Search Results for "image processing" - Page 7
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Showing 162 open source projects for "image processing"
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C++ library that implements basic data structures and algorithms for computer vision and image processing applications
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The Data Fusion Peer is a multitier computer vision internet application. The system provides image processing, motion tracking, and visualization information. Application will convert data into 3-Deminsional and other digital environments.
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GLM-5.3-Flash
Efficient 320B multimodal MoE model for coding and autonomous agents
GLM-5.3-Flash is Z.ai’s natively multimodal model designed for efficient coding, agentic engineering, reasoning, and long-context workloads. It uses a sparse architecture with 320B total parameters and only 18B active parameters, targeting high capability with substantially lower inference costs. The model introduces a hybrid architecture combining sparse and linear attention to reduce long-context serving costs while retaining precise understanding across large inputs. It also uses... -
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Gemma 4
Google’s flagship dense multimodal model for coding and reasoning
Gemma 4 is Google DeepMind’s flagship dense open-weight multimodal model, designed for high-end reasoning, coding, agentic workflows, and multimodal understanding. The model contains approximately 30.7B parameters and supports text and image inputs with text generation output, while also processing video as image-frame sequences. Built as the most capable model in the Gemma 4 family, it combines strong reasoning performance with a large 256K-token context window and configurable thinking modes. Gemma 4 31B supports native function calling, structured outputs, and more than 140 languages, making it suitable for enterprise assistants, coding agents, document analysis, and multilingual applications. ... -
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Ministral 3 3B Base 2512
Small 3B-base multimodal model ideal for custom AI on edge hardware
...It supports dozens of languages, making it practical for multilingual, global, or distributed environments. With a large 256k token context window, it can handle long documents, extended inputs, or multi-step processing workflows even at its small size. -
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NVIDIA Nemotron 3.5 Bolt 74B-A9B
Full-precision 74B multimodal MoE for AI agents and model fine-tuning
NVIDIA Nemotron 3.5 Bolt 74B-A9B BF16 is an open-weight, full-precision vision-language model designed for customization, post-training, and autonomous AI agent development. Compressed from Nemotron 3.5 Super VL using NVIDIA's Star Elastic method, it contains 74B total parameters with 9B active per token. Its hybrid Mamba-2–Transformer LatentMoE architecture integrates a C-RADIO v4-H vision encoder for processing text, images, and video. The model supports a 256K-token context window,... -
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NVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB
24GB multimodal MoE model for local coding, reasoning, and AI agents
NVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB is an open-weight multimodal AI model optimized for running autonomous agents on systems with 24GB of GPU memory. Derived from Nemotron 3.5 Super VL through NVIDIA's Star Elastic compression method, it features 74B total parameters with approximately 9B activated per token. Its hybrid Mamba-2–Transformer LatentMoE architecture incorporates a C-RADIO v4-H vision encoder for processing text, images, and video. The model supports a 256K-token context window... -
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Inkling-Small
Efficient multimodal MoE model for coding, tools, and reasoning
Inkling-Small is an open-weight general-purpose multimodal model from Thinking Machines Lab, designed for agentic systems, coding assistants, chatbots, retrieval workflows, and natural-language applications. It accepts text, images, and audio as input and produces text output, with multilingual and multi-programming-language capabilities. The model uses a sparse Mixture-of-Experts architecture with 276B total parameters and 12B active per token, enabling strong performance with lower... -
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MiMo-V2.5
Omnimodal AI model for agents, coding, and long-context tasks
MiMo-V2.5 is a native omnimodal large language model developed by Xiaomi, designed for advanced agentic workflows, multimodal reasoning, and long-context processing. Built on a Mixture-of-Experts architecture with approximately 309B total parameters and around 15B activated per inference, it balances high capability with efficient execution. The model natively processes text, images, video, and audio within a unified system, enabling cross-modal understanding and complex task execution in a... -
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Ministral 3 8B Instruct 2512
Compact 8B multimodal instruct model optimized for edge deployment
Ministral 3 8B Instruct 2512 is a balanced, efficient model in the Ministral 3 family, offering strong multimodal capabilities within a compact footprint. It combines an 8.4B-parameter language model with a 0.4B vision encoder, enabling both text reasoning and image understanding. This FP8 instruct-fine-tuned variant is optimized for chat, instruction following, and structured outputs, making it ideal for daily assistant tasks and lightweight agentic workflows. Designed for edge deployment,... -
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NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-B
Base 120B multimodal MoE for custom AI training and fine-tuning
NVIDIA Nemotron 3.5 Super VL 120B-A12B Base BF16 is an open-weight foundation vision-language model designed for continued pretraining, fine-tuning, distillation, and custom multimodal AI development. It uses a hybrid Mamba-2–Transformer Mixture-of-Experts architecture with 120B total parameters and 12B active per token, combining 512 routed experts with a C-RADIO vision encoder. The model processes text, images, and video and supports context windows up to one million tokens. Its language... -
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Ministral 3 14B Instruct 2512
Efficient 14B multimodal instruct model with edge deployment and FP8
Ministral 3 14B Instruct 2512 is the largest model in the Ministral 3 family, delivering frontier performance comparable to much larger systems while remaining optimized for edge-level deployment. It combines a 13.5B-parameter language model with a 0.4B-parameter vision encoder, enabling strong multimodal understanding in both text and image tasks. This FP8 instruct-tuned variant is designed specifically for chat, instruction following, and agentic workflows with robust system-prompt...
