NOTE: Sample datasets must consist of a small sampling of images and be in ImageNet, Pascal Visual Object Classes (Pascal VOC), Common Objects in Context (COCO), Common Semantic Segmentation, Labeled Faces in the Wild (LFW), Visual Geometry Group Face 2 (VGGFace2), or unannotated format. To learn more about the formats, refer to Dataset Types.
At the bottom of the Create Project page, find the table with validation datasets and click Import:
To import a new dataset, click Upload Dataset tab. Upload a
.tar.gz archive with your dataset and put its name in the Dataset Name :
Click Import. You are automatically directed back to the datasets table, where you can see the status of the import and select the dataset:
You can upload your images to create not annotated dataset. If you do not have enough images or want to enlarge the dataset, use augmentation methods to increase the size of a dataset by generating modified image copies.
On the Create Project page, find the table with datasets and click Import:
You will see the Create Dataset page where you can add your own images and specify the dataset name:
After you click Import, you are redirected to the Create Project page where you can check the import status.
Apply different augmentation types to create variations of your images and improve the model performance. Extending your validation dataset also helps to avoid possible overfitting of a calibrated model. Augmentation methods include different image modifications, such as horizontal and vertical flips, random erase, noise injection, and color transformations.
Horizontal image flip means reversing the rows and columns of an image pixels horizontally. Usually it does not modify the object category.
Vertical image flip reverse the rows and columns of an image pixels vertically. It is recommended to use this method in the context of the selected image and model task to avoid recognition issues.
Random Erase randomly selects a rectangle section in the image and erases its pixels with random values. Note that this augmentation methon might randomly erase an object particularly important for your use case. It is recommended to use this method in the context of the selected image and model task.
Noise Injection means injecting a matrix of random values. Noise Injection presents itself as random black and white pixels spread through the image. This method helps to avoid overfitting when you model concentrates on the image patterns that occur frequently but may not be useful.
Color Transformations change brightness and contrast of the image. You can select one or several presets with changed parameters. The preset specifies whether the brightness of the augmented image will be lighter(+20%) or darker(-20%). Contrast is the degree to which light and dark colours in the image differ. You can make the constrast of the augmented image higher(+20%) or lower(-20%).
After clicking Import, you are redirected to the Create Project page where you can check the import status. To remove an imported dataset from the list, click the bin icon in the Action column.