dk - edit

2026-06-29 17:59:06 +09:00 · 2026-06-29 17:22:16 +09:00 · 2026-06-29 16:57:37 +09:00 · 2026-06-29 16:57:11 +09:00
6 changed files with 1377 additions and 176 deletions
@@ -0,0 +1 @@
+3.11
@@ -1,181 +1,51 @@
-# Person-Attribute-Recognition-MarketDuke
-A simple baseline implemented in PyTorch for **pedestrian attribute recognition** task, evaluating on Market-1501-attribute and DukeMTMC-reID-attribute dataset.
+# Person Attribute Recognition

-## Dataset
-You can get [Market-1501-attribute](https://github.com/vana77/Market-1501_Attribute) and [DukeMTMC-reID-attribute](https://github.com/vana77/DukeMTMC-attribute) annotations from [here](https://github.com/vana77). Also you need to download Market-1501 and DukeMTMC-reID dataset.
+COCO80 기반 **YOLO26m** 객체 탐지 결과를 받아, 탐지된 **Person** 영역의 상세 속성을 분석하는 ClearML Agent입니다.

-Then, create a folder named 'attribute' under your dataset path, and put corresponding annotations into the folder.
+## 역할

-For example,<br>
-```
-├── dataset
-│   ├── DukeMTMC-reID
-│       ├── bounding_box_test
-│       ├── bounding_box_train
-│       ├── query
-│       ├── attribute
-│           ├── duke_attribute.mat
+1. YOLO26m이 COCO80 클래스로 이미지를 분석하고 Person의 바운딩 박스(`xywh`)를 전달
+2. 해당 영역을 크롭한 뒤 보행자 속성 인식 모델로 상세 속성 추론
+3. 결과를 ClearML 아티팩트로 업로드
+
+## 입력 / 출력
+
+| 항목 | 설명 |
+|------|------|
+| `--image_url` | 분석할 이미지 URL 또는 로컬 경로 |
+| `--xywh` | Person 바운딩 박스 (`x,y,w,h`) — YOLO26m 탐지 결과 |
+| 출력 | 성별, 연령대, 의류 색상·종류 등 속성 목록 (`final_result` 아티팩트) |
+
+## 실행
+
+```bash
+python main.py \
+  --image_url "https://example.com/image.jpg" \
+  --xywh "404,290,74,193"
 ```

-## Model
-Trained model are provided. You may download it from [Google Drive](https://drive.google.com/drive/folders/1JTdjuEbxSLypnfUzVuuxLj1uSKAacfd0?usp=sharing) or [Baidu Drive](https://pan.baidu.com/s/1bByCxZp9bSs8YYZPbuK21A) (提取码：jpks).
+## 요구 사항

-You may download it and move `checkpoints` folder to your project's root directory.
+- Python 3.11+
+- PyTorch, torchvision, ClearML, requests
+- 사전 학습 체크포인트: `checkpoints/market/resnet50_nfc/net_last.pth`

-## Dependencies
-* Python 3.5
-* PyTorch >= 0.4.1
-* torchvision >= 0.2.1
-* matplotlib, sklearn, prettytable (optional)
+### 설치

-## Usage
-```
-python3  train.py  --data-path  ~/dataset  --dataset  [market | duke]  --model  resnet50  [--use-id]
-
-python3  test.py   --data-path  ~/dataset  --dataset  [market | duke]  --model  resnet50  [--print-table]
-
-python3  inference.py   test_sample/test_market.jpg  [--dataset  market]  [--model  resnet50]
+```bash
+uv sync
+# 또는
+pip install -r requirements.txt
 ```

-## Result
+## ClearML

-We use **binary classification** settings (considered each attribute as an independent binary classification problem), and the classification threshold is **0.5**.
+| 항목 | 값 |
+|------|-----|
+| Project | `Person_Attribute_Recognition` |
+| Task | `model-yolo-person-classify` |

-***Note that the precision, recall and f1 score are denoted as '-' for some ill-defined cases.***
+## 모델

