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{
"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
} |