Tabular data2025–2026
Stable AI (Beijing, with Tsinghua University) · China
A table model that alone can classify, predict numbers and fill in missing data. The lightweight LimiX-2M runs on an ordinary computer.
- Filling gaps in 1C and CRM exports
- Churn prediction and scoring
- Classifying customers and products
- Sizes
- 2M – 16M and LimiX-2
- Hardware
- from: Laptop
Autonomous driving2020–2026
comma.ai · USA
An open driver assistance system: a neural network keeps the lane and controls speed from a camera, plus a driver attention monitoring model. The models live right in the repository and are updated constantly.
- A research testbed for driver assistance systems
- Studying driver attention monitoring with an in-cabin camera
- Comparison with your own lane-keeping algorithms
- Sizes
- compact, designed for an in-vehicle device
- Hardware
- from: Laptop
Voice assistants2026
Samsung · South Korea
Tiny audio-understanding models for smartphones: they listen to speech, music and ambient sounds and answer in text - describing a recording and answering questions about it. They run on the device itself; prompts and answers are in English - no other languages are present in the training data.
- Describing an audio recording in words
- Answering questions about a sound
- Identifying the type of sound and the setting
- Sizes
- 99M – 356M
- Hardware
- from: Laptop
Computer vision2024–2026
Microsoft Research · USA
Reconstructs the 3D geometry of a scene from one photo: depth in meters, a point cloud and surface normals.
- Measuring rooms and objects from photos
- 3D point cloud from a single shot
- Preparing data for robots and AR
- Sizes
- ViT-S – ViT-G
- Hardware
- from: Laptop
Forecasting2024–2026
Google · USA
A ready-made Google forecasting model: forecasts any time series without training on your data.
- Sales and demand forecasting
- Purchase and inventory planning
- Load and traffic forecasting
- Sizes
- 200M – 500M
- Hardware
- from: Laptop
Robotics2025–2026
GigaAI · China
A robot control model trained mostly on synthetic data from a world model. It reduces spending on collecting data from real robots.
- Controlling a robot arm
- Fine-tuning on a small amount of your own data
- Sorting and assembly pilots
- Sizes
- 3.5B
- Hardware
- from: 1 GPU
TextOllama2024–2026
NVIDIA · USA
NVIDIA models for agents and reasoning, optimized to run fast on its GPUs. Nemotron 3 is a Mamba and MoE hybrid from 4B to 550B; Nano Omni handles video, audio and images (English only).
- Agents with tool calling
- Reasoning and calculation tasks
- Answers based on long documents
- Sizes
- 4B – 550B-A55B
- Hardware
- from: Laptop
Weather and climate2023–2026
Google DeepMind · UK
Google DeepMind's family of global weather models: GraphCast (10-day forecast), GenCast (probabilistic ensemble) and WeatherNext 2 with cyclone forecasting. Since August 2026 the weights are cleared for commercial use.
- Medium-range weather forecasts for planning shifts, voyages and deliveries
- Probabilistic assessment of extreme weather for insurance portfolios
- Tropical cyclone track forecasts for marine and port operations
- Sizes
- from lightweight 1° versions to full 0.25°
- Hardware
- from: 1 GPU
Autonomous driving2025–2026
NVIDIA · USA
Vision-language-action models for self-driving vehicles: they plan a trajectory from camera video and explain the decision in text. Used to develop and test autopilot systems, not as a ready-made autopilot.
- Auto-labeling camera recordings to train your own driver assistance systems
- Analyzing complex road scenes with text explanations
- Testing autopilot systems in simulation on rare scenarios
- Sizes
- 10B – 34B
- Hardware
- from: 1 GPU
Autonomous driving2026
Alibaba (Qwen team) · China
An autonomous driving model based on Qwen3.5-4B: 3D detection of objects around the vehicle, answers to questions about the road scene and trajectory planning in one model.
- A perception and planning prototype for autonomous vehicles on closed sites
- Answering questions about camera recordings when reviewing incidents
- Labeling road scenes to train your own models
- Sizes
- 4B
- Hardware
- from: 1 GPU
Robotics2025–2026
NVIDIA · USA
"World" models for robots and self-driving vehicles: they generate realistic video of physical scenes and predict actions. Cosmos 3 combines understanding, generation and control.
