			`4 V100 \| 64 \| 0.41s \| 13min \| $0.70`
			`8 V100 \| 128 \| 0.49s \| 7min \| $0.80`
Update README.md 2019-03-20 11:35:39 +00:00
updates 2018-08-26 09:05:13 +00:00			`# Inference`
updates 2019-02-11 13:13:27 +00:00
			Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder:
updates 2018-12-22 12:05:52 +00:00
Update README.md 2019-03-21 11:01:07 +00:00			YOLOv3: `python3 detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.weights`
Update README.md 2019-03-21 11:00:24 +00:00			`<img src="https://user-images.githubusercontent.com/26833433/50524393-b0adc200-0ad5-11e9-9335-4774a1e52374.jpg" width="600">`
updates 2018-12-22 12:05:52 +00:00
Update README.md 2019-03-21 11:01:07 +00:00			YOLOv3-tiny: `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.weights`
Update README.md 2019-03-21 11:00:24 +00:00			`<img src="https://user-images.githubusercontent.com/26833433/50374155-21427380-05ea-11e9-8d24-f1a4b2bac1ad.jpg" width="600">`

Update README.md 2019-03-21 11:01:07 +00:00			YOLOv3-SPP: `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights weights/yolov3-spp.weights`
Update README.md 2019-03-21 11:00:24 +00:00			`<img src="https://user-images.githubusercontent.com/26833433/54747926-e051ff00-4bd8-11e9-8b5d-93a41d871ec7.jpg" width="600">`
updates 2018-09-04 12:36:51 +00:00
updates 2019-02-11 13:11:24 +00:00			`## Webcam`

updates 2019-02-11 13:13:27 +00:00			Run `detect.py` with `webcam=True` to show a live webcam feed.
updates 2019-02-11 13:11:24 +00:00
yolov3-tiny addition 2018-12-22 11:49:55 +00:00			`# Pretrained Weights`
updates 2018-12-22 11:58:59 +00:00
Update README.md 2019-03-20 12:08:24 +00:00			- Darknet `*.weights` format: https://pjreddie.com/media/files/yolov3.weights
			- PyTorch `*.pt` format: https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI
updates 2018-09-01 16:48:53 +00:00
Update README.md 2019-03-20 11:26:46 +00:00			`# mAP`

Update README.md 2019-03-20 12:08:24 +00:00			- Use `test.py --weights weights/yolov3.weights` to test the official YOLOv3 weights.
			- Use `test.py --weights weights/latest.pt` to test the latest training results.
			`- Compare to official darknet results from https://arxiv.org/abs/1804.02767.`
Update README.md 2019-03-20 11:26:46 +00:00
Update README.md 2019-03-20 12:08:24 +00:00			`<i></i> \| ultralytics/yolov3 \| darknet`
			`--- \| ---\| ---`
			`YOLOv3-320 \| 51.3 \| 51.5`
			`YOLOv3-416 \| 54.9 \| 55.3`
			`YOLOv3-608 \| 57.9 \| 57.9`
Update README.md 2019-03-20 11:26:46 +00:00
			``` bash
			`sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3`
			`# bash yolov3/data/get_coco_dataset.sh`
			`sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3`
Update README.md 2019-03-20 12:08:24 +00:00			`cd yolov3`

			`python3 test.py --save-json --conf-thres 0.001 --img-size 416`
			`Namespace(batch_size=32, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights')`
			`Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.308`
			`Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.549`
			`Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.310`
			`Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.141`
			`Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.334`
			`Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.454`
			`Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.267`
			`Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.403`
			`Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.428`
			`Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.237`
			`Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.464`
			`Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.585`

			`python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16`
			`Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights')`
			`Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.328`
			`Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.579`
			`Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.335`
			`Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.190`
			`Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.357`
			`Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.428`
			`Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.279`
			`Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.429`
			`Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.456`
			`Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.299`
			`Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.483`
			`Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.572`
Update README.md 2019-03-20 11:26:46 +00:00			```
Initial commit 2018-08-26 08:51:39 +00:00
			`# Contact`

Update README.md 2018-12-28 18:17:33 +00:00			`For questions or comments please contact Glenn Jocher at glenn.jocher@ultralytics.com or visit us at https://contact.ultralytics.com.`