-### Market-1501 gallery
-```
-+------------+----------+-----------+--------+----------+
-| attribute  | accuracy | precision | recall | f1 score |
-+------------+----------+-----------+--------+----------+
-|   young    |  0.998   |   0.533   | 0.267  |  0.356   |
-|  teenager  |  0.892   |   0.927   | 0.951  |  0.939   |
-|   adult    |  0.895   |   0.582   | 0.450  |  0.508   |
-|    old     |  0.992   |   0.037   | 0.012  |  0.019   |
-|  backpack  |  0.883   |   0.828   | 0.672  |  0.742   |
-|    bag     |  0.790   |   0.608   | 0.378  |  0.467   |
-|  handbag   |  0.893   |   0.254   | 0.065  |  0.104   |
-|  clothes   |  0.946   |   0.956   | 0.984  |  0.970   |
-|    down    |  0.945   |   0.968   | 0.949  |  0.959   |
-|     up     |  0.936   |   0.938   | 0.998  |  0.967   |
-|    hair    |  0.877   |   0.871   | 0.773  |  0.819   |
-|    hat     |  0.982   |   0.812   | 0.505  |  0.623   |
-|   gender   |  0.919   |   0.947   | 0.864  |  0.903   |
-|  upblack   |  0.954   |   0.859   | 0.790  |  0.823   |
-|  upwhite   |  0.926   |   0.846   | 0.882  |  0.863   |
-|   upred    |  0.974   |   0.904   | 0.840  |  0.871   |
-|  uppurple  |  0.985   |   0.703   | 0.815  |  0.755   |
-|  upyellow  |  0.976   |   0.895   | 0.836  |  0.865   |
-|   upgray   |  0.909   |   0.852   | 0.391  |  0.537   |
-|   upblue   |  0.946   |   0.868   | 0.420  |  0.566   |
-|  upgreen   |  0.966   |   0.790   | 0.713  |  0.750   |
-| downblack  |  0.879   |   0.815   | 0.889  |  0.850   |
-| downwhite  |  0.956   |   0.608   | 0.550  |  0.578   |
-|  downpink  |  0.989   |   0.795   | 0.782  |  0.788   |
-| downpurple |  1.000   |     -     |   -    |    -     |
-| downyellow |  0.999   |   0.000   | 0.000  |  0.000   |
-|  downgray  |  0.878   |   0.756   | 0.443  |  0.559   |
-|  downblue  |  0.861   |   0.762   | 0.446  |  0.563   |
-| downgreen  |  0.978   |   0.766   | 0.295  |  0.426   |
-| downbrown  |  0.958   |   0.754   | 0.590  |  0.662   |
-+------------+----------+-----------+--------+----------+
-Average accuracy: 0.9361
-Average f1 score: 0.6492
-```
-
-### DukeMTMC-ReID gallery
-```
-+-----------+----------+-----------+--------+----------+
-| attribute | accuracy | precision | recall | f1 score |
-+-----------+----------+-----------+--------+----------+
-|  backpack |  0.829   |   0.794   | 0.926  |  0.855   |
-|    bag    |  0.836   |   0.496   | 0.287  |  0.364   |
-|  handbag  |  0.935   |   0.469   | 0.073  |  0.126   |
-|   boots   |  0.905   |   0.784   | 0.791  |  0.787   |
-|   gender  |  0.858   |   0.806   | 0.828  |  0.817   |
-|    hat    |  0.898   |   0.883   | 0.680  |  0.768   |
-|   shoes   |  0.916   |   0.756   | 0.414  |  0.535   |
-|    top    |  0.893   |   0.590   | 0.381  |  0.463   |
-|  upblack  |  0.821   |   0.827   | 0.903  |  0.864   |
-|  upwhite  |  0.959   |   0.750   | 0.509  |  0.606   |
-|   upred   |  0.973   |   0.745   | 0.649  |  0.694   |
-|  uppurple |  0.995   |   0.258   | 0.123  |  0.167   |
-|   upgray  |  0.900   |   0.611   | 0.333  |  0.432   |
-|   upblue  |  0.943   |   0.766   | 0.519  |  0.619   |
