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Cataloged from litert-community/embeddinggemma-2-text-vision-440m-litert-lm
All models by litert-community →Main Model Card: google/embeddinggemma-2
EmbeddingGemma 2 Text Vision 440M is a lightweight, multimodal (text and vision) variant of the EmbeddingGemma 2 model family, tailored for efficient on-device execution using LiteRT. By combining the 270M parameter text backbone with a modular 170M parameter vision encoder, this 440M parameter model maps text, code, and images into a single, unified 768-dimensional embedding space. It captures deep cross-modality semantics—allowing visual concepts and corresponding text queries to align co-dependently—making it perfect for on-device applications such as image search, visual document retrieval, and image classification.
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MediaPipe uses EmbeddingGemma 2 and LiteRT-LM to enable high-level, cross-platform tasks which you can experience firsthand with the following interactive web demos:
Additional Notes:
The text performance was measured by loading and running the 128 text signature. For vision benchmarking, the vision encoder used the 70 signature. The latency reported is the average of 5 iterations.
Memory was measured with each platform's native metrics and is not directly comparable across operating systems. CPU memory was measured using, rusage::ru_maxrss on Android, Linux and IOT, task_vm_info::phys_footprint on iOS and MacBook, and process_memory_counters::PrivateUsage on Windows. With the exception of task_vm_info::phys_footprint, accelerator (GPU/NPU/TPU) memory is not included. Memory measurements were taken from the second load which reads from loading caches. Memory usage on the first load may vary.
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IoT