Key Capabilities
Object Detection and Recognition
Detect, classify, and track objects across images and video frames with configurable confidence thresholds and custom class taxonomies
Visual Search and Similarity
Find visually similar content across millions of images and video frames using learned embeddings that capture shape, texture, color, and spatial relationships
Scene Understanding and Analysis
Classify scenes, detect activities, and extract spatial relationships between objects to build structured representations of visual content
How It Works
Building production computer vision systems requires stitching together detection models, embedding pipelines, vector databases, and serving infrastructure. Mixpeek provides the complete stack as managed APIs: ingest images and video, extract visual features with configurable extractors, index embeddings for retrieval, and query across your entire visual corpus. Teams ship computer vision features in days instead of months, without managing GPU clusters or model deployment infrastructure.
Benefits
Ship computer vision features 10x faster than building from scratch
Process millions of images daily without managing GPU infrastructure
Achieve 95%+ accuracy with pre-trained models and fine-tuning support
Reduce computer vision infrastructure costs by 60-80%
Why Mixpeek
Unified API covering the full computer vision stack from ingestion through retrieval, eliminating the integration complexity of assembling separate detection, embedding, and search components
Frequently Asked Questions
What object detection models does Mixpeek support?
Mixpeek provides pre-trained detection models covering 1,000+ common object categories out of the box. For domain-specific needs, custom taxonomies can be configured through the feature extractor API. Detection works on both still images and video frames with configurable sampling rates for video content.
How does visual similarity search work at scale?
Images and video frames are processed through visual embedding models that encode appearance, texture, shape, and spatial information into dense vectors. These embeddings are indexed in Qdrant for sub-100ms approximate nearest neighbor search across millions of items. Queries can be images, video frames, or text descriptions.
What is the processing throughput for image and video analysis?
Batch image processing handles 10,000-50,000 images per hour depending on extraction depth. Video processing runs at 5-15x real-time for comprehensive analysis including object detection, scene classification, and embedding generation. Throughput scales linearly with compute allocation.
Can I bring my own detection or embedding models?
Yes. Mixpeek supports custom model deployment through the engine API. Bring ONNX, TorchScript, or TensorFlow SavedModel formats. Custom models run alongside built-in extractors in the same pipeline, and their outputs are indexed and searchable through the standard retrieval API.
How does Mixpeek handle real-time video analysis?
Real-time video processing is available via HLS and RTMP stream ingestion. Configurable frame sampling rates (1-30 fps) balance analysis depth with latency requirements. Detection and classification results are available via webhook or polling within seconds of frame capture.
What industries use Mixpeek for computer vision?
Common deployments include e-commerce (product recognition and visual search), media (content tagging and moderation), manufacturing (quality inspection and defect detection), security (object and person detection), and healthcare (medical image analysis). The platform is domain-agnostic with configurable extractors for each vertical.
How does pricing work for computer vision workloads?
Pricing is based on the number of images and video hours processed per month, plus storage for embeddings and metadata. Standard plans cover 100,000-1,000,000 images per month. Enterprise plans support higher volumes with dedicated compute, custom model training, and premium SLAs. There are no per-query charges for search and retrieval.
Do I need to manage GPUs or ML infrastructure?
No. Mixpeek handles all GPU provisioning, model serving, autoscaling, and infrastructure management. Cloud deployments run on managed compute with automatic scaling based on workload. Self-hosted options are available for organizations that require on-premises GPU control.
How accurate is object detection compared to building custom models?
Pre-trained models achieve 85-95% accuracy on common object categories, comparable to custom-trained models for general use cases. For domain-specific applications (medical imaging, industrial inspection), custom model fine-tuning typically improves accuracy by 5-15% over generic models. Mixpeek supports both approaches.
Can Mixpeek integrate with existing image processing pipelines?
Yes. The REST API accepts images and video from S3, GCS, Azure Blob Storage, CDN URLs, or direct upload. Webhook notifications and batch status APIs integrate with existing orchestration tools like Airflow, Prefect, or custom pipelines. Results export in standard JSON formats compatible with downstream analytics and ML workflows.