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Add Docs models pages FAQs (#14167)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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@ -44,14 +44,14 @@ YOLOv10 comes in various model scales to cater to different application needs:
YOLOv10 outperforms previous YOLO versions and other state-of-the-art models in terms of accuracy and efficiency. For example, YOLOv10-S is 1.8x faster than RT-DETR-R18 with similar AP on the COCO dataset, and YOLOv10-B has 46% less latency and 25% fewer parameters than YOLOv9-C with the same performance.
| Model | Input Size | AP<sup>val</sup> | FLOPs (G) | Latency (ms) |
| --------- | ---------- | ---------------- | --------- | ------------ |
| YOLOv10-N | 640 | 38.5 | **6.7** | **1.84** |
| YOLOv10-S | 640 | 46.3 | 21.6 | 2.49 |
| YOLOv10-M | 640 | 51.1 | 59.1 | 4.74 |
| YOLOv10-B | 640 | 52.5 | 92.0 | 5.74 |
| YOLOv10-L | 640 | 53.2 | 120.3 | 7.28 |
| YOLOv10-X | 640 | **54.4** | 160.4 | 10.70 |
| Model | Input Size | AP<sup>val</sup> | FLOPs (G) | Latency (ms) |
| -------------- | ---------- | ---------------- | --------- | ------------ |
| [YOLOv10-N][1] | 640 | 38.5 | **6.7** | **1.84** |
| [YOLOv10-S][2] | 640 | 46.3 | 21.6 | 2.49 |
| [YOLOv10-M][3] | 640 | 51.1 | 59.1 | 4.74 |
| [YOLOv10-B][4] | 640 | 52.5 | 92.0 | 5.74 |
| [YOLOv10-L][5] | 640 | 53.2 | 120.3 | 7.28 |
| [YOLOv10-X][6] | 640 | **54.4** | 160.4 | 10.70 |
Latency measured with TensorRT FP16 on T4 GPU.
@ -90,33 +90,40 @@ Compared to other state-of-the-art detectors:
Here is a detailed comparison of YOLOv10 variants with other state-of-the-art models:
| Model | Params (M) | FLOPs (G) | APval (%) | Latency (ms) | Latency (Forward) (ms) |
| ------------- | ---------- | --------- | --------- | ------------ | ---------------------- |
| YOLOv6-3.0-N | 4.7 | 11.4 | 37.0 | 2.69 | **1.76** |
| Gold-YOLO-N | 5.6 | 12.1 | **39.6** | 2.92 | 1.82 |
| YOLOv8-N | 3.2 | 8.7 | 37.3 | 6.16 | 1.77 |
| **YOLOv10-N** | **2.3** | **6.7** | 39.5 | **1.84** | 1.79 |
| | | | | | |
| YOLOv6-3.0-S | 18.5 | 45.3 | 44.3 | 3.42 | 2.35 |
| Gold-YOLO-S | 21.5 | 46.0 | 45.4 | 3.82 | 2.73 |
| YOLOv8-S | 11.2 | 28.6 | 44.9 | 7.07 | **2.33** |
| **YOLOv10-S** | **7.2** | **21.6** | **46.8** | **2.49** | 2.39 |
| | | | | | |
| RT-DETR-R18 | 20.0 | 60.0 | 46.5 | **4.58** | **4.49** |
| YOLOv6-3.0-M | 34.9 | 85.8 | 49.1 | 5.63 | 4.56 |
| Gold-YOLO-M | 41.3 | 87.5 | 49.8 | 6.38 | 5.45 |
| YOLOv8-M | 25.9 | 78.9 | 50.6 | 9.50 | 5.09 |
| **YOLOv10-M** | **15.4** | **59.1** | **51.3** | 4.74 | 4.63 |
| | | | | | |
| YOLOv6-3.0-L | 59.6 | 150.7 | 51.8 | 9.02 | 7.90 |
| Gold-YOLO-L | 75.1 | 151.7 | 51.8 | 10.65 | 9.78 |
| YOLOv8-L | 43.7 | 165.2 | 52.9 | 12.39 | 8.06 |
| RT-DETR-R50 | 42.0 | 136.0 | 53.1 | 9.20 | 9.07 |
| **YOLOv10-L** | **24.4** | **120.3** | **53.4** | **7.28** | **7.21** |
| | | | | | |
| YOLOv8-X | 68.2 | 257.8 | 53.9 | 16.86 | 12.83 |
| RT-DETR-R101 | 76.0 | 259.0 | 54.3 | 13.71 | 13.58 |
| **YOLOv10-X** | **29.5** | **160.4** | **54.4** | **10.70** | **10.60** |
| Model | Params (M) | FLOPs (G) | APval (%) | Latency (ms) | Latency (Forward) (ms) |
| ------------------ | ---------- | --------- | --------- | ------------ | ---------------------- |
| YOLOv6-3.0-N | 4.7 | 11.4 | 37.0 | 2.69 | **1.76** |
| Gold-YOLO-N | 5.6 | 12.1 | **39.6** | 2.92 | 1.82 |
| YOLOv8-N | 3.2 | 8.7 | 37.3 | 6.16 | 1.77 |
| **[YOLOv10-N][1]** | **2.3** | **6.7** | 39.5 | **1.84** | 1.79 |
| | | | | | |
| YOLOv6-3.0-S | 18.5 | 45.3 | 44.3 | 3.42 | 2.35 |
| Gold-YOLO-S | 21.5 | 46.0 | 45.4 | 3.82 | 2.73 |
| YOLOv8-S | 11.2 | 28.6 | 44.9 | 7.07 | **2.33** |
| **[YOLOv10-S][2]** | **7.2** | **21.6** | **46.8** | **2.49** | 2.39 |
| | | | | | |
| RT-DETR-R18 | 20.0 | 60.0 | 46.5 | **4.58** | **4.49** |
| YOLOv6-3.0-M | 34.9 | 85.8 | 49.1 | 5.63 | 4.56 |
| Gold-YOLO-M | 41.3 | 87.5 | 49.8 | 6.38 | 5.45 |
| YOLOv8-M | 25.9 | 78.9 | 50.6 | 9.50 | 5.09 |
| **[YOLOv10-M][3]** | **15.4** | **59.1** | **51.3** | 4.74 | 4.63 |
| | | | | | |
| YOLOv6-3.0-L | 59.6 | 150.7 | 51.8 | 9.02 | 7.90 |
| Gold-YOLO-L | 75.1 | 151.7 | 51.8 | 10.65 | 9.78 |
| YOLOv8-L | 43.7 | 165.2 | 52.9 | 12.39 | 8.06 |
| RT-DETR-R50 | 42.0 | 136.0 | 53.1 | 9.20 | 9.07 |
| **[YOLOv10-L][5]** | **24.4** | **120.3** | **53.4** | **7.28** | **7.21** |
| | | | | | |
| YOLOv8-X | 68.2 | 257.8 | 53.9 | 16.86 | 12.83 |
| RT-DETR-R101 | 76.0 | 259.0 | 54.3 | 13.71 | 13.58 |
| **[YOLOv10-X][6]** | **29.5** | **160.4** | **54.4** | **10.70** | **10.60** |
