YOLO v8 or v10 Architecture Improvement for Object Detection -- 3
Budget: ₹1,500 – ₹12,500 INR
⛔PLEASE DON'T POST YOUR BID IF YOU HAVE A HIGHER OFFER Than YOUR BID Or YOU CAN'T DO
This project aims to enhance the performance of the YOLO v8 object detection model by improving its accuracy by 5% through targeted architectural modifications while ensuring that the model's processing speed remains efficient.
Objective:
The primary goal of this project is to improve the accuracy of the YOLO v8 object detection model by 5% through architectural modifications while ensuring that the processing speed remains efficient. The project will also involve a comprehensive comparative analysis between the modified model and the base YOLO v8 model, with a detailed justification of the performance improvements.
Key Tasks:
1. Architectural Improvements:
- Analyze the existing YOLO v8 architecture to identify potential areas for optimization.
- Implement modifications aimed at increasing model accuracy (e.g., through adjustments in layers, loss functions, data augmentation, etc.).
- Ensure that the speed and real-time processing capabilities are maintained or improved.
2. Model Training and Evaluation:
- Train the modified YOLO v8 model on the specified dataset.
- Conduct a rigorous evaluation to ensure that the accuracy is improved by at least 5%.
3. Comparative Analysis:
- Perform a side-by-side comparison between the base YOLO v8 model and the modified model.
- Evaluate key performance metrics, including accuracy, speed, precision, recall, and mAP (mean Average Precision).
- Justify the improvements and discuss trade-offs (if any) between speed and accuracy.
4. Documentation and Reporting:
- Document the process, from architectural changes to training details and performance metrics.
- Provide a detailed report, including visualizations of model performance, comparisons, and conclusions.
Deliverables:
- Modified YOLO v8 model with improved accuracy.
- A comparative analysis report, including performance metrics and justifications.
- Code and implementation details for architectural changes.
Expected Outcome:
The project is expected to result in a YOLO v8 model that shows a minimum of 5% improvement in accuracy over the base model while retaining or improving speed. The comparative analysis will provide insights into how the architectural changes contribute to performance improvements.
The model will be trained and evaluated using the Pascal VOC dataset.
This project aims to enhance the performance of the YOLO v8 object detection model by improving its accuracy by 5% through targeted architectural modifications while ensuring that the model's processing speed remains efficient.
Objective:
The primary goal of this project is to improve the accuracy of the YOLO v8 object detection model by 5% through architectural modifications while ensuring that the processing speed remains efficient. The project will also involve a comprehensive comparative analysis between the modified model and the base YOLO v8 model, with a detailed justification of the performance improvements.
Key Tasks:
1. Architectural Improvements:
- Analyze the existing YOLO v8 architecture to identify potential areas for optimization.
- Implement modifications aimed at increasing model accuracy (e.g., through adjustments in layers, loss functions, data augmentation, etc.).
- Ensure that the speed and real-time processing capabilities are maintained or improved.
2. Model Training and Evaluation:
- Train the modified YOLO v8 model on the specified dataset.
- Conduct a rigorous evaluation to ensure that the accuracy is improved by at least 5%.
3. Comparative Analysis:
- Perform a side-by-side comparison between the base YOLO v8 model and the modified model.
- Evaluate key performance metrics, including accuracy, speed, precision, recall, and mAP (mean Average Precision).
- Justify the improvements and discuss trade-offs (if any) between speed and accuracy.
4. Documentation and Reporting:
- Document the process, from architectural changes to training details and performance metrics.
- Provide a detailed report, including visualizations of model performance, comparisons, and conclusions.
Deliverables:
- Modified YOLO v8 model with improved accuracy.
- A comparative analysis report, including performance metrics and justifications.
- Code and implementation details for architectural changes.
Expected Outcome:
The project is expected to result in a YOLO v8 model that shows a minimum of 5% improvement in accuracy over the base model while retaining or improving speed. The comparative analysis will provide insights into how the architectural changes contribute to performance improvements.
The model will be trained and evaluated using the Pascal VOC dataset.