- Synthetic video for training robots and self-driving vehicles
- Testing scenarios in simulation
- Robot control (Policy versions)
- Sizes
- 2B – 65B
- Hardware
- from: 1 GPU
Robotics2026
Ant Group (Robbyant) · China
A robot control model from Ant Group trained on a large volume of data from real robots. Version 2.0 works with different types of robot arms.
- Controlling a two-armed robot
- Fine-tuning for your own operation
- Assembly and sorting pilots
- Sizes
- 4B – 6B
- Hardware
- from: 1 GPU
Robotics2026
Xiaomi · China
Open robot control models from Xiaomi. Robotics-1 is designed for household and kitchen tasks, U0 combines scene understanding and action.
- Controlling a robot arm by command
- Household and service scenarios
- Base for fine-tuning
- Sizes
- 4B – 5B
- Hardware
- from: 1 GPU
TextGGUF2025–2026
Meituan · China
Models from Meituan, China's largest delivery service. LongCat-Flash adjusts compute to query complexity; LongCat-2.0 has 1.6 trillion parameters under MIT. Omni models (Flash-Omni, Next) and AudioDiT speech synthesis too.
- Agents for orders and service processes
- Corporate assistant
- Analysis of long documents
- Sizes
- 1B – 1.6T-A48B
- Hardware
- from: Laptop
TextOllama2024–2026
LG AI Research · South Korea
Korean-English models from LG. Most of the line is non-commercial, but the flagship K-EXAONE 2.0 with 750 billion parameters is released under Apache 2.0.
- Corporate assistant
- Working with Korean and English texts
- Analysis of documents and images (4.5)
- Sizes
- 1.2B – 750B-A37B
- Hardware
- from: Laptop
Tabular data2026
LG AI Research · South Korea
LG's small tabular model: with 21M parameters it nearly matches the leaders in classification and regression accuracy. Weights are for non-commercial use only.
- Pilot churn forecasts
- Testing scoring hypotheses
- Exploring customer data
- Sizes
- about 21M
- Hardware
- from: Laptop
Weather and climate2024–2026
Microsoft Research · USA
A foundation model of Earth's atmosphere: global weather forecasts, plus separate versions for air quality and ocean waves. Computes a forecast in seconds instead of hours on a supercomputer.
- Your own forecast of temperature, wind and precipitation for company locations
- Sea state estimates for planning voyages and port operations
- Air pollution forecasts for industrial sites
- Sizes
- about 1.3B (a small test version is available)
- Hardware
- from: 1 GPU
Weather and climate2022–2026
NVIDIA · USA
NVIDIA's set of weather and climate models: global FourCastNet forecasts, downscaling to kilometers (CorrDiff), regional storm forecasts (StormCast), climate generation (cBottle, Atlas). Run via Earth2Studio.
- Global forecasts followed by downscaling to the region you need
- Short-term forecasts of thunderstorms and heavy rain for dispatch services
- Generating many weather scenarios for stress tests
- Sizes
- 98M – 2.5B (FourCastNet 3 — about 711M)
- Hardware
- from: 1 GPU
Computer vision2025–2026
Roboflow · USA
Real-time object detector, an open alternative to YOLO without AGPL. Supports segmentation (object outlines) and, since 2026, keypoints.
- Object detection in video and photos
- Precise outlines of parts and defects
- Fine-tuning for your own object classes
- Sizes
- Nano – 2XL
- Hardware
- from: Laptop
Forecasting2025–2026
NXAI · Austria
A compact forecasting model on the xLSTM architecture, a leader in open benchmarks despite its small size. Runs fast on a regular CPU.
- Demand and sales forecasting
- Energy consumption forecasting
- Forecasts on modest hardware and on site
- Sizes
- about 35M to 82M
- Hardware
- from: Laptop
Image + textOllama2024–2026
Moondream (M87 Labs) · USA
A small, fast vision model for product use cases: answering questions, finding and pointing to objects, captions. Moondream 3.1 is a 9B MoE with 2B active.
- Finding and counting objects in photos
- Checking photos from field reports
- Captions and tags for a catalogue
- Sizes
- 2B – 9B-A2B
- Hardware
- from: Laptop
Documents and OCRRU2022–2026
Baidu (PaddlePaddle) · China
Classic lightweight PaddleOCR models: detecting and recognizing lines of text plus page layout. They run on CPUs and phones; there is a separate model for East Slavic languages, including Russian.
