Hosted object detection models

Object detection models locate and classify objects within images or documents. They solve problems such as extracting tables from scanned PDFs (e.g., microsoft/table-transformer-structure-recognition, TahaDouaji/detr-doc-table-detection), detecting table regions in layouts (microsoft/table-transformer-detection, microsoft/table-transformer-structure-recognition-v1.1-all), and identifying small objects in general scenes (hustvl/yolos-small). Other models target document layout parsing (PaddlePaddle/PP-DocLayoutV3_safetensors) or high-accuracy detection across diverse categories (PekingU/rtdetr_v2_r50vd, PekingU/rtdetr_r50vd_coco_o365).

In production, these models are often deployed as part of a document processing pipeline, a real-time video analysis system, or a batch annotation service. They are typically called via an API that accepts an image or a document page and returns bounding boxes with class labels and confidence scores. Integration involves preprocessing inputs, handling model inference, and post-processing outputs for downstream tasks such as OCR, data extraction, or automation.

Choosing among object detection models involves a trade-off between model size, inference speed, and detection quality. Larger backbones (e.g., r50vd-based RT-DETR models) tend to achieve higher accuracy but require more compute and latency. Smaller models such as yolos-small trade some accuracy for faster inference and lower memory footprint. Domain-specific models (like the table transformers) are purpose-built for particular use cases and generally outperform general-purpose models on their target task. The right choice depends on the acceptable throughput, hardware budget, and precision requirements of your application.

For most call volumes, using a hosted API eliminates the overhead of provisioning GPUs, managing inference frameworks, and scaling for variable demand — making it a simpler and more cost-effective option than self-hosting.