From f3838b2441b5e865866d8451d128e1649ef55209 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 20 Feb 2025 21:32:46 +0800 Subject: [PATCH] Fix YOLO12 cfg links (#19332) Signed-off-by: Glenn Jocher Co-authored-by: UltralyticsAssistant --- docs/en/models/yolo12.md | 35 ++++++++++++++++------------------- 1 file changed, 16 insertions(+), 19 deletions(-) diff --git a/docs/en/models/yolo12.md b/docs/en/models/yolo12.md index 11da8e53..feec8b21 100644 --- a/docs/en/models/yolo12.md +++ b/docs/en/models/yolo12.md @@ -10,6 +10,8 @@ keywords: YOLO12, attention-centric object detection, YOLO series, Ultralytics, YOLO12 introduces an attention-centric architecture that departs from the traditional CNN-based approaches used in previous YOLO models, yet retains the real-time inference speed essential for many applications. This model achieves state-of-the-art object detection accuracy through novel methodological innovations in attention mechanisms and overall network architecture, while maintaining real-time performance. +![YOLO12 comparison visualization](https://github.com/user-attachments/assets/8009d90f-b43c-4a96-bb89-47ef843e7144) + ## Key Features - **Area Attention Mechanism**: A new self-attention approach that processes large receptive fields efficiently. It divides [feature maps](https://www.ultralytics.com/glossary/feature-maps) into _l_ equal-sized regions (defaulting to 4), either horizontally or vertically, avoiding complex operations and maintaining a large effective receptive field. This significantly reduces computational cost compared to standard self-attention. @@ -31,32 +33,27 @@ YOLO12 introduces an attention-centric architecture that departs from the tradit YOLO12 supports a variety of computer vision tasks. The table below shows task support and the operational modes (Inference, Validation, Training, and Export) enabled for each: -| Model Type | Task | Inference | Validation | Training | Export | -| ----------------------------------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ | -| [YOLO12](https://github.com/ultralytics/ultralytics/blob/yolov12/ultralytics/cfg/models/12/yolo12.yaml) | [Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| [YOLO12-seg](https://github.com/ultralytics/ultralytics/blob/yolov12/ultralytics/cfg/models/12/yolo12-seg.yaml) | [Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | -| [YOLO12-pose](https://github.com/ultralytics/ultralytics/blob/yolov12/ultralytics/cfg/models/12/yolo12-pose.yaml) | [Pose](../tasks/pose.md) | ✅ | ✅ | ✅ | ✅ | -| [YOLO12-cls](https://github.com/ultralytics/ultralytics/blob/yolov12/ultralytics/cfg/models/12/yolo12-cls.yaml) | [Classification](../tasks/classify.md) | ✅ | ✅ | ✅ | ✅ | -| [YOLO12-obb](https://github.com/ultralytics/ultralytics/blob/yolov12/ultralytics/cfg/models/12/yolo12-obb.yaml) | [OBB](../tasks/obb.md) | ✅ | ✅ | ✅ | ✅ | +| Model Type | Task | Inference | Validation | Training | Export | +| -------------------------------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ | +| [YOLO12](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/12/yolo12.yaml) | [Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| [YOLO12-seg](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/12/yolo12-seg.yaml) | [Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | +| [YOLO12-pose](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/12/yolo12-pose.yaml) | [Pose](../tasks/pose.md) | ✅ | ✅ | ✅ | ✅ | +| [YOLO12-cls](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/12/yolo12-cls.yaml) | [Classification](../tasks/classify.md) | ✅ | ✅ | ✅ | ✅ | +| [YOLO12-obb](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/12/yolo12-obb.yaml) | [OBB](../tasks/obb.md) | ✅ | ✅ | ✅ | ✅ | ## Performance Metrics - - - - - YOLO12 demonstrates significant [accuracy](https://www.ultralytics.com/glossary/accuracy) improvements across all model scales, with some trade-offs in speed compared to the _fastest_ prior YOLO models. Below are quantitative results for [object detection](https://www.ultralytics.com/glossary/object-detection) on the COCO validation dataset: ### Detection Performance (COCO val2017) -| Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT
(ms) | params
(M) | FLOPs
(B) | Comparison
(mAP/Speed) | -| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | --------------------------------- | ------------------ | ----------------- | -------------------------------- | -| [YOLO12n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12n.pt) | 640 | 40.6 | - | 1.64 | 2.6 | 6.5 | +2.1% / -9% (vs. YOLOv10n) | -| [YOLO12s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12s.pt) | 640 | 48.0 | - | 2.61 | 9.3 | 21.4 | +0.1% / +42% (vs. RT-DETRv2-R18) | -| [YOLO12m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12m.pt) | 640 | 52.5 | - | 4.86 | 20.2 | 67.5 | +1.0% / +3% (vs. YOLO11m) | -| [YOLO12l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12l.pt) | 640 | 53.7 | - | 6.77 | 26.4 | 88.9 | +0.4% / -8% (vs. YOLO11l) | -| [YOLO12x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12x.pt) | 640 | 55.2 | - | 11.79 | 59.1 | 199.0 | +0.6% / -4% (vs. YOLO11x) | +| Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT
(ms) | params
(M) | FLOPs
(B) | Comparison
(mAP/Speed) | +| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | --------------------------------- | ------------------ | ----------------- | ------------------------------ | +| [YOLO12n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12n.pt) | 640 | 40.6 | - | 1.64 | 2.6 | 6.5 | +2.1%/-9% (vs. YOLOv10n) | +| [YOLO12s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12s.pt) | 640 | 48.0 | - | 2.61 | 9.3 | 21.4 | +0.1%/+42% (vs. RT-DETRv2) | +| [YOLO12m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12m.pt) | 640 | 52.5 | - | 4.86 | 20.2 | 67.5 | +1.0%/+3% (vs. YOLO11m) | +| [YOLO12l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12l.pt) | 640 | 53.7 | - | 6.77 | 26.4 | 88.9 | +0.4%/-8% (vs. YOLO11l) | +| [YOLO12x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12x.pt) | 640 | 55.2 | - | 11.79 | 59.1 | 199.0 | +0.6%/-4% (vs. YOLO11x) | - Inference speed measured on an NVIDIA T4 GPU with TensorRT FP16 [precision](https://www.ultralytics.com/glossary/precision). - Comparisons show the relative improvement in mAP and the percentage change in speed (positive indicates faster; negative indicates slower). Comparisons are made against published results for YOLOv10, YOLO11, and RT-DETR where available.