- Recognizing text on scans, photos and screens
- Reading labels, displays and markings in production and warehouses
- Page layout: tables, formulas, stamps, headings
- Sizes
- from 1.5M to tens of millions of parameters
- Hardware
- from: Laptop
Satellite and geo2025–2026
Allen Institute for AI (Ai2) · USA
Ai2's family of models for Sentinel-1, Sentinel-2 and Landsat imagery, with ready-made fine-tunes for mangroves, deforestation and ecosystem types. The license excludes the extractive industries.
- Monitoring deforestation and forest condition across the supply chain
- Classifying land and crops from image series
- Image embeddings for finding similar plots
- Sizes
- Nano – Large (Base about 114M)
- Hardware
- from: Laptop
3D2024–2026
VAST (TripoSR together with Stability AI) · China
VAST family: a 3D model from a single photo. TripoSR runs in under a second, TripoSG gives cleaner geometry, TripoSplat builds a scene from Gaussian points.
- 3D product model from a photo
- Object assets for games and AR
- Quick 3D prototype for printing
- Sizes
- up to 1.5B
- Hardware
- from: Laptop
Robotics2025–2026
Ai2 (Allen Institute for AI) · USA
A fully open robot control model that first "reasons" about space and trajectory, then acts. Its reasoning can be checked.
- Controlling a robot arm with explainable steps
- Fine-tuning for your own robot
- Research pilots
- Sizes
- 5B – 8B
- Hardware
- from: 1 GPU
3D2025–2026
Meta and the University of Oxford (VGG) · USA / UK
Reconstructs a 3D scene from one, several or hundreds of photos in seconds: camera positions, depth and a point cloud. Best Paper at CVPR 2025.
- 3D model of a room or object from a photo series
- Camera pose estimation for photogrammetry
- Point cloud for measurements and comparison with the plan
- Sizes
- about 1.2B
- Hardware
- from: 1 GPU
Rerankers2025–2026
NVIDIA · USA
A small 1B reranker from NVIDIA. The vl version also takes document pages as images, not just text. The card states multilingual support without listing the languages.
- Reordering passages before an AI assistant answers
- Sorting retrieved scan and PDF pages
- Search across internal policies and instructions
- Sizes
- 1B
- Hardware
- from: Laptop
Forecasting2024–2026
THUML, Tsinghua University · China
A compact forecasting foundation model from the Tsinghua lab: trained on a large set of diverse series and fine-tunable on your own data.
- Forecasting demand and load
- Forecasting sensor readings on the shop floor
- Fine-tuning forecasts on your own history
- Sizes
- 84M (timer-base)
- Hardware
- from: Laptop
Computer vision2023–2026
Meta · USA
Selects any object in photos and videos with a click or a box. The basis for background removal and object counting.
- Background removal from product photos
- Counting objects in photos
- Data labeling for training
- Sizes
- 91M – ~0,85B
- Hardware
- from: Laptop
Robotics2025–2026
NVIDIA · USA
NVIDIA's foundation model for humanoid robots and robot arms: it sees, understands a command and outputs movements. Built into the Isaac ecosystem.
- Controlling humanoid robots
- Fine-tuning for your own robotic cell
- Training in simulation with transfer to a real robot
- Sizes
- 2B – 3B
- Hardware
- from: 1 GPU
Text2025–2026
Reka AI · USA
Compact Reka models: Flash 3 (21B) for reasoning and Reka Edge (7B), which quickly analyzes images and video on-device.
- Photo and video analysis (Edge)
- Object detection in images
- Reasoning tasks (Flash)
- Sizes
- 7B – 21B
- Hardware
- from: Laptop
Image + textGGUF2023–2026
Shanghai AI Laboratory (OpenGVLab) · China
A large family of Chinese vision models sized from 1B to 241B. InternVL-U (4B) combines image understanding, generation and editing.
- Understanding documents, diagrams and charts
- Answering questions about photos
- Video analysis
- Sizes
- 1B – 241B-A28B
- Hardware
- from: Laptop
Image + text2024–2026
Ai2 (Allen Institute for AI) · USA
Fully open vision models from Ai2 (weights and data). They can point to a spot in an image and count objects; Molmo2 understands video, MolmoWeb controls a browser.
