Life Cycle of a Deep Learning Computer Vision Project

  • By Aniket Kulkarni
  • December 31, 2024
  • Deep Learning
Life Cycle of a Deep Learning Computer Vision Project

Life Cycle of a Deep Learning Computer Vision Project

The life cycle of a deep learning computer vision project involves multiple stages, from defining the problem to deploying and monitoring the model in production. Below is a detailed explanation of each stage:

1. Problem Definition

  • Objective: Clearly define the problem you aim to solve (e.g., object detection, image classification, segmentation).
  • Key Questions:
    • What is the problem (e.g., detecting vehicles in traffic)?
    • Why is it important?
    • What is the expected outcome?
  • Deliverable: A well-defined project scope and success criteria.

 

2. Data Collection

  • Objective: Gather relevant data to train your model.
  • Steps:
    • Collect images/videos from cameras, online datasets, APIs, or simulations.
    • Label the data manually or using automated tools (e.g., bounding boxes, segmentation masks).
  • Tools: LabelImg, LabelMe, CVAT, or custom scripts.
  • Deliverable: A labeled dataset, usually split into training, validation, and test sets.

 

3. Data Preprocessing

  • Objective: Prepare the data for model training.
  • Steps:
    • Resize images to a fixed dimension.
    • Normalize pixel values (e.g., scale them to [0, 1] or [-1, 1]).
    • Augment data (e.g., rotations, flips, color shifts) to increase robustness.
    • Handle class imbalance (e.g., oversampling, undersampling).
  • Deliverable: A clean, standardized dataset ready for training.

 

4. Model Selection

  • Objective: Choose a suitable architecture for the task.
  • Options:
    • Image Classification: ResNet, EfficientNet, MobileNet.
    • Object Detection: YOLO, Faster R-CNN, SSD.
    • Segmentation: U-Net, DeepLab, Mask R-CNN.
  • Considerations:
    • Accuracy vs. inference speed.
    • Hardware constraints (e.g., edge devices).
  • Deliverable: Selected model architecture.

 

5. Model Training

  • Objective: Train the deep learning model on the dataset.
  • Steps:
    • Load data and define the model architecture.
    • Specify a loss function (e.g., Cross-Entropy, IoU loss) and optimizer (e.g., Adam, SGD).
    • Train the model using the training dataset.
    • Monitor metrics like loss, accuracy, precision, recall, and mAP during training.
    • Use techniques like learning rate scheduling, early stopping, or checkpointing.
  • Deliverable: A trained model (e.g., best.pt, model.pth).

 

6. Model Evaluation

  • Objective: Evaluate the model on the validation and test datasets.
  • Metrics:
    • Classification: Accuracy, F1-score, Precision, Recall.
    • Detection: mAP (mean Average Precision), IoU (Intersection over Union).
    • Segmentation: Dice Coefficient, IoU.
  • Tools: Matplotlib, TensorBoard, or built-in library visualizations.
  • Deliverable: Performance report, confusion matrix, and identified shortcomings.

 

7. Model Optimization

  • Objective: Improve model performance and efficiency.
  • Techniques:
    • Hyperparameter tuning (e.g., learning rate, batch size, number of layers).
    • Fine-tuning with pre-trained models (e.g., Transfer Learning).
    • Model pruning, quantization, or knowledge distillation for deployment on resource-constrained devices.
  • Deliverable: Optimized model ready for deployment.

 

8. Model Deployment

  • Objective: Integrate the trained model into an application.
  • Options:
    • Deploy as a web service using Flask, FastAPI, or TensorFlow Serving.
    • Deploy on-edge devices using ONNX, TensorFlow Lite, or NVIDIA TensorRT.
  • Steps:
    • Convert the model to the required format (e.g., .onnx, .tflite).
    • Set up inference pipelines.
    • Ensure latency and throughput meet requirements.
  • Deliverable: A functional, integrated model.

 

9. Monitoring and Maintenance

  • Objective: Monitor the deployed model and maintain performance over time.
  • Steps:
    • Track inference metrics like latency, accuracy, and failures.
    • Log edge cases and identify data drift.
    • Periodically retrain the model with updated data.
  • Tools: Prometheus, Grafana, or custom monitoring dashboards.
  • Deliverable: Continuous model updates and performance tracking.

 

Do visit our channel to learn more: Click Here

 

Author:-

Aniket Kulkarni

Call the Trainer and Book your free demo Class For Deep Learning Call now!!!
| SevenMentor Pvt Ltd.

© Copyright 2021 | SevenMentor Pvt Ltd.