From 71b3c21f289d910118244838530aaa9cfee1bb29 Mon Sep 17 00:00:00 2001 From: Daekeun Date: Mon, 29 Jun 2026 17:59:06 +0900 Subject: [PATCH] dk - edit --- README.md | 206 ++++++++++-------------------------------------------- main.py | 18 +++-- 2 files changed, 50 insertions(+), 174 deletions(-) diff --git a/README.md b/README.md index f1bb08e..d850974 100644 --- a/README.md +++ b/README.md @@ -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,
-``` -├── 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) diff --git a/main.py b/main.py index 4ebf4f6..2469817 100644 --- a/main.py +++ b/main.py @@ -19,10 +19,16 @@ transforms = T.Compose([ T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) -task = Task.init( - project_name="Person_Attribute_Recognition", - task_name="model-yolo-person-classify" -) +def init_acai_task(output): + task = Task.init( + project_name="Person_Attribute_Recognition", + 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): @@ -103,8 +109,8 @@ 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__":