- Counting products and objects in photos
- Pointing to where an item is in an image
- Video analysis
- Sizes
- 1B-A7B – 72B
- Hardware
- from: Laptop
Tabular data2025–2026
Lexsi Labs · India
Recent open models for tabular data: they predict from a few examples given in the prompt, with no task-specific training.
- Classification and forecasting on tables with no separate training
- Quickly testing models on new datasets
- Assessing features in large tables
- Sizes
- size not stated on the model card
- Hardware
- from: Laptop
Voice assistantsGGUF2025–2026
OpenBMB (ModelBest, Tsinghua University) · China
A small model that sees, hears and replies by voice in real time, and can clone a voice. Voice dialogue in English and Chinese, text in 30+ languages.
- Voice assistant on your own server
- Analyzing videos and documents
- Voice answers about a camera image
- Sizes
- 8B – 9B
- Hardware
- from: Laptop
Tabular data2025–2026
Inria (Soda team) · France
An open tabular model from the creators of scikit-learn: classifies and predicts from examples without training and handles tables of up to hundreds of thousands of rows. The license allows business use.
- Predicting customer churn
- Scoring applications and deals
- Classifying customers from 1C and CRM data
- Sizes
- about 25–30M
- Hardware
- from: Laptop
Computer vision2023–2026
Ultralytics · USA
The most widely used real-time object detector: finds and marks items in video even on modest hardware. YOLOv5 came out back in 2020; the catalog starts from YOLOv8.
- Counting people, cars and goods on video
- Checking hard hats and workwear
- Spotting defects on the production line
- Sizes
- 2.4M – 68M
- Hardware
- from: Laptop
TextRUOllama2023–2026
Technology Innovation Institute (TII) · UAE
A family from Abu Dhabi: from the early Falcon 40B and 180B to hybrid Falcon-H1 and tiny Falcon-H1-Tiny models of 90–600M parameters for devices.
- Assistant and answers based on documents
- Running on low-end hardware and devices
- Tool calling in simple agents
- Sizes
- 90M – 180B
- Hardware
- from: Laptop
Weather and climate2024–2026
European Centre for Medium-Range Weather Forecasts (ECMWF) · Europe (intergovernmental organization)
ECMWF's weather neural network running operationally: a 15-day forecast four times a day, an ensemble version with 51 scenarios, and since version 2, ocean waves.
- Running your own forecast from open initial data
- Ensemble forecasts to estimate the probability of frost, downpours and storms
- Wave forecasts for marine operations
- Sizes
- checkpoint of about 1 GB
- Hardware
- from: 1 GPU
3DGGUF2024–2025
Microsoft · USA
One of the strongest open 3D models: from an image or text it produces a textured mesh or a Gaussian scene. TRELLIS.2 is noticeably more detailed than the first version.
- 3D models of products and interiors from photos
- Assets for games and AR/VR
- Prototypes for 3D printing
- Sizes
- up to 4B (TRELLIS.2)
- Hardware
- from: 1 GPU
Computer visionGGUF2024–2025
ByteDance and the University of Hong Kong · China
Estimates depth, the distance to every point, from one ordinary photo or video. DA3 reconstructs scene geometry from several frames.
- Estimating distances and volumes from a camera
- Depth effects for photo and video
- Navigation for robots and drones
- Sizes
- 25M – 1.4B
- Hardware
- from: Laptop
3D2025
Meta and Carnegie Mellon University · USA
A single model builds a metric 3D reconstruction from photos, and uses camera, depth or pose data when available. One weights variant is under Apache 2.0.
- 3D reconstruction of an object or room from photos
- Exporting the scene to COLMAP format for further processing
- Depth and camera pose estimation
- Sizes
- about 1.2B
- Hardware
- from: 1 GPU
Computer vision2025
Meta · USA
Meta's family of encoders for images and video, and with PE-AV also for audio. PE-Core searches by text more accurately than SigLIP 2 (per Meta); small versions are available.
- Search photos and videos by description
- Catalog labeling and tagging
- Search across audio and video (PE-AV)
- Sizes
- size not stated on the model card
- Hardware
- from: Laptop
Tabular dataGGUF2024–2025
Zhejiang University · China
A family for working with tables and databases: it understands data structure, writes parsing code and answers questions about exports.