[1]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10n.pt
[2]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10s.pt
[3]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10m.pt
[4]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10b.pt
[5]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10l.pt
[6]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10x.pt
## Usage Examples
@ -224,9 +231,65 @@ We would like to acknowledge the YOLOv10 authors from [Tsinghua University](http
For detailed implementation, architectural innovations, and experimental results, please refer to the YOLOv10 [research paper](https://arxiv.org/pdf/2405.14458) and [GitHub repository](https://github.com/THU-MIG/yolov10) by the Tsinghua University team.
[1]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10n.pt
[2]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10s.pt
[3]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10m.pt
[4]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10b.pt
[5]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10l.pt
[6]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10x.pt
## FAQ
### What is YOLOv10 and how does it differ from previous versions?
YOLOv10 is the latest version of the YOLO (You Only Look Once) series for real-time object detection, optimized for both accuracy and efficiency. Unlike previous versions, YOLOv10 eliminates the need for non-maximum suppression (NMS) by utilizing consistent dual assignments, significantly reducing inference latency. It also introduces a holistic model design approach and various architectural enhancements, making it superior in performance and computational efficiency.
Learn more about YOLOv10's architecture in the [Architecture section](#architecture).
### How can I use YOLOv10 for real-time object detection in Python?
To use YOLOv10 for real-time object detection in Python, you can follow this example script:
```python
from ultralytics import YOLO
# Load a pre-trained YOLOv10n model
model = YOLO("yolov10n.pt")
# Perform object detection on an image
results = model("image.jpg")
# Display the results
results[0].show()
```
This example demonstrates loading a pre-trained YOLOv10 model and performing object detection on an image. You can also run detection via CLI with:
```bash
# Load a COCO-pretrained YOLOv10n model and run inference on the 'bus.jpg' image
yolo detect predict model=yolov10n.pt source=path/to/bus.jpg
```
Explore more usage examples in the [Usage Examples](#usage-examples) section.
### What are the key features that make YOLOv10 stand out?
YOLOv10 offers several innovative features:
- **NMS-Free Training**: Utilizes consistent dual assignments to eliminate NMS, reducing inference latency.
- **Holistic Model Design**: Comprehensive optimization of model components for both efficiency and accuracy, including lightweight classification heads and large-kernel convolutions.
- **Enhanced Model Capabilities**: Incorporates partial self-attention modules and other advanced techniques to boost performance without significant computational cost.
Dive into more details on these features in the [Key Features](#key-features) section.
### Which model variants are available in YOLOv10 and how do they differ?
YOLOv10 offers several variants tailored for different application needs:
- **YOLOv10-N**: Nano version for extremely resource-constrained environments.
- **YOLOv10-S**: Small version balancing speed and accuracy.
- **YOLOv10-M**: Medium version for general-purpose use.
- **YOLOv10-B**: Balanced version with increased width for higher accuracy.
- **YOLOv10-L**: Large version for higher accuracy at the cost of increased computational resources.
- **YOLOv10-X**: Extra-large version for maximum accuracy and performance.
Each variant offers a trade-off between computational efficiency and detection accuracy, suitable for various real-time applications. See the complete [Model Variants](#model-variants) for more information.
### How does YOLOv10's performance compare to other object detection models?
YOLOv10 outperforms previous YOLO versions and other state-of-the-art models in both accuracy and efficiency metrics. For example, YOLOv10-S is 1.8x faster than RT-DETR-R18 with a similar Average Precision (AP) on the COCO dataset. Additionally, YOLOv10-B has 46% less latency and 25% fewer parameters than YOLOv9-C while maintaining equivalent performance.
For detailed benchmark results, check out the [Performance](#performance) section.