Object Detection

The Qwen3.6 35B-A3B native vision-language model is built on a hybrid architecture that integrates linear attention mechanisms with a sparse mixture-of-experts framework, achieving higher inference efficiency. Compared with the 3.5-35B-A3B, this model demonstrates significantly improved agentic coding capabilities, mathematical and code reasoning abilities, spatial intelligence, as well as object localization and object detection performance.

Grounding DINO: zero-shot text-prompted object detection (SwinT-OGC). H100 build.

Ultralytics YOLO26 object detection (COCO), selectable size n/s/m/l/x.

Moondream 3 is a fast, efficient 9B mixture-of-experts vision language model (2B active parameters) that delivers frontier-level visual reasoning for tasks like object detection, pointing, OCR, and structured output.

Cut and Learn for unsupervised object detection and instance segmentation

High performance and lightweight object detection models

Zero-shot / open vocabulary object detection

Real-Time Open-Vocabulary Object Detection

Best Human detection and Object Detection Background removal.

Real-Time Open-Vocabulary Object Detection using the xl weights

YOLOv10: Real-Time End-to-End Object Detection

RF-DETR: SOTA Real-Time Object Detection Model

Ultralytics YOLO11n object detection (COCO) — fast CPU PyTorch inference.