2) Optimized the fba post-processing method for basnet, deeplabv3 models.
3) The deprecated deeplabv3 tensorflow models were removed instead of them one deeplabv3 model was added with the resnet101 backbone from the pytorch hub. Also now the project does not depend on tensorflow.
4) Improved trimap generators.
5) Removed deprecated tests. Also added testing of the program on different platforms (Windows, MacOS, Linux).
6) Added automatic creation of a Docker container after testing program and testing tool in it.
7) Fixed serious bugs in Flask/FastAPI http api. In particular, a memory overflow error with a large number of requests, an error with the processing of some parameters in a request when removing the background, an error in processing a system variable when working in a Docker container, etc.
8) Added a queue for requests in the FastAPI http api, also added a simple implementation of the Task Queue system with a time for deleting unclaimed server responses in 1 hour after processing.
9) All links, disclaimers have been added in CREDITS.md.
10) A large-scale work has been done with README.md. Added new example images, also written new examples for working with the FastAPI http api, updated information about building a docker container, updated information about the arguments of the console interface, updated information about installing the program, etc.
11) Updated project dependencies.
12) Completely rewritten code of framework.
13) The consumption of video memory and RAM is maximally optimized.
14) Optimized the code of post-processing methods. #29
15) Now the console interface accepts image paths as the user expects. #26
16) Also added a recursive search for images in a folder.
17) Dropped python 3.5 support.
18) Updated colab notebook.
19) Refactored Docker
20) Added separate Docker images building for CPU and GPU processing.
21) Models downloader now inside framework and downloads all automaticly to ~/.carvekit.
20) Refactored and rewrited the HTTP API to the FastAPI backend.
22) Updated and optimized post-processing methods and segmentation models to improve quality.
23) Added simple front-end to HTTP API.
24) Refactored demo code
25) Updated Github CI/CD workflows ymls
26) Added docker-compose files
27) Added good support for NVIDIA CUDA processing devices.
29) Added 100% test coverage of the main code.
Changes:
2) Optimized the fba post-processing method for basnet, deeplabv3 models. 3) The deprecated deeplabv3 tensorflow models were removed instead of them one deeplabv3 model was added with the resnet101 backbone from the pytorch hub. Also now the project does not depend on tensorflow. 4) Improved trimap generators. 5) Removed deprecated tests. Also added testing of the program on different platforms (Windows, MacOS, Linux). 6) Added automatic creation of a Docker container after testing program and testing tool in it. 7) Fixed serious bugs in Flask/FastAPI http api. In particular, a memory overflow error with a large number of requests, an error with the processing of some parameters in a request when removing the background, an error in processing a system variable when working in a Docker container, etc. 8) Added a queue for requests in the FastAPI http api, also added a simple implementation of the Task Queue system with a time for deleting unclaimed server responses in 1 hour after processing. 9) All links, disclaimers have been added in CREDITS.md. 10) A large-scale work has been done with README.md. Added new example images, also written new examples for working with the FastAPI http api, updated information about building a docker container, updated information about the arguments of the console interface, updated information about installing the program, etc. 11) Updated project dependencies. 12) Completely rewritten code of framework. 13) The consumption of video memory and RAM is maximally optimized. 14) Optimized the code of post-processing methods. #29 15) Now the console interface accepts image paths as the user expects. #26 16) Also added a recursive search for images in a folder. 17) Dropped python 3.5 support. 18) Updated colab notebook. 19) Refactored Docker 20) Added separate Docker images building for CPU and GPU processing. 21) Models downloader now inside framework and downloads all automaticly to ~/.carvekit. 20) Refactored and rewrited the HTTP API to the FastAPI backend. 22) Updated and optimized post-processing methods and segmentation models to improve quality. 23) Added simple front-end to HTTP API. 24) Refactored demo code 25) Updated Github CI/CD workflows ymls 26) Added docker-compose files 27) Added good support for NVIDIA CUDA processing devices. 29) Added 100% test coverage of the main code.