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{
"name": "37_Lane_Detection_ResNet50_TuSimple_DL",
"query": "Develop a lane detection system. Start by importing the standard pre-trained ResNet-50 model from PyTorch in `src/model.py`. We'll work here with the TuSimple lane detection dataset as our test dataset, which should be loaded through `src/data_loader.py`. Then load and preprocess the dataset, including data augmentation techniques such as random cropping, rotation, and scaling in `src/data_loader.py`. Fine-tune the model and save the detection accuracy in `results/metrics/detection_accuracy.txt`, and save the trained model as `models/saved_models/lane_detection_model.pth`. Split a subset of the data for validation, implemented in `src/data_loader.py`. Visualize detection results using matplotlib and save them to `results/figures/`. Create a detailed report of the entire process, including data preprocessing, model training, and evaluation, and save it as `results/lane_detection_report.pdf`. The report should also analyze the model's performance under challenging conditions such as curves or poor lighting.",
"tags": [
"Computer Vision"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"TuSimple\" lane detection dataset is loaded in `src/data_loader.py`.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [
0
],
"criteria": "Data augmentation, including random cropping, rotation, and scaling, is performed in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [
0
],
"criteria": "A subset of the data is split for validation and implemented in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [],
"criteria": "The pre-trained \"ResNet-50\" model is imported from PyTorch in `src/model.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
1,
2,
3
],
"criteria": "Fine tune the \"ResNet-50\" model and save it as `models/saved_models/lane_detection_model.pth`.",
"category": "Save Trained Model",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
4
],
"criteria": "Detection accuracy is saved as `results/metrics/detection_accuracy.txt`.",
"category": "Performance Metrics",
"satisfied": null
},
{
"requirement_id": 6,
"prerequisites": [
4
],
"criteria": "Detection results are visualized with \"matplotlib\" and saved to `results/figures/`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 7,
"prerequisites": [
0,
1,
2,
3,
4,
5
],
"criteria": "A detailed report containing data preprocessing, model training, and evaluation process is created and saved as `results/lane_detection_report.pdf`.",
"category": "Other",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The report should include an analysis of the model's performance on challenging scenarios, such as curves or poor lighting conditions.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "The data augmentation steps should be well-documented, with examples of augmented images included in the report.",
"satisfied": null
}
],
"is_kaggle_api_needed": false,
"is_training_needed": true,
"is_web_navigation_needed": false
} |