Multi-task yolov5 with detection and segmentation based on yolov5(branch v6.0)
- decoupled head
- anchor free
- segmentation head
All experiments is trained on a small dataset with 47 classes ,2.6k+ images for training and 1.5k+ images for validation:
| model | P | R | [email protected] | [email protected]:95 |
|---|---|---|---|---|
| yolov5s | 0.536 | 0.368 | 0.374 | 0.206 |
| yolov5s+train scrach | 0.452 | 0.314 | 0.306 | 0.152 |
| yolov5s+decoupled head | 0.555 | 0.375 | 0.387 | 0.214 |
| yolov5s + decoupled head+class balance weights | 0.541 | 0.392 | 0.396 | 0.217 |
| yolov5s + decoupled head+class balance weights | 0.574 | 0.386 | 0.403 | 0.22 |
| yolov5s + decoupled head+seghead | 0.533 | 0.383 | 0.396 | 0.212 |
The baseline model is yolov5s. triks like decoupled head, add class balance weights all help to improve MAP.
Adding a segmentation head can still get equivalent MAP as single detection model.
python trainds.py
As VOC dataset do not offer the box labels and mask labels for all images, so we forward this model with a detection batch and a segmention batch by turns, and accumulate the gradient , than update the whole model parameters.
To compare with the SSD512, we use VOC07+12 training set as the detection training set, VOC07 test data as detection test data, for segmentation ,we use VOC12 segmentation datset as training and test set.
the input size is 512(letter box).
| model | VOC2007 test |
|---|---|
| SSD512 | 79.8 |
| yolov5s+seghead(512) | 79.2 |
The above results only trained less than 200 epoch, weights
see detectds.py.
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Use labelme to label box and mask on your dataset;
the box label format is voc, you can use voc2yolo.py to convert to yolo format,
the mask label is json files , you should convert to mask .png image labels,like VOC2012 segmentation labels.
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see how to arrange your detection dataset with yolov5 , then arrange your segmentation dataset same as yolo files , see data/voc.yaml:
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] path: . # dataset root dir train: VOC/det/images/train # train images (relative to 'path') 118287 images val: VOC/det/images/test # train images (relative to 'path') 5000 images road_seg_train: VOC/seg/images/train # road segmentation data road_seg_val: VOC/seg/images/val # Classes nc: 20 # number of classes segnc: 20 names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] # class names segnames: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']- change the config in trainds.py and :
python trainds.py-
test image folder with :
python detectds.py
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