Comments (4)
Copy-paste requires masks, it's designed for use with instance segmentation not object detection. Are you trying to take the entire contents inside of a bounding box and paste them into another image? I expect that would make object detection performance worse, but if you want to try it, it is possible.
You'll have to create masks from the bounding boxes that you have. Code like this would go into load_example
in coco.py.
image = load_image(index) #some image (H, W, 3)
bboxes = load_bboxes(index) #some bounding boxes (N, 4)
bbox_classes = load_bbox_classes(index) #(N,) object classes
masks = []
copy_paste_bboxes = []
for ix, (bbox, bbox_class) in enumerate(zip(bboxes, bbox_classes)):
mask = np.zeros(image.shape[:2], dtype=np.bool)
ymin, xmin, ymax, xmax = bbox[:4] #this is pascal format, not coco
mask[ymin:ymax, xmin:xmax] = True #a rectangle
masks.append(mask)
copy_paste_bboxes.append(bbox + (bbox_class, ix)) #assumes bbox is a tuple, not a list
#apply transforms, etc. (see coco.py)
from copy-paste-aug.
@Auth0rM0rgan @conradry good news 😃! Your original issue may now be fixed ✅ in PR ultralytics/yolov5#3845. This YOLOv5 update implements copy-paste augmentation using the new copy_paste
hyperparameter (set from 0-1 for 0-100% of labels copied and pasted). Note this is only available for segment labels, not for box labels.
To receive this update:
- Git –
git pull
from within youryolov5/
directory orgit clone https://github.com/ultralytics/yolov5
again - PyTorch Hub – Force-reload with
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)
- Notebooks – View updated notebooks
- Docker –
sudo docker pull ultralytics/yolov5:latest
to update your image
Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!
from copy-paste-aug.
Hello,
I have an add-on to this question.
How do shadows affect the performance when using copy-paste augmentation?
Lets say I have a dataset consisting of 10 differently colored marbles in high definition. For training purposes, the marbles have been laid out on 10 differently colored backgrounds.
In the test set, all marbles can appear on all backgrounds. In the training set, each marble only appears on its own colored background. Unfortunately, the model appears to be learning the color-keyed backgrounds.
If I use copy-paste augmentation, but I do not bring the shadows produced by the marbles, will it work?
from copy-paste-aug.
@Auth0rM0rgan @conradry good news 😃! Your original issue may now be fixed ✅ in PR ultralytics/yolov5#3845. This YOLOv5 update implements copy-paste augmentation using the new
copy_paste
hyperparameter (set from 0-1 for 0-100% of labels copied and pasted). Note this is only available for segment labels, not for box labels.To receive this update:
* **[Git](https://github.com/ultralytics/yolov5)** – `git pull` from within your `yolov5/` directory or `git clone https://github.com/ultralytics/yolov5` again * **[PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/)** – Force-reload with `model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)` * **[Notebooks](https://github.com/ultralytics/yolov5/blob/master/tutorial.ipynb)** – View updated notebooks [![Open In Colab](https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) [![Open In Kaggle](https://camo.githubusercontent.com/a08ca511178e691ace596a95d334f73cf4ce06e83a5c4a5169b8bb68cac27bef/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667)](https://www.kaggle.com/ultralytics/yolov5) * **[Docker](https://hub.docker.com/r/ultralytics/yolov5)** – `sudo docker pull ultralytics/yolov5:latest` to update your image [![Docker Pulls](https://camo.githubusercontent.com/280faedaf431e4c0c24fdb30ec00a66d627404e5c4c498210d3f014dd58c2c7e/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f756c7472616c79746963732f796f6c6f76353f6c6f676f3d646f636b6572)](https://hub.docker.com/r/ultralytics/yolov5)
Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!
What changes should be implemented in augmentations.py if i am using coco2017 dataset for training?
from copy-paste-aug.
Related Issues (18)
- AttributeError: 'CopyPaste' object has no attribute '_to_dict'
- The example script is no use for my aerial image
- How do I use copy-paste-aug in instance segmentation HOT 1
- how to integrate with detectron2? HOT 1
- RandomScale of albumentations drops "bboxes" content in coco annotations HOT 1
- Torchvision Dataset Integration HOT 1
- Mosaic can't be used for Instance Segmentation?
- An error occurred while using Copy-Paste! HOT 2
- Facing issues with test dataset ../../datasets/coco/train2014/
- How to use copy-paste augmentation for YOLOX object detection
- How do I use this augment data? HOT 3
- Reproduce results in Paper HOT 3
- How to use this copy-paste for semantic segmentation HOT 3
- copy paste to background image HOT 2
- How can I use the copy-paste augmentation without albumentation framework? HOT 1
- How To Visualize Augmented Images in As presented in Fig 2. HOT 1
- TypeError: __init__() takes from 3 to 5 positional arguments but 6 were given. HOT 2
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from copy-paste-aug.