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MaureenZOU 2023-10-04 16:57:27 -05:00
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@ -4,12 +4,12 @@ We introduce **SEEM** that can **S**egment **E**verything **E**verywhere with **
:grapes: \[[Read our arXiv Paper](https://arxiv.org/pdf/2304.06718.pdf)\]   :apple: \[[Try our Demo](http://semantic-sam.xyzou.net:6090/)\]
**One-Line Demo with Linux:**
:fire: **One-Line Demo with Linux:**
```sh
git clone git@github.com:UX-Decoder/Segment-Everything-Everywhere-All-At-Once.git && sh aasets/scripts/run_demo.sh
```
**Getting Started:**
:fire: **Getting Started:**
[INSTALL.md](assets/readmes/INSTALL.md) <br>
[DATASET.md](assets/readmes/DATASET.md) <br>
@ -32,19 +32,6 @@ git clone git@github.com:UX-Decoder/Segment-Everything-Everywhere-All-At-Once.gi
**SEEM_v0:** Supporting Single Interactive object training and inference <br>
**SEEM_v1:** Supporting Multiple Interactive objects training and inference
:fire: **Related projects:**
* [FocalNet](https://github.com/microsoft/FocalNet) and [DaViT](https://github.com/dingmyu/davit) : We used FocalNet and DaViT as the vision backbones.
* [UniCL](https://github.com/microsoft/UniCL) : We used unified contrastive learning technique for learning image-text representations.
* [X-Decoder](https://github.com/microsoft/X-Decoder) : We built SEEM based on X-Decoder which is a generalist decoder that can do multiple tasks with one model only.
:fire: **Other projects you may find interesting:**
* [Semantic-SAM](https://github.com/UX-Decoder/Semantic-SAM), a universal image segmentation model to enable segment and recognize anything at any desired granularity
* [OpenSeed](https://github.com/IDEA-Research/OpenSeeD) : Strong open-set segmentation methods.
* [Grounding SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything) : Combining Grounding DINO and Segment Anything; [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO): A strong open-set detection model.
* [X-GPT](https://github.com/microsoft/X-Decoder/tree/xgpt) : Conversational Visual Agent supported by X-Decoder.
* [LLaVA](https://github.com/haotian-liu/LLaVA) : Large Language and Vision Assistant.
## :rocket: Updates
* **[2023.07.27]** :roller_coaster: We are excited to release our [X-Decoder](https://github.com/microsoft/X-Decoder) training code! We will release its descendant SEEM training code very soon!
* **[2023.07.10]** We release [Semantic-SAM](https://github.com/UX-Decoder/Semantic-SAM), a universal image segmentation model to enable segment and recognize anything at any desired granularity. Code and checkpoint are available!
@ -155,18 +142,21 @@ In the following figure, we compare the levels of interaction and semantics of t
## :bookmark_tabs: Catelog
- [x] SEEM Demo
- [x] Inference and Installation Code
- [ ] (Soon) Evaluation Code
- [ ] (TBD When) Training Code
- [x] Evaluation Code
- [x] Training Code
:fire: **Related projects:**
* [FocalNet](https://github.com/microsoft/FocalNet) and [DaViT](https://github.com/dingmyu/davit) : We used FocalNet and DaViT as the vision backbones.
* [UniCL](https://github.com/microsoft/UniCL) : We used unified contrastive learning technique for learning image-text representations.
* [X-Decoder](https://github.com/microsoft/X-Decoder) : We built SEEM based on X-Decoder which is a generalist decoder that can do multiple tasks with one model only.
:fire: **Other projects you may find interesting:**
* [Semantic-SAM](https://github.com/UX-Decoder/Semantic-SAM), a universal image segmentation model to enable segment and recognize anything at any desired granularity
* [OpenSeed](https://github.com/IDEA-Research/OpenSeeD) : Strong open-set segmentation methods.
* [Grounding SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything) : Combining Grounding DINO and Segment Anything; [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO): A strong open-set detection model.
* [X-GPT](https://github.com/microsoft/X-Decoder/tree/xgpt) : Conversational Visual Agent supported by X-Decoder.
* [LLaVA](https://github.com/haotian-liu/LLaVA) : Large Language and Vision Assistant.
## :cupid: Acknowledgements
- We appreciate hugging face for the GPU support on demo!
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