Search Results for "image processing in java"
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Showing 4 open source projects for "image processing in java"
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MediaPipe Face Detection
Detect faces in an image
The MediaPipe Face Detection model is a high-performance, real-time face detection solution that uses machine learning to identify faces in images and video streams. It is optimized for mobile and embedded platforms, offering fast and accurate face detection while maintaining a small memory footprint. This model supports multiple face detections and is highly efficient, making it suitable for a variety of applications such as augmented reality, user authentication, and facial expression analysis. -
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Nanonets-OCR-s
State-of-the-art image-to-markdown OCR model
Nanonets-OCR-s is an advanced image-to-markdown OCR model that transforms documents into structured and semantically rich markdown. It goes beyond basic text extraction by intelligently recognizing content types and applying meaningful tags, making the output ideal for Large Language Models (LLMs) and automated workflows. The model expertly converts mathematical equations into LaTeX syntax, distinguishing between inline and display modes for accuracy. It also generates descriptive <img> tags... -
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ERNIE-4.5-VL-28B-A3B-Base-PT
Pretrained multimodal MoE model for complex text and vision tasks
ERNIE-4.5-VL-28B-A3B-Base-PT is a large-scale multimodal Mixture-of-Experts (MoE) model developed by Baidu, featuring 28 billion total parameters and 3 billion activated per token. It is pretrained to handle both text and image inputs, enabling it to excel in image-to-text and conversational AI tasks. The model uses a staged training strategy—starting with text-only training and then integrating vision components using ViT, adapters, and visual experts for robust cross-modal understanding... -
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ERNIE-4.5-VL-424B-A47B-Base-PT
Multimodal MoE model fine-tuned for text and visual comprehension
ERNIE-4.5-VL-424B-A47B-Base-PT is a powerful multimodal Mixture-of-Experts (MoE) model developed by Baidu and fine-tuned for enhanced performance across both text and visual tasks. It builds upon the pretraining of ERNIE 4.5, using modality-specific post-training techniques to optimize for general-purpose natural language processing and visual-language reasoning. The model employs a heterogeneous MoE architecture with modality-isolated routing and loss-balancing mechanisms to ensure efficient... -
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