Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+  - vision
+  - image-segmentation
+  - universal-segmentation
+  - korean-road
+  - oneformer
+  - distillation
+  - aihub
+license: cc-by-4.0
+model_name: KoalaSeg-Edge-ViT
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# KoalaSeg-Edge-ViT 🐨🛣️  
+**KoalaSeg = _KOrean lAyered assistive Segmentation_**  
 
+한국 도로 · 보행 환경 전용 **Universal Segmentation** 모델입니다.  
+3-중 레이어 마스크(XML 폴리곤 ▶ AIHUB 도로보행 데이터로 학습한 한국 도로 전용 모델 ▶ OneFormer-Cityscapes)를 겹쳐 만든 통합 GT로 파인튜닝한 OneFormer Edge-ViT 학생 버전입니다.
 
+---
 
 ## Model Details
+| 항목 | 내용 |
+|------|------|
+| **Developed by** | Team RoadSight |
+| **Model type** | Edge-ViT backbone + OneFormer head<br>(semantic-only task token) |
+| **Finetuned from** | `shi-labs/oneformer_cityscapes_swin_large` |
+| **Framework** | 🤗 Transformers v4.41 / PyTorch 2.3 |
+| **License** | CC BY 4.0 |
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
+---
 
-**APA:**
+## Training Data
+| 출처 | 수량 | 주석 방식 |
+|------|------|-----------|
+| **AIHUB 도로·보행환경** <br> (도로 차선, 인도, 횡단보도) | 5 615 장 | 공식 pixel-wise GT |
+| 자가 촬영 지방도 | 9 042 장 | CVAT XML 폴리곤 |
+| Street View 파생 | 3 712 장 | OneFormer-Cityscapes pseudo-mask |
+| **총합** | **18 369 장** | 3-중 레이어 합성 → Morph Open/Close + MedianBlur(17 px) |
 
-[More Information Needed]
+---
 
-## Glossary [optional]
+## Speeds & Sizes   *(512 × 512 batch 1)*
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+| Device | Baseline Cityscapes | Ensemble(3-레이어) | Custom(K-Road) | **KoalaSeg(ft)** |
+|--------|--------------------|-------------------|---------------|------------------|
+| **A100**   | 3.58 s → 0.28 FPS | 3.74 s → 0.27 FPS | 0.15 s → 6.67 FPS | **0.14 s → 7.25 FPS** |
+| **T4**     | 5.61 s → 0.18 FPS | 6.01 s → 0.17 FPS | 0.39 s → 2.60 FPS | **0.31 s → 3.27 FPS** |
+| **CPU (i9-12900K)** | 124 s | 150 s | 26.6 s | **18.4 s** |
 
-[More Information Needed]
+---
 
-## More Information [optional]
+## Evaluation (국내 테스트셋)
+| Metric | Baseline | **KoalaSeg** |
+|--------|----------|--------------|
+| mIoU (전체 클래스) | 0.55 | **0.81** |
+| F1 – 도로 vs 인도 | 0.58 | **0.89** |
 
-[More Information Needed]
+---
 
-## Model Card Authors [optional]
+## Quick Start
+```python
+from transformers import AutoProcessor, AutoModelForUniversalSegmentation
+import torch, numpy as np, matplotlib.pyplot as plt
+from PIL import Image
 
-[More Information Needed]
+model_id = "roadsight/KoalaSeg-Edge-ViT"
+proc  = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForUniversalSegmentation.from_pretrained(model_id).to("cuda")
 
-## Model Card Contact
+img   = Image.open("korean_road.jpg").convert("RGB")
+inputs = proc(images=img, task_inputs=["semantic"], return_tensors="pt").to("cuda")
+with torch.no_grad():
+    out = model(**inputs)
 
-[More Information Needed]
+idmap = proc.post_process_semantic_segmentation(out, target_sizes=[img.size[::-1]])[0]
+plt.imshow(idmap.cpu()); plt.axis("off"); plt.show()