Usage

Remove the background from your photo.

• Method :
1. Method_1 : Quick conversion
2. Method_2 : Normal conversion
3. Method_3 : Object-oriented
4. Method_4 : Character-oriented
• Background :
1. White : white background
2. Black : black background
3. Transparent : transparent background
• File : Select the photo to change. You can also enter an image URL in the input field (Example:https://i.pinimg.com/564x/94/7a/5c/ABCDEFGHIJKLMNOPQRSTUVWXYZ.jpg)
• Try it : Convert start button

Description

This feature uses the u2net AI model.

U2Net is a deep learning-based model, which is used for image segmentation tasks. 'U2Net: Going Deeper with Nested U-Structure for Salient Object Detection.' This model shows excellent performance mainly in the task of accurately separating object boundaries.

U2Net is based on the U-Net structure. -Net is a widely used architecture in image segmentation, it is a neural network consisting of an encoder and a decoder, where the encoder downsamples the input image to extract feature maps, and the decoder upsamples these feature maps to generate segmentation maps. has been improved to further extend the structure of U-Net to obtain more accurate segmentation results.

U2Net has also introduced a nested U-Structure to improve performance. The steed structure can effectively utilize detailed information and global context information by nesting different sized U-Net modules for each encoder and decoder.

U2Net works with large image datasets and corresponding It uses the image's bounding box, or pixel-by-pixel label, which gives the model the ability to learn the boundaries and key features of an object and distinguish it from the surrounding background.

U2Net is primarily used in the computer vision field. It is used in a wide range of applications including object detection, object tracking, image segmentation, etc. Using this model, accurate and refined object separation results can be obtained, and automation and efficiency can be improved for computer vision tasks.