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Project Title and Objective\n- Project Name:\n- Brief Description: What is the goal of the project? What problem does it aim to solve?\n2. Dataset Information\n- Source of dataset (e.g. Kaggle, public dataset, self-collected, sensors):\n- Did you create your own dataset?\n- Did you perform manual annotation? If yes, describe how.\n- Dataset size (e.g., number of images, number of classes):\n- Dataset split (e.g., 70% train, 15% validation, 15% test):\n3. AI Model Used\n- Model type (e.g., YOLOv5, U-Net, ResNet, MobileNet, ViT):\n- Was the model trained from scratch or fine-tuned?\n- If a pre-trained model was used:\n- Which pre-trained model?\n4. Training Process and Hyperparameters\n- Framework used (e.g., PyTorch, TensorFlow, Keras, Ultralytics YOLO):\n- Training duration:\n- Number of epochs:\n- Batch size:\n- Learning rate:\n- Optimizer used:\n- Data augmentation applied (e.g., rotation, flipping, blurring):\n- Challenges encountered and how they were solved (if any):\n5. Evaluation and Performance Metrics\n- Evaluation metrics used (e.g., Accuracy, mAP, F1-score, IoU):\n- Test results summary:\n- Was overfitting or underfitting observed?\n- Additional visuals (e.g., confusion matrix, sample outputs – optional):\n6. Codebase and Infrastructure\n- If a GitHub repository was used, provide the link:\n- If code was written from scratch, summarize components:\n- Hardware used for training (e.g., local PC, Google Colab, GPU specs):\n7. Additional Notes and Learnings\n- Key things learned during the project:\n- What could be improved or done differently?\n- Potential directions for future improvement:","cbCaitW4y3HFwVV9","https://ap.wps.com/l/cbCaitW4y3HFwVV9","docx",27397,"English","# Project Title and Objective\n# Dataset Information\n# AI Model Used\n# Training Process and Hyperparameters\n# Evaluation and Performance Metrics\n# Codebase and Infrastructure\n# Additional Notes and Learnings","[{\"question\":\"What should be included in the Project Title and Objective section?\",\"answer\":\"Include the project name and a brief description explaining the goal and the problem the project aims to solve.\"},{\"question\":\"What dataset details does the template request?\",\"answer\":\"Provide the dataset source, whether it was self-created, annotation approach (including manual annotation if used), dataset size, and the dataset split for training, validation, and testing.\"},{\"question\":\"Which training and evaluation information must be documented?\",\"answer\":\"Record the model type, whether training was from scratch or fine-tuning, the training framework, duration, epochs, batch size, learning rate, optimizer, data augmentation, challenges, evaluation metrics, and a test results summary including overfitting or underfitting observations.\"}]","CEN 462 - Introduction to Computer Vision - AI Project Report Template | DOCX",1788330768]