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Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers. ## Motivation Support CAT-Seg open-vocabulary semantic segmentation (CVPR2023). ## Modification Support CAT-Seg open-vocabulary semantic segmentation (CVPR2023). - [x] Support CAT-Seg model training. - [x] CLIP model based `backbone` (R101 & Swin-B), aggregation layers based `neck`, and `decoder` head. - [x] Provide customized coco-stuff164k_384x384 training configs. - [x] Language model supports for `open vocabulary` (OV) tasks. - [x] Support CLIP-based pretrained language model (LM) inference. - [x] Add commonly used prompts templates. - [x] Add README tutorials. - [x] Add zero-shot testing scripts. **Working on the following tasks.** - [x] Add unit test. ## BC-breaking (Optional) Does the modification introduce changes that break the backward-compatibility of the downstream repos? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR. ## Use cases (Optional) If this PR introduces a new feature, it is better to list some use cases here, and update the documentation. ## Checklist 1. Pre-commit or other linting tools are used to fix the potential lint issues. 2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. 3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. 4. The documentation has been modified accordingly, like docstring or example tutorials. --------- Co-authored-by: xiexinch <xiexinch@outlook.com>
Projects
The OpenMMLab ecosystem can only grow through the contributions of the community. Everyone is welcome to post their implementation of any great ideas in this folder! If you wish to start your own project, please go through the example project for the best practice. For common questions about projects, please read our faq.
External Projects
There are also selected external projects released in the community that use MMSegmentation:
- SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
- Vision Transformer Adapter for Dense Predictions
- UniFormer: Unifying Convolution and Self-attention for Visual Recognition
- Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation
- ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias
- DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
- MPViT : Multi-Path Vision Transformer for Dense Prediction
- TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation
Note: These projects are supported and maintained by their own contributors. The core maintainers of MMSegmentation only ensure the results are reproducible and the code quality meets its claim at the time each project was submitted, but they may not be responsible for future maintenance.