tanishapatil1234
commented
8 months ago Sub ticket Tanisha CNN implementation
Week 1: Planning and Data Preparation
Day 0: Sprint Planning
- [x] - Define the project scope and objectives.
- [x] - Set up a Scrum board or project plan (like this).
- [x] - Kick-off meeting to clarify goals and expectations.
Day 1: Data Collection
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[x] Gather a dataset of facial images labeled with emotions ( Kaggle's FER-2013 dataset)
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[x] Clean the dataset by removing irrelevant or low-quality images.
Day 2: Data Preprocessing
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[x] Resize images to a consistent format (e.g., 224x224 pixels).
Edit: pre cleaned data set, no need for this!
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[x] Normalize pixel values to a common scale (e.g., [0, 1]).
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[x] Split the dataset into training, validation, and test sets.
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[x] Augment the training dataset (e.g., rotate, flip, zoom) to increase diversity. ENSURE EQUAL DIST.
Day 3: Model Architecture Design
- [x] Decide on a CNN architecture (e.g., VGG16, ResNet, or custom architecture).
- [x] Define the layers, filters, and activation functions for the model.
Model weights and architecture defined.
Day 4: Model Implementation
- [x] Start coding the CNN model in a deep learning framework like TensorFlow or PyTorch.
- [x] Implement forward and backward passes, loss function, and optimization algorithm (e.g., Adam).
- [x] Ensure the model can handle the input image size and number of classes.
Day 5: Training
(CURRENT)
- [x] Train the model on the preprocessed dataset.
- [x] Monitor training metrics such as loss and accuracy.
- [x] Use early stopping if validation performance stalls.
- [x] Document training progress on your Scrum board.
Day 6: Model Evaluation
- [x] Evaluate the model's performance using the test set.
- [x] Calculate metrics like accuracy, precision, recall, and F1-score.
- [x] Identify potential sources of bias or error and document them.
Week 2: Model Optimization and Deployment
Day 7: Hyperparameter Tuning
- [x] Fine-tune hyperparameters, like learning rate, batch size, or dropout rates.
- [x] Experiment with different CNN architectures to improve performance.
Day 8: Error Analysis
- [x] Analyze model errors on the test set to understand common misclassifications.
- [x] Adjust the model or dataset to address error patterns.
Day 9: Model Interpretability
- [x] Implement visualization techniques (e.g., Grad-CAM) to understand what the model focuses on when making predictions.
Day 10: Model Optimization
- [x] Implement model optimization techniques (e.g., weight quantization or model pruning) for deployment on resource-constrained devices.
Day 11: Deployment Setup
- [x] Set up the infrastructure and environment for model deployment (e.g., cloud server or edge device).
- [x] Create an API or interface for the model.
Day 12: Testing and Validation
- [x] Test the deployed model to ensure it functions correctly in the production environment.
- [x] Validate the model's real-world performance.
Day 13: Documentation and Training
- [x] Document the model architecture, training process, and deployment steps.
- [x] Train end-users or colleagues on using the model effectively.
Day 14: Final Review and Retrospective
- [x] Review the project's achievements and ensure all requirements are met.
- [x] Conduct a retrospective meeting to discuss what went well and what could be improved in the development process.
- [x] Plan for future iterations or improvements.
vivianknee
sreejagangapuram
isabelle926
Project Plan: Your Pocket Therapist
Your Pocket Therapist uses an AI Face algorithm to accurately understand the user's emotions and provide advice/quotes in response.
Team Members
Vivian N Isabelle G Tanisha P Sreeja G
Project Objectives
Project Tasks and Timeline
Frontend (JS HTML)
Emotions AI (PYTHON)
Quote Generation (JAVA)