diff --git a/README.md b/README.md
index b194e89c..b84d5441 100755
--- a/README.md
+++ b/README.md
@@ -16,30 +16,30 @@
-# 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
+## Introduction
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 `pip install -U -r requirements.txt` packages including `torch >= 1.5`. Docker images come with all dependencies preinstalled. Docker requirements are:
-- Nvidia Driver >= 440.44
-- Docker Engine - CE >= 19.03
+## Requirements
-# Tutorials
+Python 3.7 or later with all `requirements.txt` dependencies installed, including `torch >= 1.5`. To install run:
+```bash
+$ pip install -U -r requirements.txt
+```
+
+## Tutorials
+
+*
* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) < highly recommended!!
-* [Train Single Class](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class)
-* [Google Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb) with quick training, inference and testing examples
* [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
* [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart)
* [A TensorRT Implementation of YOLOv3 and YOLOv4](https://github.com/wang-xinyu/tensorrtx/tree/master/yolov3-spp)
-# Training
+
+## Training
**Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco2017.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.
@@ -49,13 +49,15 @@ Python 3.7 or later with all `pip install -U -r requirements.txt` packages inclu
-## Image Augmentation
+
+### Image Augmentation
`datasets.py` applies OpenCV-powered (https://opencv.org/) augmentation to the input image. We use a **mosaic dataloader** to increase image variability during training.
-## Speed
+
+### Speed
https://cloud.google.com/deep-learning-vm/
**Machine type:** preemptible [n1-standard-8](https://cloud.google.com/compute/docs/machine-types) (8 vCPUs, 30 GB memory)
@@ -73,6 +75,7 @@ T4 |1
2| 32 x 2
64 x 1 | 41
61 | 48 min
32 min | $0.09
$0.11
V100 |1
2| 32 x 2
64 x 1 | 122
**178** | 16 min
**11 min** | **$0.21**
$0.28
2080Ti |1
2| 32 x 2
64 x 1 | 81
140 | 24 min
14 min | -
-
+
# Inference
```bash
@@ -96,10 +99,11 @@ python3 detect.py --source ...
-# Pretrained Weights
+## Pretrained Checkpoints
Download from: [https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0](https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0)
+
## Darknet Conversion
```bash
@@ -114,7 +118,8 @@ $ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolo
Success: converted 'weights/yolov3-spp.pt' to 'weights/yolov3-spp.weights'
```
-# mAP
+
+## mAP
|Size |COCO mAP
@0.5...0.95 |COCO mAP
@0.5
--- | --- | --- | ---
@@ -153,7 +158,7 @@ Speed: 17.5/2.3/19.9 ms inference/NMS/total per 640x640 image at batch-size 16
-# Reproduce Our Results
+## Reproduce Our Results
Run commands below. Training takes about one week on a 2080Ti per model.
```bash
@@ -162,17 +167,31 @@ $ python train.py --data coco2014.data --weights '' --batch-size 32 --cfg yolov3
```
-# Reproduce Our Environment
+
+## 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.sandbox.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb)
-- **Docker Image** from https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart)
+- **Google Colab Notebook** with 12 hours of free GPU time.
+- **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart)
+
+
# Citation
[![DOI](https://zenodo.org/badge/146165888.svg)](https://zenodo.org/badge/latestdoi/146165888)
-# Contact
-**Issues should be raised directly in the repository.** For business inquiries or professional support requests please visit us at https://contact.ultralytics.com.
+## 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** surveillance 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.