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Video Converter Software
Video converter software, also known as video encoding or video transcoding software, allows users to convert video files from one format to another, ensuring compatibility with various devices, platforms, or media players. These platforms typically support a wide range of video formats, such as MP4, AVI, MOV, MKV, and more, enabling users to adjust resolution, bitrate, and other settings during the conversion process. Video converter software often includes additional features like batch conversion, video trimming, and audio extraction, allowing for greater flexibility. By using this software, users can efficiently prepare videos for different uses, whether for sharing, editing, or playback on various devices.
Artificial Intelligence Software
Artificial Intelligence (AI) software is computer technology designed to simulate human intelligence. It can be used to perform tasks that require cognitive abilities, such as problem-solving, data analysis, visual perception and language translation. AI applications range from voice recognition and virtual assistants to autonomous vehicles and medical diagnostics.
40 Products for "shutter encoder" with 1 filter applied:
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1
Shutter Studio
Shutter Studio
Use your phone to remotely connect with professional photographers. The client or model installs Shutter app onto a smartphone which will be used for their virtual photo shoot. After the virtual photo shoot the photographer downloads their high-resolution images from Shutter app portal. Images can be retouched and sent back to the client through Shutter app portal. The service allows photographers to build a global portfolio, attracting new clients worldwide and increasing their income and profit margins. ... -
2
Universal Sentence Encoder
Tensorflow
The Universal Sentence Encoder (USE) encodes text into high-dimensional vectors that can be utilized for tasks such as text classification, semantic similarity, and clustering. It offers two model variants: one based on the Transformer architecture and another on Deep Averaging Network (DAN), allowing a balance between accuracy and computational efficiency. The Transformer-based model captures context-sensitive embeddings by processing the entire input sequence simultaneously, while the DAN-based model computes embeddings by averaging word embeddings, followed by a feedforward neural network. ... -
3
CodeT5
Salesforce
Code for CodeT5, a new code-aware pre-trained encoder-decoder model. Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. This is the official PyTorch implementation for the EMNLP 2021 paper from Salesforce Research. CodeT5-large-ntp-py is specially optimized for Python code generation tasks and employed as the foundation model for our CodeRL, yielding new SOTA results on the APPS Python competition-level program synthesis benchmark. ... -
4
Mu
Microsoft
Mu is a 330-million-parameter encoder–decoder language model designed to power the agent in Windows settings by mapping natural-language queries to Settings function calls, running fully on-device via NPUs at over 100 tokens per second while maintaining high accuracy. Drawing on Phi Silica optimizations, Mu’s encoder–decoder architecture reuses a fixed-length latent representation to cut computation and memory overhead, yielding 47 percent lower first-token latency and 4.7× higher decoding speed on Qualcomm Hexagon NPUs compared to similar decoder-only models. ... -
5
AISixteen
AISixteen
...The first step is to convert the textual description of an image into a numerical format that a neural network can process. Text embedding is a popular technique that converts each word in the text into a vector representation. After encoding, a deep neural network generates an initial image based on the encoded text. This image is usually noisy and lacks detail, but it serves as a starting point for the next step. The generated image is refined in several iterations to improve the quality. Diffusion steps are applied gradually, smoothing and removing noise while preserving important features such as edges and contours. -
6
Whisper
OpenAI
...We are open-sourcing models and inference code to serve as a foundation for building useful applications and for further research on robust speech processing. The Whisper architecture is a simple end-to-end approach, implemented as an encoder-decoder Transformer. Input audio is split into 30-second chunks, converted into a log-Mel spectrogram, and then passed into an encoder. -
7
Karlo
Kakao Brain
...We started from scratch, utilizing a vast dataset of 115 million image-text pairs, which included COYO-100M, CC3M, and CC12M. In the case of the Prior and Decoder components, we harnessed the power of ViT-L/14, a text encoder from OpenAI's CLIP repository. To optimize efficiency, we made a significant modification to the original unCLIP implementation. Instead of employing a trainable transformer in the decoder, we integrated the text encoder from ViT-L/14.Starting Price: Free -
8
MonoQwen-Vision
LightOn
...MonoQwen2-VL-v0.1 addresses these limitations by leveraging Visual Language Models (VLMs) that process images directly, eliminating the need for OCR and preserving the integrity of visual content. This reranker operates in a two-stage pipeline, initially, it uses separate encoding to generate a pool of candidate documents, followed by a cross-encoding model that reranks these candidates based on their relevance to the query. By training a Low-Rank Adaptation (LoRA) on top of the Qwen2-VL-2B-Instruct model, MonoQwen2-VL-v0.1 achieves high performance without significant memory overhead. -
9
Vectara
Vectara
...Developers can embed the most advanced NLP models for app and site search in minutes. Vectara automatically extracts text from PDF and Office to JSON, HTML, XML, CommonMark, and many more. Encode at scale with cutting edge zero-shot models using deep neural networks optimized for language understanding. Segment data into any number of indexes storing vector encodings optimized for low latency and high recall. Recall candidate results from millions of documents using cutting-edge, zero-shot neural network models. Increase the precision of retrieved results with cross-attentional neural networks to merge and reorder results. ...Starting Price: Free -
