TensorSense provides an AI agent that automates the development of enterprise-level computer vision software by allowing users to describe their requirements in plain English. This solution addresses the high failure rate of computer vision projects by eliminating the need for specialized skills and deep domain expertise.
Funding
Funding not disclosed
Founders
Product
Problem
Developing enterprise-level computer vision software is complex, often resulting in high failure rates due to the need for specialized skills and deep domain expertise. Traditional methods require extensive manual coding and fine-tuning, making the process time-consuming and resource-intensive.
Solution
TensorSense offers an AI-powered platform designed to automate the development of computer vision applications. The platform allows users to define their requirements in plain English, which the AI agent then translates into functional computer vision software. By eliminating the need for specialized skills, TensorSense democratizes access to computer vision technology, enabling businesses to rapidly prototype, test, and deploy solutions tailored to their specific needs. The platform includes tools for fine-tuning open-source multimodal LLMs, parsing and annotating data, and benchmarking inference performance. TensorSense also provides pre-trained models and deployment kits to streamline the development process.
Target Audience
The primary target audience includes enterprises seeking to implement computer vision solutions without the need for extensive in-house expertise, as well as developers and researchers working on multimodal AI models.
Features
- VLMTrainingKit: Fine-tunes open-source multimodal LLMs for computer vision tasks, supporting video and image inputs for object detection, segmentation, and QA.
- DataBuilder: Parses and automatically annotates data for training and evaluating LLMs for computer vision tasks.
- Gemamba 2B (Base): A Mamba-based video encoder combined with an LLM for Video QA.
- BenchmarkingInference: An inference engine designed to efficiently run video-based LLM benchmarks, leveraging parallelism to maximize resource usage.
- VideoEmbeddings-General: Turns videos into semantic vectors for integration into Retrieval-Augmented Generation (RAG) systems.
- VideoBenchmark-CCTV: Evaluates model performance on video footage from CCTV cameras, delivering scores for logging events involving people, objects, and environment.
- VLMDeploymentKit: Deploys fine-tuned multimodal LLMs on edge devices or private clouds, orchestrating different adapters and using RAGs.