- Answering questions about tables and data exports
- Automated data analysis with generated code
- A helper for BI and internal reporting
- Sizes
- 7B – 72B
- Hardware
- from: 1 GPU
Satellite and geo2025
IBM and the European Space Agency (ESA) · USA / Europe
A multimodal Earth model: understands optical and radar imagery, terrain, vegetation index and land use maps, and can generate a missing data type (for example, a "see-through-clouds" image from radar).
- Analyzing fields and forests even in cloudy weather using radar imagery
- Land use maps for assessing plots
- Flood and wildfire assessment (ready-made fine-tunes available)
- Sizes
- tiny – large (checkpoints from ~200 MB to ~3.8 GB)
- Hardware
- from: Laptop
Computer vision2023–2025
IDEA Research · China
Finds any objects in an image from a text description, without training on your data: "red box", "person without a hard hat". Rex-Omni is the new VLM-based generation.
- Finding objects by description without labeling
- Automatic data labeling for training
- Checking photos against requirements
- Sizes
- 172M – 3B
- Hardware
- from: Laptop
ForecastingGGUF2024–2025
Amazon · USA
Amazon forecasting models, among the most downloaded. Chronos-2 takes external factors into account: prices, promotions, weather.
- Demand forecasting with promotions and prices
- Inventory planning
- Forecasting revenue and customer flow
- Sizes
- 8M – 710M
- Hardware
- from: Laptop
Image + textOllama2023–2025
Alibaba (Qwen team) · China
One of the strongest open vision models: reads documents, tables, charts and video, and works with user interfaces. Since Qwen3.5, vision is built directly into the main Qwen model.
- Extracting data from scanned invoices and delivery notes
- Analysing photos of products and shelves
- Analysing video and camera footage
- Sizes
- 2B – 235B-A22B
- Hardware
- from: Laptop
Robotics2025
Physical Intelligence · USA
Robot control models from Physical Intelligence: folding laundry, tidying up, handling objects. π0.5 copes better in unfamiliar settings.
- Controlling robot arms and two-armed robots
- Fine-tuning for your own operations
- Pilots for automating manual work
- Sizes
- about 3B
- Hardware
- from: 1 GPU
Satellite and geo2023–2025
IBM and NASA · USA
Foundation models for Landsat and Sentinel-2 satellite imagery that account for image time series. Ready-made fine-tunes for floods, burn scars and crop types, plus a separate weather model, WxC.
- Mapping crops and field condition over the season
- Assessing flood zones and burn scars after natural disasters
- Monitoring changes in buildings and land use
- Sizes
- tiny – 600M (imagery), 2.3B (Prithvi WxC weather)
- Hardware
- from: Laptop
Computer vision2023–2025
Meta · USA
Foundation models that turn an image into a numeric "fingerprint". They are used to build similar-image search, classification and segmentation without large labeled datasets.
- Finding similar products and photos
- Image classification on small datasets
- Base for your own quality-control models
- Sizes
- 21M – 7B
- Hardware
- from: Laptop
ForecastingGGUF2024–2025
Salesforce · USA
Salesforce's universal forecasting model for series with different frequencies and many variables. Weights are open for research only.
- Research forecasting pilots
- Comparison with other forecasting models
- Forecasts across many related series
- Sizes
- 11M – 311M
- Hardware
- from: Laptop
Robotics2025
Hugging Face · USA
A small robot control model that runs on a regular laptop. Trained on open data from the LeRobot community, suited to low-cost robot arms.
- Controlling a low-cost robot arm
- Quick robotization pilots and demos
- Training staff and students
- Sizes
- 450M
- Hardware
- from: Laptop
Satellite and geo2025
MBZUAI · UAE
A compact research model for satellite imagery, trained on both optical (Sentinel-2) and radar (Sentinel-1) data. Narrower in scope and community than Prithvi and TerraMind.
- Classification and segmentation of satellite imagery after fine-tuning
- A base for a land monitoring prototype
- Sizes
- TerraFM-B (ViT-Base)
- Hardware
- from: Laptop
Forecasting2025
THUML, Tsinghua University · China
A forecasting model that returns a set of possible scenarios rather than a single line — useful when you need a range for demand or load, not one number.
- Forecasting demand with a range of values
- Planning stock while accounting for spread
- Forecasting load on services and staff
- Sizes
- 128M (sundial-base)
- Hardware
- from: Laptop
Robotics2024–2025
Stanford, Berkeley and partners · USA
The first large open vision-language-action model: a robot arm carries out commands like "put the apple in the bowl". OFT makes it several times faster.
