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Xueyan Zou 2023-10-04 17:07:49 -05:00 committed by GitHub
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@ -4,19 +4,27 @@ 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:**
## :bookmark_tabs: Catalog
We release the following content for **both SEEM and X-Decoder**:exclamation:
- [x] Demo Code
- [x] Model Checkpoint
- [x] Comprehensive User Guide
- [x] Training Code
- [x] Evaluation Code
:point_right: **One-Line SEEM 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:**
:round_pushpin: *[New]* **Getting Started:**
[INSTALL.md](assets/readmes/INSTALL.md) <br>
[DATASET.md](assets/readmes/DATASET.md) <br>
[TRAIN.md](assets/readmes/TRAIN.md) <br>
[EVAL.md](assets/readmes/EVAL.md)
* [INSTALL.md](assets/readmes/INSTALL.md) <br>
* [DATASET.md](assets/readmes/DATASET.md) <br>
* [TRAIN.md](assets/readmes/TRAIN.md) <br>
* [EVAL.md](assets/readmes/EVAL.md)
:point_right: *[New]* **Latest Checkpoints and Numbers:**
:round_pushpin: *[New]* **Latest Checkpoints and Numbers:**
| | | | COCO | | | Ref-COCOg | | | VOC | | SBD | |
|-----------------|---------------------------------------------------------------------------------------------|----------|------|------|------|-----------|------|------|-------|-------|-------|-------|
| Method | Checkpoint | backbone | PQ &uarr; | mAP &uarr; | mIoU &uarr; | cIoU &uarr; | mIoU &uarr; | AP50 &uarr; | NoC85 &darr; | NoC90 &darr;| NoC85 &darr;| NoC90 &darr;|
@ -57,12 +65,6 @@ git clone git@github.com:UX-Decoder/Segment-Everything-Everywhere-All-At-Once.gi
* [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.
## :bookmark_tabs: Catelog
- [x] SEEM Demo
- [x] Inference and Installation Code
- [x] Evaluation Code
- [x] Training Code
## :bulb: Highlights
Inspired by the appealing universal interface in LLMs, we are advocating a universal, interactive multi-modal interface for any type of segmentation with **ONE SINGLE MODEL**. We emphasize **4** important features of **SEEM** below.
1. **Versatility**: work with various types of prompts, for example, clicks, boxes, polygons, scribbles, texts, and referring image;