-|  upgreen  |  0.975   |   0.463   | 0.403  |  0.431   |
-|  upbrown  |  0.980   |   0.481   | 0.328  |  0.390   |
-| downblack |  0.787   |   0.740   | 0.807  |  0.772   |
-| downwhite |  0.945   |   0.771   | 0.395  |  0.522   |
-|  downred  |  0.991   |   0.739   | 0.645  |  0.689   |
-|  downgray |  0.927   |   0.471   | 0.238  |  0.317   |
-|  downblue |  0.807   |   0.741   | 0.669  |  0.703   |
-| downgreen |  0.997   |     -     |   -    |    -     |
-| downbrown |  0.979   |   0.871   | 0.594  |  0.706   |
-+-----------+----------+-----------+--------+----------+
-Average accuracy: 0.9152
-Average f1 score: 0.5739
-```
-
-### Inference
-```
->> python inference.py test_sample/test_market.jpg --dataset market
-age: teenager
-carrying backpack: no
-carrying bag: no
-carrying handbag: no
-type of lower-body clothing: dress
-length of lower-body clothing: short
-sleeve length: short sleeve
-hair length: long hair
-wearing hat: no
-gender: female
-color of upper-body clothing: white
-color of lower-body clothing: white
-
->> python inference.py test_sample/test_duke.jpg --dataset duke
-carrying backpack: no
-carrying bag: yes
-carrying handbag: no
-wearing boots: no
-gender: male
-wearing hat: no
-color of shoes: dark
-length of upper-body clothing: short upper body clothing
-color of upper-body clothing: black
-color of lower-body clothing: blue
-```
-
-## Update
-*20-06-03: Added **identity loss** for joint optimization; Adjusted the learning rate for better performace.*
-
-*20-06-03: Updated **test.py**, settled the issue of ill-defined metrics.*
-
-*19-09-16: Updated **inference.py**, fixed the error caused by missing data-transform.*
-
-*19-09-06: Updated **test.py**, added **F1 score** for evaluating.*
-
-*19-09-03: Added **inference.py**, thanks @ViswanathaReddyGajjala.*
-
-*19-08-23: Released trained models.*
-
-*19-01-09: Fixed the error caused by an update of market and duke attribute dataset.*
-
-## FAQ
-
-### 1. Why attribute order in import_Market1501Attribute.py is different for train and test data?
-
-The label order in import_Market1501Attribute.py is consistent with the attribute order of the dataset.
-
-You can load market_attribute.mat in MATLAB and print "market_attribute.train" or "market_attribute.test" to obtain these orders.
-
-### 2. Why predictions in the Market-1501 dataset have 30 attributes instead of 27?
-
-This repo consider attribute prediction as multiple binary classification, but some attribute have more than two categories.
-
-For example, attribute 'age' in Market-1501 has four categories: young(1), teenager(2), adult(3), old(4). So it can be split into four attributes: 'young', 'teenager', 'adult' and 'old'.
-
-That's why preds of Market-1501 has 30 attributes.
-
-## Reference
-
-*[1] Lin Y, Zheng L, Zheng Z, et al. Improving person re-identification by attribute and identity learning[J]. Pattern Recognition, 2019.*
+- **탐지**: YOLO26m (COCO80)
+- **속성 인식**: ResNet50 + NFC (Market-1501 Attribute, 기본값)
@@ -19,11 +19,17 @@ transforms = T.Compose([
    T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
 ])

