MhRnd
/

ConvNext_Multi

@@ -1,244 +1,124 @@
 ---
 ---
-# Model Card for ConvNext_Multi
-<!-- Provide a quick summary of what the model is/does. [Optional] -->
-다중분광 영상 데이터를 입력으로 받아 작물의 생육 조건 분류 작업을 수행하는 최신 ConvNeXt 기반 딥러닝 모델입니다. 효율적인 멀티밴드 특성 학습을 통해 다중분광 이미지 분석에 최적화되어 있습니다.
-#  Table of Contents
-- [Model Card for ConvNext_Multi](#model-card-for--model_id-)
-- [Table of Contents](#table-of-contents)
-- [Table of Contents](#table-of-contents-1)
-- [Model Details](#model-details)
-  - [Model Description](#model-description)
-- [Uses](#uses)
-  - [Direct Use](#direct-use)
-  - [Downstream Use [Optional]](#downstream-use-optional)
-  - [Out-of-Scope Use](#out-of-scope-use)
-- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
-  - [Recommendations](#recommendations)
-- [Training Details](#training-details)
-  - [Training Data](#training-data)
-  - [Training Procedure](#training-procedure)
-    - [Preprocessing](#preprocessing)
-    - [Speeds, Sizes, Times](#speeds-sizes-times)
-- [Evaluation](#evaluation)
-  - [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
-    - [Testing Data](#testing-data)
-    - [Factors](#factors)
-    - [Metrics](#metrics)
-  - [Results](#results)
-- [Model Examination](#model-examination)
-- [Environmental Impact](#environmental-impact)
-- [Technical Specifications [optional]](#technical-specifications-optional)
-  - [Model Architecture and Objective](#model-architecture-and-objective)
-  - [Compute Infrastructure](#compute-infrastructure)
-    - [Hardware](#hardware)
-    - [Software](#software)
-- [Citation](#citation)
-- [Glossary [optional]](#glossary-optional)
-- [More Information [optional]](#more-information-optional)
-- [Model Card Authors [optional]](#model-card-authors-optional)
-- [Model Card Contact](#model-card-contact)
-- [How to Get Started with the Model](#how-to-get-started-with-the-model)
-# Model Details
-## Model Description
-<!-- Provide a longer summary of what this model is/does. -->
-다중분광 영상 데이터를 입력으로 받아 작물의 생육 조건 분류 작업을 수행하는 최신 ConvNeXt 기반 딥러닝 모델입니다. 효율적인 멀티밴드 특성 학습을 통해 다중분광 이미지 분석에 최적화되어 있습니다.
-- **Developed by:** More information needed
-- **Shared by [Optional]:** More information needed
-- **Model type:** Language model
-- **Language(s) (NLP):** ko
-- **License:** mit
-- **Parent Model:** More information needed
-- **Resources for more information:** More information needed
-    - [Associated Paper](Liu et al., &#34;ConvNeXt: A ConvNet for the 2020s,&#34; arXiv:2201.03545)
-# 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. -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info 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 -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-## Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-# Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
-## Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-# Training Details
-## Training Data
-<!-- This should link to a Data 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 on training data 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
-More information needed
-### Speeds, Sizes, Times
-<!-- 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 Data 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
-# Model Examination
-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
-<!-- 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:**
-More information needed
-# Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-More information needed
-# More Information [optional]
-More information needed
-# Model Card Authors [optional]
-<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
-MuhanRnd
-# Model Card Contact
-More information needed
-# How to Get Started with the Model
-Use the code below to get started with the model.
-<details>
-<summary> Click to expand </summary>
-More information needed
-</details>

