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Convolutional Neural Networks (CNNs) are deep neural networks specialized for processing multidimensional data like images. By applying filters, they detect patterns such as edges, textures, and shapes, building spatial hierarchies of features. Convolutions, a key operation in CNNs, identify local patterns, which are then combined to recognize complex structures. This capability makes CNNs highly effective for image recognition and classification tasks, such as defect detection in manufacturing production lines.
The CNN architecture is a deep neural network (an input layer, hidden layers and an output layer) with the following architecture:
The hidden layers are composed of convolutional layers and of pooling layers. Convolutional layers apply kernels (filters) that perform convolutions to detect local patterns such as edges and textures in the input data, creating feature maps. Then, pooling layers reduce the size of these feature maps, allowing the neural network to feeding forward only the essential information, avoiding passing on unnecessary information. This allows CNNs to effectively capture spatial dependencies in visual data.
The convolutional layers use kernels to scan the input image. These kernels have weights which allow for the convolution operations - however, each kernel remains fixed throughout the image’s scan. This allows the model to detect specific features throughout the entire image regardless of their position (translation invariance) without an underlying increase of the amount of parameters in the network: shared weights enhances computational efficiency. This is make CNN robust to pattern recognition.
CNNs learn to extract hierarchical features, where each layer builds upon the previous one to capture increasingly complex patterns. Initial layers might detect simple features like edges or textures, while deeper layers identify more complex structures, such as parts of objects or entire objects. This hierarchical learning allows CNNs to understand and represent the data at multiple levels of abstraction, making them particularly powerful for visual recognition tasks.
CNNs enable the detection of local patterns in images through convolution layers while maintaining computational efficiency with information flow management through pooling layers. The use of kernels enhances computational efficiency by reducing the total amount of parameters in the model, resulting in a lightweight model.
BENEFITS:
DRAWBACKS:
In CNN training, raw datasets and high-resolution formats like TIFF images significantly increase storage demands. Storing intermediate results, like feature maps, adds to the issue. UltiHash addresses this with an advanced byte-level deduplication algorithm, removing redundancies and cutting storage needs by up to 60%. This allows organizations to manage growing data without skyrocketing storage costs.
Training CNN models is costly, with rising storage and compute demands. Faster model training reduces both costs and competitiveness risks. UltiHash boosts performance with a lightweight algorithm, offering high throughput and low latency. By caching frequently accessed data, multiple applications can share it, improving read performance without increasing resource load.
For CNNs, smooth data management across systems is crucial. UltiHash is Kubernetes-native, making it easy to deploy across cloud and on-premise setups. Its S3-compatible API ensures broad compatibility with tools like Apache Kafka, AWS SageMaker, and PySpark, while supporting open table formats like Apache Hudi and Delta Lake for seamless integration.
