fix README.md

README.md

@@ -1,64 +1,77 @@
 ---
 library_name: transformers
-tags: []
+tags: [transformers, pipeline, vision, image-classification, vit, imagenet-1k]
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for tmp-pl-image-classification
 
+이 저장소는 🤗 Transformers의 `pipeline()` 동작을 이해하고 연습하기 위한 **학습용(pipeline practice) 모델 repo**입니다.  
+모델 가중치는 원본 모델 **`google/vit-base-patch16-224`**을 그대로 사용하며, 추가적인 fine-tuning은 수행하지 않았습니다.
 
+---
 
 ## Model Details
 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
+본 모델은 **Vision Transformer(ViT)** 기반 이미지 분류 모델을 `pipeline("image-classification")` 형태로  
+Hub에 업로드하고 다시 불러오는 전체 흐름을 실습하기 위해 구성됨.
 
-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]
+- **Developed by:** Google Research (원본모델)
+- **Shared by [optional]:** dsaint31
+- **Model type:** Image Classification (Vision Transformer)
+- **Language(s) (NLP):** 해당 없음 (이미지 입력)
+- **License:** Apache-2.0
+- **Finetuned from model [optional]:** google/vit-base-patch16-224 (가중치 변경 없음. fine-tuning 미수행)
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Baee model Repository:** [https://huggingface.co/google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224)
+- **Paper [optional]:** [Dosovitskiy et al., *An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale*, arXiv:2010.11929](https://arxiv.org/abs/2010.11929)
+- **Demo [optional]:** None
 
 ## 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]
+- `pipeline("image-classification", model=...)` 사용법 실습
+- Hugging Face Hub에 pipeline 형태로 모델을 업로드 / 다운로드하는 흐름 이해
+- Vision 모델과 pipeline의 관계 학습
 
 ### 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]
+- 본 repo 자체는 downstream task를 위한 fine-tuning을 목적으로 하지 않습니다.
+- 학습 또는 성능 비교 목적이라면 **원본 모델 repo**를 직접 사용하는 것이 적절합니다.
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
+- 모델 성능 평가 또는 벤치마크
+- 실제 서비스 환경에서의 모델 배포
+- 특정 도메인(의료, 산업 영상 등)에 대한 신뢰성 있는 추론
+
 [More Information Needed]
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
+- 본 모델은 ImageNet 기반 데이터로 학습된 일반 목적 이미지 분류 모델의 특성을 그대로 가집니다.
+- 특정 객체, 문화적 맥락, 전문 도메인에 대한 분류 성능은 보장되지 않습니다.
+- 본 repo는 **연습용 pipeline 저장소**이므로 모델의 사회적 영향이나 편향 분석을 목적으로 하지 않습니다.
+
 [More Information Needed]
 
 ### Recommendations
@@ -67,10 +80,31 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
+- 실제 사용 목적이 있는 경우, 원본 모델 카드(`google/vit-base-patch16-224`)의 제한 사항을 반드시 참고하십시오.
+- 이 repo는 학습 및 실습 목적에 한해 사용하기를 권장합니다.
+
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
+아래 예제는 Hugging Face `pipeline`을 이용해 본 모델을 로드하고 이미지를 분류하는 최소 예제입니다.
+
+```python
+from transformers import pipeline
+from PIL import Image
+import requests
+
+img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png"
+image = Image.open(requests.get(img_url, stream=True).raw)
+
+clf = pipeline(
+    task="image-classification",
+    model="dsaint31/tmp-pl-image-classification",
+)
+
+print(clf(image))
+```
+
 [More Information Needed]
 
 ## Training Details
@@ -79,6 +113,9 @@ Use the code below to get started with the model.
 
 <!-- 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. -->
 
+* 본 repo에서는 **추가 학습을 수행하지 않았습니다.**
+* 원본 모델은 ImageNet-21k로 사전학습(pretraining) 후 ImageNet-1k로 fine-tuning된 모델입니다.
+
 [More Information Needed]
 
 ### Training Procedure
@@ -87,12 +124,17 @@ Use the code below to get started with the model.
 
 #### Preprocessing [optional]
 
+* 원본 ViT 모델의 기본 이미지 전처리(Image Processor)를 그대로 사용합니다.
+
+
 [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 -->
+- **Training regime:** 해당 없음(학습 미수행) [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]
 
@@ -110,6 +152,9 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card if possible. -->
 
+* 본 repo에서는 별도의 평가를 수행하지 않았습니다.
+* 성능 지표는 원본 모델 카드의 평가 결과를 참고하십시오.
+
 [More Information Needed]
 
 #### Factors
@@ -142,6 +187,9 @@ Use the code below to get started with the model.
 
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 
+본 repo에서는 학습을 수행하지 않았으므로 추가적인 환경적 영향은 없습니다.
+* 원본 모델 학습에 대한 환경 영향은 base model 문서를 참고하십시오.
+
 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]
@@ -154,6 +202,9 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ### Model Architecture and Objective
 
+* Vision Transformer (ViT-Base, patch size 16, input resolution 224x224)
+* Objective: Image classification
+
 [More Information Needed]
 
 ### Compute Infrastructure
@@ -162,18 +213,34 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 #### Hardware
 
+* 해당없음 (학습 미수행)
 [More Information Needed]
 
 #### Software
 
+* Transformers
+* Pillow
+* PyTorch
+
 [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. -->
 
+원본 모델 인용 시 아래 논문을 참고하십시오.
+
 **BibTeX:**
 
+```bibtex
+@article{dosovitskiy2020image,
+  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
+  author={Dosovitskiy, Alexey and others},
+  journal={arXiv preprint arXiv:2010.11929},
+  year={2020}
+}
+```
+
 [More Information Needed]
 
 **APA:**
@@ -192,8 +259,12 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Authors [optional]
 
+* dsaint31 (pipeline practice repository)
+
 [More Information Needed]
 
 ## Model Card Contact
 
-[More Information Needed]
+* Hugging Face profile: [https://huggingface.co/dsaint31](https://huggingface.co/dsaint31)
+
+[More Information Needed]