With the wealth of tools and techniques at our disposal today, achieving outstanding results in computer vision is not only possible but also achievable with the right practices and strategies. In this article, I will share some of the best practices for data preprocessing, model selection, hyperparameter tuning, and fine-tuning in the context of computer vision tasks.
๐ ๐๐ฎ๐๐ฎ
๐น ๐๐บ๐ฎ๐ด๐ฒ ๐๐ฎ๐๐ฎ - An image is just Height x Width grid of pixels in the range: 0 to 255.
๐น ๐๐ฎ๐๐ฎ ๐๐๐ด๐บ๐ฒ๐ป๐๐ฎ๐๐ถ๐ผ๐ป - Rotation, gaussian blur, stretching, shifting (to increase the diversity of the training dataset)
๐ ๐๐ผ๐ฟ๐ฒ ๐๐ผ๐บ๐ฝ๐ผ๐ป๐ฒ๐ป๐๐
๐น ๐๐ผ๐ป๐๐ผ๐น๐๐๐ถ๐ผ๐ป ๐๐ฎ๐๐ฒ๐ฟ - Applies filters to extract features like edges and corners.
๐น ๐ฃ๐ผ๐ผ๐น๐ถ๐ป๐ด ๐๐ฎ๐๐ฒ๐ฟ - Downsamples feature data using operations like max pooling -> max(pixels)
๐น ๐๐ก๐ก - Flatten feature H x W x C into tabular data then train a dense neural network.
๐ ๐๐๐ ๐ถ๐น๐ถ๐ฎ๐ฟ๐ ๐๐๐ป๐ฐ๐๐ถ๐ผ๐ป
๐น ๐ก๐ผ๐ฟ๐บ๐ฎ๐น๐ถ๐๐ฎ๐๐ถ๐ผ๐ป - Helps vanishing/exploding gradient & convergence
๐น ๐๐ฐ๐๐ถ๐๐ฎ๐๐ถ๐ผ๐ป ๐๐๐ป๐ฐ๐๐ถ๐ผ๐ป - ReLU is commonly used to introduce non-linearity.
๐น ๐๐๐ฎ๐น๐๐ฎ๐๐ถ๐ผ๐ป ๐ ๐ฒ๐๐ฟ๐ถ๐ฐ - Cross-entropy used and errors propagated backwards
It's not as complicated as you would imagine once you understand the building blocks.
โโโ LICENSE
โโโ README.md <- Brief documentation
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โโโ basic_neural_networks <- Folder for Basic Neural Networks section
โ โโโ README.md <- see folders (1 to 8) for details
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โโโ data_processing <- Folder for Data Processing section
โ โโโ README.md <- see folders (1 to 4) for details
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โโโ model_selection <- Folder for Model Selection section
โ โโโ README.md <- see folders (1 to 4) for details
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โโโ hyperparameter_tuning <- Folder for Hyperparameter Tuning section
โ โโโ README.md <- see folders (1 to 4) for details
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โโโ fine_tuning <- Folder for Fine Tuning section
โ โโโ README.md <- see folders (1 to 3) for details
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โโโ computer_vision <- Folder for Computer Vision Topics
โ โโโ README.md <- Brief introduction to computer vision topics
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โ โโโ object_detection <- Subfolder for Object Detection
โ โ โโโ README.md <- Details on object detection topics
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โ โโโ image_segmentation <- Subfolder for Image Segmentation
โ โ โโโ README.md <- Details on image segmentation topics
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โ โโโ yolo_faster_rcnn <- Subfolder for YOLO and Faster R-CNN
โ โ โโโ README.md <- Details on YOLO and Faster R-CNN
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โ โโโ image_captioning <- Subfolder for Image Captioning and Generation
โ โ โโโ README.md <- Details on image captioning and generative models
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โ โโโ object_tracking <- Subfolder for Object Tracking
โ โ โโโ README.md <- Details on object tracking methods
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โ โโโ data_augmentation <- Subfolder for Data Augmentation in Computer Vision
โ โ โโโ README.md <- Details on data augmentation techniques
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โ โโโ hardware_acceleration <- Subfolder for Hardware Acceleration in Computer Vision
โ โ โโโ README.md <- Details on hardware acceleration options
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โ โโโ advanced_applications <- Subfolder for Advanced Computer Vision Applications
โ โ โโโ README.md <- Details on more complex applications
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โ โโโ ethics_in_cv <- Subfolder for Ethical Considerations in Computer Vision
โ โ โโโ README.md <- Details on ethical issues in computer vision
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โ โโโ deployment_scaling <- Subfolder for Deployment and Scalability in Computer Vision
โ โ โโโ README.md <- Details on deploying and scaling computer vision models
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โ โโโ industry_use_cases <- Subfolder for Industry-Specific Use Cases in Computer Vision
โ โโโ README.md <- Details on industry-specific applications
Data augmentation is a powerful technique to enhance the size and quality of your dataset. Apply transformations such as rotations, flips, zooms, and crops to increase the variability of your training data.
Standardizing image data by scaling pixel values to a consistent range (e.g., [0, 1] or [-1, 1]). This ensures that the neural network converges faster and is less sensitive to input variations.
Addressing class imbalance by oversampling, undersampling, or using techniques like Synthetic Minority Over-sampling Technique (SMOTE) to balance class distributions.
Leveraging pretrained models such as VGG, ResNet, or Inception, and fine-tune them on your specific task. Transfer learning often leads to improved performance and faster convergence.
For most computer vision tasks, CNNs are the go-to choice due to their exceptional feature extraction capabilities. Experiment with different architectures, including deeper and more specialized networks.
Considering the specific architecture that suits your task best. For image classification, architectures like ResNet, DenseNet, and EfficientNet are popular. For object detection, models like YOLO and Faster R-CNN are strong contenders.
Choosing appropriate loss functions for your task. For instance, use cross-entropy loss for classification, mean squared error for regression, and Intersection over Union (IoU) for object detection.
Preventing overfitting with techniques like dropout, weight decay, and batch normalization. Experiment with various dropout rates and weight decay values for optimal results.
The learning rate is a critical hyperparameter. Use learning rate schedules, such as learning rate annealing, to adapt the learning rate during training.
Optimal batch size depends on your dataset and available hardware. Smaller batches can result in better convergence, while larger batches can make better use of parallelism.
Experimenting with optimizers like Adam, SGD, or RMSprop. Their performance can vary depending on the dataset and model architecture.
Using cross-validation to evaluate model performance. It helps identify the best hyperparameters while providing a more robust estimate of model performance.
When fine-tuning pretrained models, consider freezing early layers (feature extraction) and only training the later layers on your task-specific data. This can speed up training and prevent overfitting.
Implementing early stopping based on a validation metric. This prevents overfitting and saves training time.
Applying regularization techniques during fine-tuning as well. These can help fine-tuned models generalize better.
These best practices encompass the essence of successful computer vision projects. Remember that no one-size-fits-all solution exists, and experimentation is key. Understanding the nuances of your specific task and dataset is crucial. By following these guidelines and continuously refining your approach, you can harness the full potential of deep learning in computer vision.
Happy coding and visioning!
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Basic Neural Networks:
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Data Processing:
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Model Selection:
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Hyperparameter Tuning:
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Fine Tuning:
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Deep Learning and Neural Network Architectures:
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Backpropagation:
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Overfitting and Regularization: