Adobe Image Dataset License Agreement

Standard training requires 4 GPUs with 11GB of memory and the stack size is 10 per GPU. First, you need to set your training and validation data path in the configuration, and the data loader merges the training images on the fly: via the newly estimated foreground. I tried to re-evaluate the training models, but I only managed to do so in 411 out of 431 fg. 20 of them could not be optimized. Did you have the same problem? Also, when calculating the alpha* error in the foreground during the test, are the foreground images of the truth on the ground reestimated by closed form? Run the following command to perform the indexNet Matting/Deep Matting inference on the Adobe Image Matting record: Hi, I read your article on arxiv. In your work, the problem of paint spillage has been suggested and an example has been given in Fig.2. I tried to reproduce your result in Fig.2 with the same foreground in the Composition 1k dataset. But the composite image is completely different from yours. Also, I tried to use the closed form method to estimate a new foreground, but I got another one. My new fg is black in the area where alpha sucks.

It seems that you are using the original size of the DIM image for inference, and is it normal to see increasing memory usage during inference mode for the pre-trained DIM model and IndexNet mating? We have included our pre-trained model in ./pretrained and several images and trimaps from the Adobe Image dataset in ./examples. Run the following command for a quick demonstration of IndexNet mating. The derived alpha mats can be found in the ./examples/mattes folder. The results are still quite impressive, but this problem is becoming more and more evident with more complex images. Thank you. Their work is excellent and very useful. I tried to understand the license, but I`m confused because there are several different ones. I`m trying to apply the Fulfillment by Amazon matte method to remove the green screen from images (portraits of people taken on a green screen).

Overall, it works well, but it leaves a few green pixels mixed with the hair (I`ll include an example here). First of all, we increase the number of input channels from 3 to 9 to allow the additional trimap. We encode the tricarte with Gaussian blurs specific foreground and background masks at three different scales (similar to the method of [19] in interactive segmentation). This encoding differs from existing approaches to deep image matting because they typically encode the tricarte as a single channel with a value of 1 in the foreground, 0.5 for unknown, and 0 in the background. I want to form my own dataset to generate indexnet_matting.pth.tar. What technology should I explore to achieve this? Is it called image segmentation or would the best term be image matting? e.B. alphamatting.com/eval_25.php) I don`t know if I should first crop and then resize as described in Deep Image Matting, as each stack creates a few trimaps that have a 100% unknown area. Also, it is impossible to crop a 640×640 image from some alpha mats because they do not have unknown pixels on which to center the cropped area. I tried a few image segmentation SDKs with pre-trained models like Tensorflow Lite & Fritz AI, but the accuracy of the cutting mask was very low, among other things. For an input image and a hand-drawn tricarte (top line), alpha coupling estimates the alpha channel of a foreground object, which can then be assembled on a different background (bottom line).

In Table 1, the gradient exclusion loss is calculated in the foreground and background of the original image, but I think it should be calculated on the intended foreground and background, is that wrong? Overview This is a set of simple scripts to process the Imagenet 1K dataset as TFRecords and create index files for NVIDIA DALI. Make TFRecords To run t Mais conv_out[-6][:, :3] is the same as img. Are you sure that the image needs to be concatenated twice? I am interested in the function of the image_alignment(), which produces images with a width of 32. What for? Another question is that you train the model without defining a specific size? Since each alpha ground truth image in Composition-1K is shared by 20 merged images, we first copy and rename these alpha images so that they have the same name as their trimaps. If your ground truth images are in ./Combined_Dataset/Test_set/Adobe-licensed images/alpha, run the following command: As far as I know, github.com/MarcoForte/FBA_Matting/blob/master/networks/models.py#L230 backbone resnet returns such feature maps here: [original_image, conv_bn_relu out, layer1 out, layer2 out, layer3 out, layer3 out, layer4 out] To further increase the variety of recordings, we randomly define a new foreground object with a 50% probability together, as in [34]. . . .