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@@ -1,22 +1,16 @@
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-# PyTorch_YOLO_Tutorial
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-YOLO Tutorial
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+# Real-time General Object Detection
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-English | [简体中文](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/blob/main/README_CN.md)
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-
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-# Introduction
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-Here is the source code for an introduction to YOLO. We adopted the core concepts of **YOLOv1~v4**, **YOLOX** and **YOLOv7** for this project and made the necessary adjustments. By learning how to construct the well-known YOLO detector, we hope that newcomers can enter the field of object detection without any difficulty.
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-
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-**Book**: The technical books that go along with this project's code is being reviewed, please be patient.
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+English | [简体中文](https://github.com/yjh0410/RT-ODLab/blob/main/README_CN.md)
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## Requirements
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- We recommend you to use Anaconda to create a conda environment:
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```Shell
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-conda create -n yolo python=3.6
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+conda create -n rtcdet python=3.6
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```
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- Then, activate the environment:
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```Shell
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-conda activate yolo
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+conda activate rtcdet
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```
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- Requirements:
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@@ -34,7 +28,7 @@ At least, please make sure your torch is version 1.x.
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### VOC
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- Download VOC.
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```Shell
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-cd <PyTorch_YOLO_Tutorial>
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+cd <RT-ODLab>
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cd dataset/scripts/
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sh VOC2007.sh
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sh VOC2012.sh
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@@ -42,7 +36,7 @@ sh VOC2012.sh
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- Check VOC
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```Shell
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-cd <PyTorch_YOLO_Tutorial>
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+cd <RT-ODLab>
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python dataset/voc.py
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```
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@@ -69,14 +63,14 @@ python train.py --cuda -d voc --root path/to/VOCdevkit -m yolov1 -bs 16 --max_ep
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- Download COCO.
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```Shell
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-cd <PyTorch_YOLO_Tutorial>
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+cd <RT-ODLab>
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cd dataset/scripts/
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sh COCO2017.sh
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```
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- Check COCO
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```Shell
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-cd <PyTorch_YOLO_Tutorial>
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+cd <RT-ODLab>
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python dataset/coco.py
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```
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yjh0410