10
DeepInspect
SwitchOn, Inc
...DeepInspect leverages cutting-edge deep learning and computer vision to deliver high-speed, accurate inspections for a wide range of products such as glass bottles, capsules, and seals. The system supports over 1000 parts per minute using up to eight industrial cameras with various resolutions and shutter types. It features a no-code setup, enabling manufacturers to deploy inspections quickly without the need for data science expertise. DeepInspect integrates smoothly with industrial equipment from Siemens, Delta, Omron, and Mitsubishi, offering real-time traceability and analytics to optimize production quality. With 24/7 support and industrial-grade hardware, SwitchOn ensures reliability and long-term operation in demanding manufacturing environments. -
11
Shap-E
OpenAI
...To get the best result, you should remove the background from the input image. Load 3D models or a trimesh, and create a batch of multiview renders and a point cloud encode them into a latent and render it back. For this to work, install Blender version 3.3.1 or higher.Starting Price: Free -
12
Towhee
Towhee
...From images to text to 3D molecular structures, Towhee supports data transformation for nearly 20 different unstructured data modalities. We provide end-to-end pipeline optimizations, covering everything from data decoding/encoding, to model inference, making your pipeline execution 10x faster. Towhee provides out-of-the-box integration with your favorite libraries, tools, and frameworks, making development quick and easy. Towhee includes a pythonic method-chaining API for describing custom data processing pipelines. We also support schemas, making processing unstructured data as easy as handling tabular data.Starting Price: Free -
13
DevBox
DevBox
71 handcrafted tools (generators, converters, encoders, etc...), 20 cheat-sheets and 65 code snippets for developers and designers, available in multiple platforms. DevBox works entirely offline, so you never have to worry bout sensitive data since it never leaves the app. All tools respond to your actions instantly, so you can always see the end result without delay. Plenty of viewers are there for you to quickly check out data in varius formats (CSV, JSON, JWT, etc).Starting Price: $25 one-time payment -
14
SmolVLM
Hugging Face
...It works with both text and image inputs, providing highly efficient results while being optimized for smaller, resource-constrained environments. Built with SmolLM2 as its text decoder and SigLIP as its image encoder, the model offers improved performance for tasks that require integration of both textual and visual information. SmolVLM-Instruct can be fine-tuned for specific applications, offering businesses and developers a versatile tool for creating intelligent, interactive systems that require multimodal inputs.Starting Price: Free -
15
Swarm
OpenAI
...It is designed to be scalable and highly customizable, making it suitable for scenarios involving a large number of independent capabilities and instructions that are challenging to encode into a single prompt. Swarm operates entirely on the client side and, like the Chat Completions API it utilizes, does not store state between calls. This stateless nature allows for the construction of scalable, real-world solutions without a steep learning curve. Swarm agents are distinct from assistants in the assistants API; they are named similarly for convenience but are otherwise completely unrelated. ...Starting Price: Free -
16
Seed-Music
ByteDance
Seed-Music is a unified framework for high-quality and controlled music generation and editing, capable of producing vocal and instrumental works from multimodal inputs such as lyrics, style descriptions, sheet music, audio references, or voice prompts, and of supporting post-production editing of existing tracks by allowing direct modification of melodies, timbres, lyrics, or instruments. It combines autoregressive language modeling with diffusion approaches and a three-stage pipeline comprising representation learning (which encodes raw audio into intermediate representations, including audio tokens, symbolic music tokens, and vocoder latents), generation (which transforms these multimodal inputs into music representations), and rendering (which converts those representations into high-fidelity audio). The system supports lead-sheet to song conversion, singing synthesis, voice conversion, audio continuation, style transfer, and fine-grained control over music structure. -
17
Arena Autonomy OS
Arena
...Similar to a physical robot, Autonomy OS is composed of three components, the sensor, the brain, and the arm. The sensor measures, the brain makes decisions, and the arm takes action. The whole system operates automatically and in real time. Autonomy OS ingests and encodes heterogeneous data with different latency profiles, from streaming real-time and structured time series, to unstructured data like images and text, into features that train machine learning models. Autonomy OS also augments data with contextual data from Arena’s Demand Graph, a daily updating index of factors that affect consumer demand and supply, from product prices and availability by location, to demand proxies from social media platforms. ... -
18
DeepSeek-VL
DeepSeek
...The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead.Starting Price: Free -
19
MedGemma
Google DeepMind
...Developers can use MedGemma to accelerate building healthcare-based AI applications. MedGemma currently comes in two variants: a 4B multimodal version and a 27B text-only version. MedGemma 4B utilizes a SigLIP image encoder that has been specifically pre-trained on a variety of de-identified medical data, including chest X-rays, dermatology images, ophthalmology images, and histopathology slides. Its LLM component is trained on a diverse set of medical data, including radiology images, histopathology patches, ophthalmology images, and dermatology images. ... -
