DL_Advanced_RabbitDetect

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DL_Advanced_RabbitDetect

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Object Detection & Segmentation with rabbit image dataset

// repository documentation

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# RabbitDetect 
Bulit on 2022/02 by Fred Liu  
Major update 2023.05.17  
New update 2023.12.07(YOLOX,SOLOv2)  
[Youtube Link](https://www.youtube.com/channel/UCnUuSyqkkXaFy57qL7aURAA)  
  
版本:MATALB: update to 2023b,minimum vervion 2022a.  
需要工具箱: Deeplearning , Image Processing, Computer Vision, Parallel Computing  
需要支援包: YOLOX,YOLOv3,YOLOv4 Package & pretrain modle Package  
[Computer Vision Toolbox Model for YOLO v3 Object Detection](https://www.mathworks.com/matlabcentral/fileexchange/87959-computer-vision-toolbox-model-for-yolo-v3-object-detection?s_tid=srchtitle)  
[Computer Vision Toolbox Model for YOLO v4 Object Detection](https://www.mathworks.com/matlabcentral/fileexchange/107969-computer-vision-toolbox-model-for-yolo-v4-object-detection?s_tid=srchtitle)  
YOLOX:  
[Computer Vision Toolbox Automated Visual Inspection Library](https://www.mathworks.com/matlabcentral/fileexchange/116555-computer-vision-toolbox-automated-visual-inspection-library?s_tid=ta_fx_results)  
MASK-RCNN  
[Computer Vision Toolbox Model for Mask R-CNN Instance Segmentation](https://www.mathworks.com/matlabcentral/fileexchange/98554-computer-vision-toolbox-model-for-mask-r-cnn-instance-segmentation?s_tid=prof_contriblnk)  
SOLOv2  
[Computer Vision Toolbox Model for SOLOv2 Instance Segmentation](https://www.mathworks.com/matlabcentral/fileexchange/131144-computer-vision-toolbox-model-for-solov2-instance-segmentation?s_tid=srchtitle)

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首先請閱讀setup_readme.m (First to read setup_readme )

因為內建資料庫資料較少，因此在訓練一些模型上效果可能較差，範例提供整體流程，但實作請換較大型的資料庫使用。
Due to the limited amount of data in the built-in database, the performance of some models may 
be poorer during training. The example provides the overall process, but for implementation, 
it is recommended to use a larger database.
  
![image](https://github.com/MoonUsagi/RabbitDetect/blob/main/rabbitLog_label.jpg)
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基於MATLAB 物件偵測於rabbit dataset   
(MATLAB Object Detection with rabbit dataset)
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- - -

1.資料請下載(data download):Rabbit_myself_416.zip or Rabbit_myself_608.zip  
2.已訓練模型(Pretain_model):model\Modeldownload  
3.演算法(algorithm):src_main\FasterRCNN,SSD,YLOLv2,YOLOv3,YOLOv4,YOLOX  
4.標記檔案(label data):Rabbit_myself_608.mat   
5.src_input: XMLinput , Jsoninput 
  
  
  
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使用流程(Use the process):
---------------------------------------
- - -
  
  
1.首先請閱讀setup_readme.m (First to read setup_readme )

2.標記影像：可使用image labeler標記　or 載入Rabbit_myselft_608標記資料(可使用Change_gTruthPath.m)    
(Label Image:use image labeler to label or download "Rabbit_myselft_608" label dataset.  
It can use "Change_gTruthPath.m" to change path.)

3.模型：可以自行透過演算法訓練(src_main)，也使用Pre-trained進行測試(model)  
(Model:you can train through the algorithm by yourself, and also use Pre-trained for testing)
  
  
- - -
基於MATLAB 語意分割於rabbit dataset  
(MATLAB semantic segmentation with rabbit dataset)
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- - -
1.資料請下載(data download):Rabbit_myself_416 or Rabbit_myself_608  
2.已訓練模型(Pretain_model):model\Modeldownload  
3.演算法(algorithm): SP_DeepLabv3  
4.標記檔案(label data):gTruth_Pixel_2.mat  
5.src_input:JsonSegInput.m,readFcn.m,readFcn2.m

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基於MATLAB 實例分割於coco dataset
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- - -
1.資料請下載(data download):https://github.com/cocodataset/cocoapi  
Download:"2014 Train images" and "2014 Train/Val annotations" links

2.已訓練模型(Pretain_model): NaN  
3.演算法(algorithm): SP_MaskRCNN,SP_SOLOv2  
4.標記檔案(label data):Using coco dataset/ gTruth_Instance.mat
5.src_input:Polygon2mask_bbox.m

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