Tasks and Duties
Task Objective: In this task, you will develop a comprehensive strategy for a computer vision project, focusing on designing the algorithms and outlining data preprocessing steps. You are expected to brainstorm and plan your approach by creating flowcharts, pseudocode, and detailed steps that will serve as the backbone for your subsequent development efforts.
Expected Deliverables: Submit a single file (preferably in PDF or Markdown format) containing: (1) a detailed description of your proposed algorithm(s), (2) a strategy for data preprocessing and augmentation, (3) flowcharts or diagrams illustrating the process, (4) pseudocode as a blueprint of your implementation, and (5) clear rationale for your choices.
Key Steps to Complete the Task: (a) Identify the scope of a typical computer vision problem such as object detection, segmentation, or feature extraction. (b) Research and outline common preprocessing techniques including normalization, resizing, noise reduction, and augmentation. (c) Create detailed diagrams and flowcharts that map out your algorithm’s processing steps. (d) Write pseudocode to represent the key functions and logic of the algorithm. (e) Provide justifications for your design and preprocessing choices based on accuracy, speed, and real-time performance.
Evaluation Criteria: Your submission will be evaluated based on clarity, thoroughness of the strategy, innovation in algorithm design, quality and detail of pseudocode and diagrams, and the logical coherence of the overall plan. The task is designed to require approximately 30 to 35 hours of work and must show practical planning skills in computer vision applications.
Task Objective: The goal of this task is to practically implement a deep learning model for a computer vision application, such as image classification or segmentation. You will design, code, and train a model using publicly available data and frameworks. The focus is on practical coding, model training, and analysis of results.
Expected Deliverables: Submit a file containing your source code, a well-documented Jupyter notebook or script, training logs, and a report (either embedded in the notebook or as a separate document) that explains your model architecture, training parameters, and analysis of performance metrics such as accuracy and loss.
Key Steps to Complete the Task: (a) Select a deep learning framework such as TensorFlow or PyTorch. (b) Define a suitable model architecture for your chosen computer vision task. (c) Implement data preprocessing routines to prepare input images for training. (d) Train your model using a publicly available dataset or a synthetic dataset generated for this purpose. (e) Document the training process, including hyperparameter tuning and any challenges encountered. (f) Analyze the performance of your model and suggest improvements for further iterations.
Evaluation Criteria: Your work will be assessed on the robustness of your code, the clarity of your documentation, correct implementation of data preprocessing and model training, and the quality of your performance analysis. Ensure that your submission clearly demonstrates practical hands-on model building, and meets the estimated 30 to 35 hours workload.
Task Objective: This task is designed to evaluate your expertise in optimizing computer vision algorithms for real-time performance. You will take an existing computer vision model (either from your previous work or a simple model created for this task) and apply optimization techniques to ensure faster processing without significant loss in accuracy.
Expected Deliverables: Submit a file that includes your modified source code, a detailed report documenting the optimization process, and performance benchmarks. Your report should cover the techniques used such as model pruning, quantization, or algorithmic improvements, and provide comparative metrics (e.g., inference time, FPS) before and after optimization.
Key Steps to Complete the Task: (a) Identify parts of the code or model that are computationally intensive. (b) Research and implement optimization techniques like model pruning, quantization, multi-threading, or batch processing. (c) Benchmark your model’s performance pre- and post-optimization using standard metrics. (d) Document the changes in a report, including code snippets, performance graphs, and a discussion on trade-offs between speed and accuracy. (e) Provide recommendations or further optimizations that could be implemented in future work.
Evaluation Criteria: Your submission will be judged on the effectiveness of the optimization strategies, the clarity and thoroughness of your performance benchmarks, quality and readability of the code modifications, and the completeness and depth of your report. The task requires a balance of theoretical understanding and practical implementation, estimated to take about 30 to 35 hours.
Task Objective: In this culminating task, you are expected to demonstrate your ability to integrate a computer vision solution into a simulated cross-functional application. Focus on building a small-scale prototype where your computer vision module interacts with other software components, emulating a real-world integration scenario.
Expected Deliverables: Submit a comprehensive file that includes your integration code (API or simulation framework), a functional prototype, and a detailed report. The report should describe your integration strategy, the challenges faced during the process, and the end-to-end testing results with clear documentation of test cases and outcomes.
Key Steps to Complete the Task: (a) Select or design a simple application context (e.g., an automated system for quality control, real-time surveillance, or augmented reality) that will benefit from computer vision. (b) Develop an integration plan that outlines how your computer vision module communicates with other application components (e.g., using RESTful APIs or socket communication). (c) Implement the integration in code, ensuring modularity and clear interaction between components. (d) Conduct thorough testing, including unit tests and system tests, to validate the overall functionality. (e) Prepare a detailed report documenting your architecture, integration process, challenges encountered, and the test results.
Evaluation Criteria: The evaluation will consider the cohesiveness of the integration, the technical soundness and clarity of your code, the depth and clarity of your testing procedures, and the thoroughness of your documentation. This task is designed to emulate 30 to 35 hours of dedicated work, testing your practical skills in system integration and cross-functional collaboration in computer vision applications.