+def init_acai_task(output):
    task = Task.init(
        project_name="Person_Attribute_Recognition", 
-    task_name="MarketDuke_Agent"
+        task_name="model-yolo-person-classify"
    )

+    result_data = {"output": output, "status": "PASS"}
+    task.upload_artifact(name="final_result", artifact_object=result_data)
+
+
+

 class PredictDecoder(object):

@@ -103,14 +109,15 @@ def main(image, dataset='market', backbone='resnet50', use_id=False):
        print(f'{name}: {value}')
    print("=" * 50)

-    result_data = {"results": results, "status": "PASS"}
-    task.upload_artifact(name="final_result", artifact_object=result_data)
+    init_acai_task(results)
+


 if __name__ == "__main__":
+    # python main.py --image_url "https://acai.ketidev.kr:20443/detect/image/202606/20260619_145116_image.jpg" --xywh "404,290,74,193"
    parser = argparse.ArgumentParser(description="Person Attribute Recognition Agent")
-    parser.add_argument("--image_url", type=str)
-    parser.add_argument("--xywh", type=str)
+    parser.add_argument("--image_url", type=str)  # --image_url "https://acai.ketidev.kr:20443/detect/image/202606/20260619_145116_image.jpg"
+    parser.add_argument("--xywh", type=str)       # --xywh "x,y,w,h"; (--xywh "404,290,74,193")

    args = parser.parse_args()
    image_url = args.image_url
@@ -0,0 +1,12 @@
+[project]
+name = "person-attribute-recognition"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "clearml>=2.1.9",
+    "requests>=2.34.2",
+    "torch>=2.12.1",
+    "torchvision>=0.27.1",
+]
@@ -0,0 +1,93 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile pyproject.toml -o requirements.txt
+attrs==26.1.0
+    # via
+    #   clearml
+    #   jsonschema
+    #   referencing
+certifi==2026.6.17
+    # via requests
+charset-normalizer==3.4.7
+    # via requests
+clearml==2.1.9
+    # via person-attribute-recognition (pyproject.toml)
+filelock==3.29.4
+    # via torch
+fsspec==2026.6.0
+    # via torch
+furl==2.1.4
+    # via clearml
+idna==3.18
+    # via requests
+jinja2==3.1.6
+    # via torch
+jsonschema==4.26.0
+    # via clearml
+jsonschema-specifications==2025.9.1
+    # via jsonschema
+markupsafe==3.0.3
+    # via jinja2
+mpmath==1.3.0
+    # via sympy
+networkx==3.6.1
+    # via torch
+numpy==2.4.6
+    # via
+    #   clearml
+    #   torchvision
+orderedmultidict==1.0.2
+    # via furl
+pathlib2==2.3.7.post1
+    # via clearml
+pillow==12.2.0
+    # via
+    #   clearml
+    #   torchvision
+psutil==7.2.2
+    # via clearml
+pyjwt==2.13.0
+    # via clearml
+pyparsing==3.3.2
+    # via clearml
+python-dateutil==2.9.0.post0
+    # via clearml
+pyyaml==6.0.3
+    # via clearml
+referencing==0.37.0
+    # via
+    #   clearml
+    #   jsonschema
+    #   jsonschema-specifications
+requests==2.34.2
+    # via
+    #   person-attribute-recognition (pyproject.toml)
+    #   clearml
+rpds-py==2026.5.1
+    # via
+    #   jsonschema
+    #   referencing
+setuptools==81.0.0
+    # via torch
+six==1.17.0
+    # via
+    #   clearml
+    #   furl
+    #   orderedmultidict
+    #   pathlib2
+    #   python-dateutil
+sympy==1.14.0
+    # via torch
+torch==2.12.1
+    # via
+    #   person-attribute-recognition (pyproject.toml)
+    #   torchvision
+torchvision==0.27.1
+    # via person-attribute-recognition (pyproject.toml)
+typing-extensions==4.15.0
+    # via
+    #   referencing
+    #   torch
+urllib3==2.7.0
+    # via
+    #   clearml
+    #   requests
Author	SHA1	Message	Date
chodaekeun	71b3c21f28	dk - edit	2026-06-29 17:59:06 +09:00
chodaekeun	a3bbf78ea2	dk - edit	2026-06-29 17:22:16 +09:00
chodaekeun	b58ab5cc1c	dk - edit	2026-06-29 16:57:37 +09:00
chodaekeun	721121b8e4	dk - edit	2026-06-29 16:57:11 +09:00