rembg VS detectron2

A bit conflicted as the linked video is also linked from the actual rembg repo but it seems way faster and more detailed to just read the readme at that repo first, and maybe use a video if something doesn’t make sense.

There are open source tools like rembg (https://github.com/danielgatis/rembg) which call into pre-trained models.

You can use Rembg extension to remove background automatically: https://github.com/danielgatis/rembg

Hi guys 📷 I am new to ml deployment. Can anyone help about production deployment ? I made fastapi docker app which removes background from image (https://github.com/danielgatis/rembg). It uses u2net segmentation. I tried aws ec2, lambda, googld cloudrun and so far ec2(t2-large) is the fastest but still too slow. Also, it costs way more than I expected. Are there any other solution that I can deploy ml app with as low as possible? Where do you guys mostly deploy ml app?

I am currently using rembg https://github.com/danielgatis/rembg

rembg

A python script with rembg could work

4.) rembg -- backgrounds be gone. If there isn't an extension in Automatic1111 for this yet, there should be (I haven't checked recently). Same deal as midas, you can point it at a folder and zap the backgrounds off of all your images. Useful in combo with midas and imagemagick if, for instance, you want images of an object on a white background (Stable Diffusion training via LoRAs/Dreambooth may not benefit, but other things like GANs prefer that sort of training image). Useful if you want to "compose" a scene and you have images of dispirate objects/people you want in that scene.

Ran into another issue with " LoadLibrary failed with error 126 Here's the solution: https://github.com/danielgatis/rembg/issues/312

I don’t do AI professionally but as a hobby, so this may not be the best way. But the way you described, it seems the user maybe taking the picture a bit further away and there may be other objects in the frame. So you may want to look into some sort of segmentation or have bounding box. This could help the user make sure they are looking at documents for the correct machine.

I think something like detectron2 [1] could help. It is Apache2 license, so commercial friendly. That said the pre-trained weights may not be used for commercial purposes, so you’ll want to check on that.

[1] https://github.com/facebookresearch/detectron2

And not enough true positives either. Add more augmentations in the config. Also make sure the config is set correctly, so that Detectron2 isn't skipping background images: https://github.com/facebookresearch/detectron2/issues/80

I followed the setup of https://github.com/facebookresearch/detectron2/tree/main/projects/TensorMask. But still I can not import it. As I can with from detectron2.projects import point_rend easily from PointRend projects

I have to use this config file with the dataloader which is in https://github.com/facebookresearch/detectron2/blob/main/projects/MViTv2/configs/common/coco_loader.py. I figured that i can use cfg.dataloader.train.dataset.names = "my_dataset_train" for this.

I want to use this config file https://github.com/facebookresearch/detectron2/blob/main/projects/MViTv2/configs/mask_rcnn_mvitv2_t_3x.py like the beneath typical way I use a yaml config file. But giving so many errors one after another that, I even failed to count at this point.

Which is built on https://github.com/facebookresearch/detectron2

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Detectron2 (GitHub: https://github.com/facebookresearch/detectron2) is a Facebook AI Research library with state-of-the-art object detection and segmentation algorithms in PyTorch.