Weed Detection Optimization Project
Budget: $250 – $750 USD
I have a dataset of approximately 30,000 images of weeds that are already sorted but not labeled. I need an image processing expert to develop a detection algorithm using advanced CNN and YOLOv8 variants (like ResNet or DenseNet). The goal is to optimize existing algorithms and improve detection accuracy.
Key requirements include:
- Train on the provided weed images
- Compare results of ready-made algorithms with optimized ones
- Detect weeds in a provided video (weeds in video are part of the dataset, and tomato images should be excluded)
- Output should include both bounding boxes and class labels
- Deliver all code and outputs
- Provide a walkthrough of the application and process
Ideal skills and experience:
- Expertise in Python
- Experience with CNN, YOLO, and image processing
- Proven track record in similar projects with demonstrable results
Key requirements include:
- Train on the provided weed images
- Compare results of ready-made algorithms with optimized ones
- Detect weeds in a provided video (weeds in video are part of the dataset, and tomato images should be excluded)
- Output should include both bounding boxes and class labels
- Deliver all code and outputs
- Provide a walkthrough of the application and process
Ideal skills and experience:
- Expertise in Python
- Experience with CNN, YOLO, and image processing
- Proven track record in similar projects with demonstrable results