81 questions
1
vote
0
answers
112
views
Roboflow Annotation for YOLO
We are using YOLO for object detection in satellite images to detect greenhouses.
I'm annotating images through Roboflow. I've downloaded satellite images with different zoom levels. (zoom level means ...
0
votes
0
answers
74
views
How can I get more images after augmenting each image?
I'm new to tensorflow. I want to generate more images after image augmentation but images and labels are (10, None, 400, 400, 3) and (10, None, 1) respectively and I guess it artifically count as 1 ...
- 9
0
votes
0
answers
56
views
the data augmentation doesn't work properly
according to me, the given code should create 5700 images, which is 10 times the number of original images, instead, when I check the shape of it, it only gives 1140, which is twice the number of ...
1
vote
1
answer
113
views
How to utilize the .experimental module in tensorflow without generating attribute error
I'm following a tutorial on data augmentation by aladdin persson. In the tutorial, some preprocessing modules in tensorflow were still being experimented as at time (3yrs ago) such as RandomFlip, ...
0
votes
1
answer
3k
views
'Image' object has no attribute 'shape'
I am trying to implement prime augmentation on my dataset. Previously I was getting error raise ValueError('pic should be 2/3 dimensional. Got {} dimensions.'.format(pic.ndim))
ValueError: pic should ...
- 17
0
votes
0
answers
491
views
ValueError: pic should be 2/3 dimensional. Got 4 dimensions
I am trying to implement an augmentation function to my images and masks, I have defined the augmentations like below:
if config.AUG == "PRIMEAugmentation":
augmentations = [autocontrast,...
- 17
0
votes
1
answer
83
views
RuntimeError: output with shape [320, 320, 3] doesn't match the broadcast shape [320, 320, 320, 320, 3]
I am trying to implement an augmentation function to my images and masks, I have defined the augmentations like below:
if config.AUG == "PRIMEAugmentation":
augmentations = [autocontrast,...
- 17
0
votes
1
answer
73
views
Image augmentation for dataset creation
I want to create an image dataset of leaf dataset. For this i captured images and found 6 classes, each classes has roughly 70-60 images. Noe while creating training and validation set, i know i ...
0
votes
1
answer
37
views
NameError: name 'pil_mask' is not defined
This is my code. I have defined various operations like this:
def identity(pil_img, pil_mask, _):
return pil_img, pil_mask
def autocontrast(pil_img, pil_mask, _):
return ImageOps.autocontrast(...
1
vote
2
answers
1k
views
How to normalize Imagenet dataset with pytroch?
I am trying to write a simple code to teach resnet50 on ImageNet dataset. I don't quite get why normalization does not work. When I use this transformation as data augmentation:
train_transforms = ...
- 65
0
votes
1
answer
602
views
How to achieve updated values on bounding box coordinates after Image Augmentation using albumentations?
I'm trying to expand the volume of my dataset using an image augmentation package called albumentations. I have 1145 images and their corresponding annotations labelled in Yolo format.
I'm having a ...
- 11
0
votes
1
answer
180
views
TF Keras - Image augmentation whilst excluding one class but recombining the data later on for use with an ImageDataGenerator object
I'm trying to augment the ISIC 2019 dataset images with 9 classes. The 'NV' class is overrepresented (12876 of a total of 25331 images) so I'd like to exclude it from the augmentation process but ...
- 21
1
vote
1
answer
758
views
Augmentation and transformations in Detectron2
I'm working on a custom Faster RCNN with Detectron2 framework and I have a doubt about transformation during training and inference. I created a custom Trainer inheriting from DefaultTrainer class and ...
1
vote
0
answers
1k
views
Data augmentation using keras.layers.RandomRotation() doesnt yield same results
In my project I have dataset of original images and correcponding masks. I want to perform augmentation so it is random, yet same for image and matching mask. For this I have defined keras sequential ...
- 11
0
votes
1
answer
368
views
Checking Data Augmentation in Pytorch
i used image augmentation in pytorch before training in unet like this
class ProcessTrainDataset(Dataset):
def __init__(self, x, y):
self.x = x
self.y = y
self.pre_process = transforms....
- 13