Image Classification with Deep Learning.pdf
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Image classification is essential in computer vision, enabling automatic categorization of visual data through deep learning techniques like CNNs. Various architectures such as VGGNet, ResNet, and InceptionNet enhance accuracy and efficiency, with applications in healthcare, agriculture, retail, and autonomous vehicles. Recent trends include Vision Transformers, self-supervised learning, and multimodal models that expand the capabilities of image classification.
Image Classification with Deep Learning.pdf
- 1. Image classification is a crucial component of computer vision that enables systems to automatically categorize and interpret visual data. For example: Cat vs. Dog With the rise of deep learning, a field of artificial intelligence inspired by the structure of the human brain, high accuracy in image classification is achieved. Gp Introduction cat dog
- 2. Why Deep Learning ? Automatic feature extraction (no manual feature engineering). High accuracy with large datasets.
- 3. A deep learning model that processes structured grid data like images CNN Convolutional Neural Networks Convolutional Layer CNNs use small filters that slide across the image, detecting specific patterns like edges, corners, and textures. Feature Maps The outputs of these filters form feature maps, representing the presence of detected features at different locations. Pooling Layer Pooling layers down-sample feature maps, reducing their size while preserving important features and making the network more efficient. Fully-connected (FC) layer Each node in the output layer connects directly to a node in the previous layer
- 4. VGGNet Known for simplicity and depth. researchers at University of Oxford ResNet: Introduces residual connections (skip connection technique) researchers at Microsoft Research InceptionNet: Multi-scale feature extraction. researchers at Google EfficientNet: Scaling depth, width, and resolution researchers at Google AI and more.......... Architectures
- 5. VGGNet
- 6. Healthcare: Disease diagnosis (e.g., tumor detection). Agriculture: Crop and pest classification. Retail & Security: Facial recognition, object detection in surveillance. Autonomous Vehicles: Identifying objects, pedestrians, and road signs Applications
- 7. Recent Trends Vision Transformers (ViTs) alternative to CNNs Utilizes attention mechanisms Self-Supervised Learning (SSL) Learning representations without labeled data. Multimodal Models Leveraging both visual and textual data (e.g., OpenAI's CLIP) Few-Shot and Zero-Shot Learning Enables classification with minimal labeled data
- 9. Image classification is a powerful tool driven by deep learning, with evolving applications and methods. Adopting the latest trends and addressing challenges will contribute in the further progress. Conclusion
- 10. References 1. Intel. (n.d.). Convolutional Neural Networks (CNNs) in Computer Vision. Intel. Retrieved from https:// www.intel.com/content/www/us/en/internet-of-things/computer-vision/convolutional-neural-networks.html 2. AI Multiple. (n.d.). Image Classification: A Comprehensive Guide. Retrieved from https://research.aimultiple.com/ image-classification/ 3. Ng, P. (2018, Oct 10). The W3H of AlexNet, VGGNet, ResNet, and Inception. Towards Data Science. Retrieved from https://towardsdatascience.com/the-w3h-of-alexnet-vggnet-resnet-and-inception-7baaaecccc96 4. Viso.ai. (n.d.). Vision Transformer (ViT) in Deep Learning*. Retrieved from https://viso.ai/deep-learning/vision- transformer-vit/ 5. Viso.ai. (n.d.). CLIP Machine Learning Model: How CLIP Works*. Retrieved from https://viso.ai/deep-learning/clip- machine-learning/ 6. Analytics Vidhya. (2022, Dec 22). Know About Zero-shot, One-shot, and Few-shot Learning. Retrieved from https:// www.analyticsvidhya.com/blog/2022/12/know-about-zero-shot-one-shot-and-few-shot-learning/