Data Augmentation
Data Augmentation is a technique where additional information is gathered to enhance the existing data to enlarge or enhance the quality of the dataset. This is usually done by customizing the existing data with a slightly modified copy of data. This helps to enhance the data set by acting as a regularizer and helps to reduce the overfitting during implementation to a machine learning model.
Synthetic Augmentation
This type of Augmentation can be used provide new dimension to your already available datasets. Apart from doing procedural stuffs like Scaling, Rotating and Blurs, this type of augmentation can replace Textures, Colours, Mood and even adding some fine grain details to provide it a new dimension.
Use cases
This can be applied to every computer vision to add some realistic and applicable correction to make as a new one to processed by your algorithm.
Even the impossibles or the scarce data issue can be addressed by this augmentation techniques, We strongly recommend this service prior the procedural augmentation to give you more dimension to your data
- Adding wear and tears to the machinery
- Changing colour or Texture of a Dress
- Changing the mood or lighting of a object.
- Jewellery / Garments alignment for a Subject
Procedural Augumentation
This method of Data Augmentation is a common technique mainly used for increasing the size & diversifying the labeled training sets by making the maximum use of the available data. This method supports all the interface right from classification, semantic segmentation, instance segmentation to Object estimation.
Use Cases
This technique multiplies your data based on a filter or Parameters requested for.
Along with data augmentation our team will validate the for any issues in augumenting the data, It was pretty common to have some bounding box misplaced or coverage doesn’t have enough data. This would drastically affect the accuracy of your model.
- Procudural
- Manual Quality Checks on request
- 90+ Filters
