Implementing Custom Computer Vision Tasks with Florence 2

I'm seeking a developer with a strong background in deep learning and experience in computer vision, particularly in fine-tuning pretrained models. The primary objective is to set up a pretrained Florence 2 model to perform various computer vision tasks on a custom dataset derived from camera trap data.

Key Tasks:
Fine-tuning the Florence 2 model: This involves adapting the model's weights to be more effective for the specific task at hand—in this case, analyzing camera trap data.
Implementing custom computer vision tasks: The main tasks the model will be expected to perform include:
Object detection
Image classification
Semantic segmentation
Depth estimation
Object count
Multi-object identification
Desired Performance:
The model should strike a balance between accuracy and speed, ensuring that it operates efficiently in real-time without compromising on the quality of its analysis.

Ideal Skills:
Proficiency in deep learning and computer vision
Previous experience in fine-tuning pre-trained models
Understanding of object-oriented programming
Familiarity with the Florence 2 model is a bonus
If you're confident in your ability to meet these requirements, I'd be excited to discuss this project further with you. Please provide examples of your previous work in similar projects.

Skills Required:

PyTorch
Image Analysis
Transformer Model