New YOLOv5 🚀 + Albumentations Official Integration!!

I’m super excited to announce our new YOLOv5 🚀 + Albumentations integration!! Now you can train the world’s best Vision AI models even better with custom Albumentations automatically applied 😃!

PR https://github.com/ultralytics/yolov5/pull/3882 implements this integration, which will automatically apply Albumentations transforms during YOLOv5 training if albumentations>=1.0.3 is installed in your environment.

Get Started

To use albumentations simply pip install -U albumentations and then update the augmentation pipeline as you see fit in the new Albumentations class in yolov5/utils/augmentations.py. Note these Albumentations operations run in addition to the YOLOv5 hyperparameter augmentations, i.e. defined in hyp.scratch.yaml.

Here’s an example that applies Blur, MedianBlur and ToGray albumentations in addition to the YOLOv5 hyperparameter augmentations normally applied to your training mosaics 😃

class Albumentations:
    # YOLOv5 Albumentations class (optional, used if package is installed)
    def __init__(self):
        self.transform = None
        try:
            import albumentations as A
            check_version(A.__version__, '1.0.3')  # version requirement

            self.transform = A.Compose([
                A.Blur(blur_limit=50, p=0.1),
                A.MedianBlur(blur_limit=51, p=0.1),
                A.ToGray(p=0.3)],
                bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))

            logging.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms))
        except ImportError:  # package not installed, skip
            pass
        except Exception as e:
            logging.info(colorstr('albumentations: ') + f'{e}')

    def __call__(self, im, labels, p=1.0):
        if self.transform and random.random() < p:
            new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0])  # transformed
            im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])])
        return im, labels

Example Result

Example train_batch0.jpg on COCO128 dataset with Blur, MedianBlur and ToGray. See the YOLOv5 Notebooks to reproduce:

Update

To receive this YOLOv5 update:

• Git – git pull from within your yolov5/ directory or git 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 your feedback and let us know if this update works for you, and of course feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀 + Albumentations!

[i-aki-y]

I checked what happened.

Default colab has:

opencv-contrib-python         4.1.2.30
opencv-python                 4.1.2.30

The albumentation requirements are given from the result of pkginfo -f requires_dist albumentations-1.1.0-py3-none-any.whl:

opencv-python-headless>=4.1.1

After the pip -u albumentations

opencv-contrib-python         4.1.2.30
opencv-python                 4.1.2.30
opencv-python-headless        4.5.5.64

So, multiple versions may cause conflicts.

I think there is no simple solution to this problem. The opencv team provides multiple package options and says, “SELECT ONLY ONE OF THEM.”

• opencv-python
• opencv-contrib-python
• opencv-python-headless
• opencv-contrib-python-headless (See also, https://pypi.org/project/opencv-python/ )

The package owner can not know which opencv package the user’s environment has installed or none of them. Unfortunately, the situation would be worse because there are environments where multiple openvcv packages have been installed, like Colab.

Installing all of them with the same version seems to work, but it introduces unnecessary and redundant dependencies. I think such behavior would not be wanted for users who use packages in production.

[BloodAxe]

Great work @glenn-jocher !