20
Pinecone Rerank v0
Pinecone
Pinecone Rerank V0 is a cross-encoder model optimized for precision in reranking tasks, enhancing enterprise search and retrieval-augmented generation (RAG) systems. It processes queries and documents together to capture fine-grained relevance, assigning a relevance score from 0 to 1 for each query-document pair. The model's maximum context length is set to 512 tokens to preserve ranking quality.Starting Price: $25 per month -
21
ColBERT
Future Data Systems
ColBERT is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. It relies on fine-grained contextual late interaction: it encodes each passage into a matrix of token-level embeddings. At search time, it embeds every query into another matrix and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators. These rich interactions allow ColBERT to surpass the quality of single-vector representation models while scaling efficiently to large corpora. ...Starting Price: Free -
22
NVIDIA DeepStream SDK
NVIDIA
NVIDIA's DeepStream SDK is a comprehensive streaming analytics toolkit based on GStreamer, designed for AI-based multi-sensor processing, including video, audio, and image understanding. It enables developers to create stream-processing pipelines that incorporate neural networks and complex tasks like tracking, video encoding/decoding, and rendering, facilitating real-time analytics on various data types. DeepStream is integral to NVIDIA Metropolis, a platform for building end-to-end services that transform pixel and sensor data into actionable insights. The SDK offers a powerful and flexible environment suitable for a wide range of industries, supporting multiple programming options such as C/C++, Python, and Graph Composer's intuitive UI. ... -
23
Arctic Embed 2.0
Snowflake
...Building upon the robust foundation of previous releases, Arctic Embed 2.0 supports multiple languages, enabling developers to create stream-processing pipelines that incorporate neural networks and complex tasks like tracking, video encoding/decoding, and rendering, facilitating real-time analytics on various data types. The model leverages Matryoshka Representation Learning (MRL) for efficient embedding storage, allowing for significant compression with minimal quality degradation. This advancement ensures that enterprises can handle demanding workloads such as training large-scale models, fine-tuning, real-time inference, and high-performance computing tasks across diverse languages and regions.Starting Price: $2 per credit -
24
WebOrion Protector Plus
cloudsineAI
...At the core of its capabilities is ShieldPrompt, a multi-layered defense system that utilizes context evaluation through LLM analysis of user prompts, canary checks by embedding fake prompts to detect potential data leaks, pand revention of jailbreaks using Byte Pair Encoding (BPE) tokenization with adaptive dropout. -
25
Diffgram Data Labeling
Diffgram
...More degrees of freedom through: Radio buttons. Multiple select. Date pickers. Sliders. Conditional logic. Directional Vectors. And more! You can capture complex knowledge and encode it into your AI. Streaming Data Automation Up to 10x Faster then manual labelingStarting Price: Free -
26
Qdrant
Qdrant
Qdrant is a vector similarity engine & vector database. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more! Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively utilise ready-made client for Python or other programming languages with additional functionality. Implement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. ... -
27
Depthify
Depthify
...We first run a monocular depth network which predicts the metric depth of each pixel in each image. Next, we convert the RGB and depth images into the left and right eyes of a stereo image. Finally, we encode the results into either an .HEIC image or MV-HEVC video which can be viewed on the Apple Vision Pro or Meta Quest. Converting RGB images to spatial photos is useful for various computer vision and 3D modeling applications. It enables the creation of depth maps, stereo images, and HEIC files for use with Apple Vision Pro and Meta Quest. ... -
28
Klee
Klee
...This means you can keep sensitive data on-premises while leveraging it to enhance the model‘s response capabilities. To implement RAG locally, you first need to segment documents into smaller chunks and then encode these chunks into vectors, storing them in a vector database. These vectorized data will be used for subsequent retrieval processes. When a user query is received, the system retrieves the most relevant chunks from the local knowledge base and inputs these chunks along with the original query into the LLM to generate the final response. ... -
29
csv2ai
csv2ai
...SEO-Optimized Content: Increase the online visibility of your products with concise SEO summaries. Support for Various CSV Formats: Work flexibly with different CSV formats and encodings without limitations. Customizable Functions: Choose the features you need – from optimizing product titles to translation. Simple, Effective Solutions: csv2ai offers a user-friendly platform for the quick processing and enhancement of your product data. Save hours of work and optimize 1000s of entries with csv2ai!Starting Price: $19 per month -
30
Pixtral Large
Mistral AI
Pixtral Large is a 124-billion-parameter open-weight multimodal model developed by Mistral AI, building upon their Mistral Large 2 architecture. It integrates a 123-billion-parameter multimodal decoder with a 1-billion-parameter vision encoder, enabling advanced understanding of documents, charts, and natural images while maintaining leading text comprehension capabilities. With a context window of 128,000 tokens, Pixtral Large can process at least 30 high-resolution images simultaneously. The model has demonstrated state-of-the-art performance on benchmarks such as MathVista, DocVQA, and VQAv2, surpassing models like GPT-4o and Gemini-1.5 Pro. ...Starting Price: Free
