change bounding box label index to text

[hjsg1010]

❔Question

Hi, thanks for your great research and sharing codes.

I am applying YOLOv5 to my custom dataset.

And during learning, the mosaic section on wandb.ai shows the figure below.

However, I want to change the bbox label index to text label, such as '118' changes to 'cheesecake'.

What should I fix in code? or do I have to change my annotation?

Currently the data folder are consists of
data_folder/
┣ images/
┃ ┣ train/
┃ ┣ valid/
┣ labels/
┃ ┣ train/
┃ ┣ valid/
┣ data_config.yaml

Additional context

[github-actions]

👋 Hello @hjsg1010, thank you for your interest in 🚀 YOLOv5! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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[glenn-jocher]

@hjsg1010 @kalenmike yes, your results are correct. The train mosaics only display the class index rather than the class name. You can see this in the Train Custom Data tutorial. We did this because the mosaics can get quite busy for high density datasets. The test dataset labels show the full name though, i.e. test_batch0_labels.jpg, so you may want to look at those instead.
https://docs.ultralytics.com/yolov5/tutorials/train_custom_data#local-logging

We're also developing dataset introspection tools as part of the new Ultralytics HUB. This should be ready sometime later on in summer 2021 at https://ultralytics.com

[hjsg1010]

@hjsg1010 @kalenmike yes, your results are correct. The train mosaics only display the class index rather than the class name. You can see this in the Train Custom Data tutorial. We did this because the mosaics can get quite busy for high density datasets. The test dataset labels show the full name though, i.e. test_batch0_labels.jpg, so you may want to look at those instead.
https://docs.ultralytics.com/yolov5/tutorials/train_custom_data#local-logging

We're also developing dataset introspection tools as part of the new Ultralytics HUB. This should be ready sometime later on in summer 2021 at https://ultralytics.com

oh, Thx for quick and kind reply.
Again, thx for sharing your codes!!

[hjsg1010]

@glenn-jocher
Hi, can I ask one more question?
I know that YOLO already does data augmentation.
However, I want to do custom data augmentation using Albumentations.
Then, where should I put augmentation pipeline code?
.utils/dataset.py ? or train.py?

sorry for my poor English skills, and low knowledge on object detection code..

[glenn-jocher]

@hjsg1010 you can inline any Albumentations augmentations you want in the dataloader here:

yolov5/utils/datasets.py

Lines 499 to 545 in 095197b

	def __getitem__(self, index):
	index = self.indices[index] # linear, shuffled, or image_weights
	hyp = self.hyp
	mosaic = self.mosaic and random.random() < hyp['mosaic']
	if mosaic:
	# Load mosaic
	img, labels = load_mosaic(self, index)
	shapes = None
	# MixUp https://arxiv.org/pdf/1710.09412.pdf
	if random.random() < hyp['mixup']:
	img2, labels2 = load_mosaic(self, random.randint(0, self.n - 1))
	r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
	img = (img * r + img2 * (1 - r)).astype(np.uint8)
	labels = np.concatenate((labels, labels2), 0)
	else:
	# Load image
	img, (h0, w0), (h, w) = load_image(self, index)
	# Letterbox
	shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape
	img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
	shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling
	labels = self.labels[index].copy()
	if labels.size: # normalized xywh to pixel xyxy format
	labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
	if self.augment:
	# Augment imagespace
	if not mosaic:
	img, labels = random_perspective(img, labels,
	degrees=hyp['degrees'],
	translate=hyp['translate'],
	scale=hyp['scale'],
	shear=hyp['shear'],
	perspective=hyp['perspective'])
	# Augment colorspace
	augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
	# Apply cutouts
	# if random.random() < 0.9:
	# labels = cutout(img, labels)

[hjsg1010]

@hjsg1010 you can inline any Albumentations augmentations you want in the dataloader here:

yolov5/utils/datasets.py

Lines 499 to 545 in 095197b

	def __getitem__(self, index):
	index = self.indices[index] # linear, shuffled, or image_weights
	hyp = self.hyp
	mosaic = self.mosaic and random.random() < hyp['mosaic']
	if mosaic:
	# Load mosaic
	img, labels = load_mosaic(self, index)
	shapes = None
	# MixUp https://arxiv.org/pdf/1710.09412.pdf
	if random.random() < hyp['mixup']:
	img2, labels2 = load_mosaic(self, random.randint(0, self.n - 1))
	r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
	img = (img * r + img2 * (1 - r)).astype(np.uint8)
	labels = np.concatenate((labels, labels2), 0)
	else:
	# Load image
	img, (h0, w0), (h, w) = load_image(self, index)
	# Letterbox
	shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape
	img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
	shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling
	labels = self.labels[index].copy()
	if labels.size: # normalized xywh to pixel xyxy format
	labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
	if self.augment:
	# Augment imagespace
	if not mosaic:
	img, labels = random_perspective(img, labels,
	degrees=hyp['degrees'],
	translate=hyp['translate'],
	scale=hyp['scale'],
	shear=hyp['shear'],
	perspective=hyp['perspective'])
	# Augment colorspace
	augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
	# Apply cutouts
	# if random.random() < 0.9:
	# labels = cutout(img, labels)

Thx for reply. Thankyou.

[glenn-jocher]

@hjsg1010 see PR #3882 for a proposed automatic Albumentations integration.

[glenn-jocher]

@hjsg1010 good news 😃! Your original issue may now be fixed ✅ in PR #3882. This PR implements a YOLOv5 🚀 + Albumentations integration. The integration will automatically apply Albumentations transforms during YOLOv5 training if albumentations>=1.0.0 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 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.

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__, '1.0.0')  # version requirement

            self.transform = A.Compose([
                A.Blur(p=0.1),
                A.MedianBlur(p=0.1),
                A.ToGray(p=0.01)],
                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 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 🚀!