- Controlling a robot arm by text command
- Pilots for robotizing simple operations
- Base for fine-tuning to your own robot
- Sizes
- 7B
- Hardware
- from: 1 GPU
Computer-use agentsGGUF2025
Microsoft Research · USA
An agent model that plans actions both in an interface (buttons on screen) and for a robot (arm movements). For now more of a research base than a finished product.
- Pilots in interface control
- Research projects spanning screens and robotics
- Analyzing screenshots with an action plan
- Sizes
- 8B
- Hardware
- from: 1 GPU
Computer vision2022–2025
University of Sydney and JD Explore Academy · Australia / China
A simple, accurate model for human pose estimation via keypoints. ViTPose++ handles human, animal and whole-body poses; built into the Transformers library.
- Body keypoints in photos and video
- Motion analysis in sports and rehabilitation
- Monitoring work postures and safety practices
- Sizes
- 33M – about 1B
- Hardware
- from: Laptop
Image + textGGUF2024
Google · USA
Google's vision model built on Gemma, designed as a base for fine-tuning on a narrow task: captions, object detection, reading text.
- Fine-tuning for your own recognition task
- Finding objects in photos
- Reading text in images
- Sizes
- 3B – 28B
- Hardware
- from: Laptop
Forecasting2024
Auton Lab, Carnegie Mellon University · USA
A foundation model for numeric series: one engine is used for forecasting, anomaly detection, filling gaps and classification.
- Forecasting demand and load
- Detecting anomalies in sensor readings and metrics
- Filling gaps in historical data
- Sizes
- about 40M – 385M
- Hardware
- from: Laptop
Forecasting2024
IBM Research · USA
Tiny forecasting models from IBM: they run on an ordinary CPU and sit next to the business system without a separate GPU server.
- Forecasting sales and warehouse stock
- Forecasting energy use and equipment load
- Fast forecasts right on the company server
- Sizes
- very small: TinyTimeMixers have about 1M parameters
- Hardware
- from: Laptop
Tabular dataGGUF2024
RUCKBReasoning, Renmin University of China · China
A model for office work with tables: for a given question it returns either a direct answer or code to process the data in a table or document.
- Processing tables from Excel and documents from a text instruction
- Generating code for recalculations and selections
- Answering questions about data in reports
- Sizes
- 7B и 13B
- Hardware
- from: Laptop
Forecasting2024
The Time-MoE team · not disclosed
A forecasting model with a sparse architecture: only part of the network runs at each step, so it stays fast at a small size.
- Forecasting sales and stock levels
- Forecasting load on services and staff
- Planning purchases from history
- Sizes
- 50M and 200M
- Hardware
- from: Laptop
Tabular dataNot maintained2024
ML Foundations · USA
A foundation model for predictions on tables: it classifies and forecasts from a handful of examples, with no separate task-specific training.
- Classifying table rows from a few examples
- Predicting a value from a data row
- Quickly testing hypotheses on new datasets
- Sizes
- 8B
- Hardware
- from: 1 GPU
Image + textNot maintained2024
Microsoft · USA
A very small vision model: captions, object detection, segmentation and text reading from a single prompt. Runs even on a CPU.
- Reading text in photos
- Finding and highlighting objects
- Automatic photo captions
- Sizes
- 0.23B – 0.77B
- Hardware
- from: Laptop
Satellite and geoNot maintained2023–2024
Allen Institute for AI (Ai2) · USA
Pretrained models from the Satlas project for Sentinel-2, Landsat and high-resolution aerial imagery. The predecessor of OlmoEarth, still used in TorchGeo.
- Detecting objects in imagery: solar farms, wind turbines, ships
- Mapping roads and buildings from aerial photos
- A starting point for fine-tuning your own geo model
- Sizes
- Swin-v2 and ResNet backbones (Base)
- Hardware
- from: Laptop
Music and soundNot maintained2022
MIT · USA
A classic 2021 sound recognition model: detects 527 AudioSet event classes (siren, barking, breaking glass, music). Lightweight, runs without a GPU, in Transformers since 2022.
- Sound event recognition
- Tagging an audio archive
- Detecting alarm sounds
- Sizes
- about 87M
- Hardware
- from: Laptop
