Instruction of a YOLOv8 Model for Extremely Small Object Recognition: MINISCULE OBJECT DETECTION!!!

While training a YOLOv8 model on a unique dataset is straightforward, the standard YOLO approach yields poor outcomes when the target object is so minute that it occupies under 0.05% of the area (such as a cursor on a high-definition display, or elements in satellite imagery, etc.).

Reasons to select services:

1. Educational background: Possessing two master's degrees, one in Data Science and another in Computer Science with a specialization in Computer Vision, from a major university.
2. Professional background: With more than 8 years in the AI sector, developing computer vision products from inception. More details are available on a professional networking profile.
3. Punctual completion: Recognizing the value of time for both parties, every effort is made to ensure timely completion.
4. Standard of output: Client contentment is paramount, involving direct consultations to address any questions or concerns regarding the final result.

Technologies and Frameworks:

OpenCV, PyTorch, TF, YOLO, Streamlit

IMPORTANT: The initial discussion is complimentary. A research charge of $200 applies (additional). Project commencement requires prior research.