Datasets:
ArXiv:
License:
| { | |
| "name": "33_Object_Detection_YOLOv3_COCO_DL", | |
| "query": "Help me develop an object detection system using the YOLOv3 model and the COCO dataset. Download the dataset and preprocess the images by resizing and normalization in `src/data_loader.py`. Implement the YOLOv3 model and use Non-Maximum Suppression (NMS) to refine the results in `src/model.py`. Save the detected objects to `results/figures/`, and create an interactive Streamlit web page in `src/app.py` to display the detection results. Finally, evaluate the model's performance, including metrics such as mAP and inference time, and save the evaluation results to `results/metrics/model_performance.txt`. The system should properly manage the launch and termination of the Streamlit application to prevent unnecessary resource usage.", | |
| "tags": [ | |
| "Computer Vision" | |
| ], | |
| "requirements": [ | |
| { | |
| "requirement_id": 0, | |
| "prerequisites": [], | |
| "criteria": "The \"COCO\" dataset downloading is implemented in `src/data_loader.py`.", | |
| "category": "Dataset or Environment", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 1, | |
| "prerequisites": [ | |
| 0 | |
| ], | |
| "criteria": "Data preprocessing, including resizing and normalization of images, is performed in `src/data_loader.py`.", | |
| "category": "Data preprocessing and postprocessing", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 2, | |
| "prerequisites": [], | |
| "criteria": "The \"YOLOv3\" model is implemented in `src/model.py`.", | |
| "category": "Machine Learning Method", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 3, | |
| "prerequisites": [ | |
| 1, | |
| 2 | |
| ], | |
| "criteria": "\"Non-Maximum Suppression\" (NMS) is applied to refine detection results. Please implement this in `src/model.py`.", | |
| "category": "Data preprocessing and postprocessing", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 4, | |
| "prerequisites": [ | |
| 2, | |
| 3 | |
| ], | |
| "criteria": "Detection results are saved to the specified folder `results/figures/`.", | |
| "category": "Visualization", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 5, | |
| "prerequisites": [ | |
| 2, | |
| 3, | |
| 4 | |
| ], | |
| "criteria": "An interactive web page in `src/app.py` using \"Streamlit\" is created to display detection results saved in `results/figures/`.", | |
| "category": "Human Computer Interaction", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 6, | |
| "prerequisites": [ | |
| 2, | |
| 3 | |
| ], | |
| "criteria": "Model performance evaluation results are saved in `results/metrics/model_performance.txt`.", | |
| "category": "Performance Metrics", | |
| "satisfied": null | |
| } | |
| ], | |
| "preferences": [ | |
| { | |
| "preference_id": 0, | |
| "criteria": "The \"Streamlit\" web page should be user-friendly, allowing users to easily upload and view new images for detection.", | |
| "satisfied": null | |
| }, | |
| { | |
| "preference_id": 1, | |
| "criteria": "The performence evalution includes mAP and inference time as metrics.", | |
| "satisfied": null | |
| }, | |
| { | |
| "preference_id": 2, | |
| "criteria": " The system should properly manage the launch and termination of the Streamlit application.", | |
| "satisfied": null | |
| } | |
| ], | |
| "is_kaggle_api_needed": false, | |
| "is_training_needed": true, | |
| "is_web_navigation_needed": false | |
| } |