 ---
+license: mit
+language:
+  - ko
+metrics:
+  - accuracy
+  - f1
+base_model:
+  - facebook/convnext-tiny-224
+pipeline_tag: image-classification
+tags:
+  - multispectral
+  - convnext
+  - image-classification
+  - remote-sensing
+  - agriculture
+  - xai
 ---
+# ConvNext_Multi 모델 카드
+## Model Details
+ConvNext_Multi는 다중분광(멀티스펙트럼) 영상 데이터를 입력으로 하여 작물 및 식생을 분류하는 ConvNeXt 기반 이미지 분류 모델입니다. 드론 및 위성에서 촬영한 5밴드 (Blue, Green, Red, Near-Infrared, RedEdge) 영상을 효율적으로 처리하도록 설계되어, 고해상도 농업·환경 모니터링에 적합합니다.
+- **Developed by:** AI Research Team, MuhanRnd
+- **License:** MIT
+- **Base model:** facebook/convnext-tiny-224
+- **Languages:** Korean (모델 주석 및 문서화)
+- **Model type:** 이미지 분류 (멀티밴드 입력)
+## Uses
+### Direct Use
+- 다중분광 영상 기반 생육 상태 분류
+- 드론 영상의 5밴드 입력 멀티스펙트럼 이미지 분류 작업
+### Downstream Use
+- 유사한 다중분광 데이터셋에 대한 파인튜닝
+- 농업 외 기타 환경 모니터링 대상 분류 문제 적용 가능
+### Out-of-Scope Use
+- RGB 3밴드 영상만을 사용하는 경우 (입력 구조상 활용 불가)
+- 보정되지 않은 멀티밴드 이미지(다중분광 보정값 처리 필요)
+- 객체 검출, 분할 등 분류 이외의 태스크
+## Bias, Risks, and Limitations
+- 본 모델은 특정 지역 및 작물 데이터를 중심으로 학습되었으므로, 미학습 환경에서는 성능 저하가 발생할 수 있음
+- 다중분광 영상의 품질, 촬영 조건, 전처리 과정에 민감함
+- 데이터 편향으로 인해 특정 작물이나 배경에 과적합 가능성 존재
+- 모델 예측은 보조적 판단 자료로 활용해야 하며, 최종 의사결정은 전문가 판단과 병행 필요
+## How to Get Started
+```python
+from transformers import AutoModelForImageClassification, AutoFeatureExtractor
+import torch
+# 모델과 특징 추출기 불러오기
+model = AutoModelForImageClassification.from_pretrained("MhRnd/ConvNext_Multi")
+extractor = AutoFeatureExtractor.from_pretrained("MhRnd/ConvNext_Multi")
+# 다중밴드 이미지 텐서 (예: [batch_size, 5, H, W])
+inputs = extractor(multi_band_images, return_tensors="pt")
+# 모델 추론
+outputs = model(**inputs)
+logits = outputs.logits
+predicted_class = torch.argmax(logits, dim=1)
+```
+## Training Details
+- **Training Data:**
+  - 드론 및 위성 촬영 다중분광(5밴드) 이미지 데이터셋
+  - 라벨: 주요 작물 및 생육 상태 클래스
+- **Training Procedure:**
+  - 파인튜닝: facebook/convnext-tiny-224 기반
+  - 에폭수: 2
+  - 배치사이즈: 16
+  - 옵티마이저: AdamW
+  - 학습률: 1e-05, Step 스케줄러 사용
+## Evaluation
+- **Testing Data:** 별도 보유한 검증용 다중분광 이미지셋
+- **Metrics:** 정확도(Accuracy), 손실(Loss)
+- **Performance:**
+  - **베스트 성능 (Epoch 2):**
+    - 훈련 손실: 1.3640
+    - 훈련 정확도: 0.2783
+    - 검증 손실: 1.3898
+    - 검증 정확도: 0.2069
+  - **마지막 업데이트:** 2025-08-20 08:32:18
+  - Accuracy: 90.0%
+## Environmental Impact
+- **Hardware:** NVIDIA RTX 3090 GPU
+- **Training Duration:** 약 15분
+## Citation
+```
+@article{liu2022convnext,
+  title={ConvNeXt: A ConvNet for the 2020s},
+  author={Liu, Zhuang and Mao, Han and Wu, Chao and Feichtenhofer, Christoph and Darrell, Trevor and Xie, Saining},
+  journal={arXiv preprint arXiv:2201.03545},
+  year={2022}
+}
+```
+## Glossary
+- **다중분광 영상(Multispectral Imagery):** 여러 파장대의 빛을 분리하여 촬영한 영상으로, 작물의 생육 상태 분석 등에 활용됨
+- **ConvNeXt:** 현대적인 구조를 갖춘 컨볼루션 신경망(CNN)
+## Model Card Authors
+- AI Research Team, MuhanRnd
+- [email protected]