Cloud Vision Precision

I am looking for an experienced freelancer proficient in machine learning and image segmentation to assist with a critical project. The task involves utilizing a U-Net model for segmenting and predicting satellite cloud images with a high level of accuracy. The project's success is pivotal for further applications in weather forecasting and climate studies, thereby requiring exceptional precision and skill. **Primary Objectives:** - Apply image segmentation on approximately 100,000 PNG formatted satellite cloud images. - Ensure the predictions maintain above 90% accuracy, focusing on detailed and robust object recognition to support future usage in complex applications. **Ideal Skills and Experience:** - Proficiency in machine learning and specifically in image segmentation models, with a strong emphasis on U-Net or similar architectures. - Extensive experience handling large datasets, particularly in PNG format, and optimizing models for high accuracy in object recognition tasks. - Background in projects related to weather, climate studies, or similar fields requiring high-fidelity satellite image analysis. - Capability to work with datasets of 100,000 images, implementing efficient preprocessing and augmentation techniques to ensure model robustness. - Familiarity with Python, TensorFlow, or PyTorch, and the ability to optimize neural network parameters for high-accuracy outcomes. **Project Deliverables:** - A fully trained and validated U-Net model capable of segmenting and predicting cloud formations with over 90% accuracy. - Documentation covering the methodology, code, and model performance evaluation. - Recommendations for further model improvements or application areas based on the project findings. This project is not just a technical task but a stepping stone towards leveraging AI in enhancing our understanding and prediction capabilities pertaining to weather patterns. Your expertise can significantly contribute to this innovative venture, and I am eager to collaborate with someone who shares a passion for technology and its application in environmental studies.