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62
docs/en/models/fast-sam.md
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@ -48,9 +48,9 @@ FastSAM is designed to address the limitations of the [Segment Anything Model (S
This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|------------|---------------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------|
| FastSAM-s | [FastSAM-s.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/FastSAM-s.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
| FastSAM-x | [FastSAM-x.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/FastSAM-x.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
| ---------- | ------------------------------------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ |
| FastSAM-s | [FastSAM-s.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/FastSAM-s.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
| FastSAM-x | [FastSAM-x.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/FastSAM-x.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
## Usage Examples
@ -139,7 +139,7 @@ To perform object tracking on an image, use the `track` method as shown below:
!!! Example
=== "Python"
```python
from ultralytics import FastSAM
@ -149,7 +149,7 @@ To perform object tracking on an image, use the `track` method as shown below:
# Track with a FastSAM model on a video
results = model.track(source="path/to/video.mp4", imgsz=640)
```
=== "CLI"
```bash
@ -164,28 +164,28 @@ FastSAM is also available directly from the [https://github.com/CASIA-IVA-Lab/Fa
1. Clone the FastSAM repository:
```shell
git clone https://github.com/CASIA-IVA-Lab/FastSAM.git
```
```shell
git clone https://github.com/CASIA-IVA-Lab/FastSAM.git
```
2. Create and activate a Conda environment with Python 3.9:
```shell
conda create -n FastSAM python=3.9
conda activate FastSAM
```
```shell
conda create -n FastSAM python=3.9
conda activate FastSAM
```
3. Navigate to the cloned repository and install the required packages:
```shell
cd FastSAM
pip install -r requirements.txt
```
```shell
cd FastSAM
pip install -r requirements.txt
```
4. Install the CLIP model:
```shell
pip install git+https://github.com/ultralytics/CLIP.git
```
```shell
pip install git+https://github.com/ultralytics/CLIP.git
```
### Example Usage
@ -195,26 +195,26 @@ FastSAM is also available directly from the [https://github.com/CASIA-IVA-Lab/Fa
- Segment everything in an image:
```shell
python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg
```
```shell
python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg
```
- Segment specific objects using text prompt:
```shell
python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --text_prompt "the yellow dog"
```
```shell
python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --text_prompt "the yellow dog"
```
- Segment objects within a bounding box (provide box coordinates in xywh format):
```shell
python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --box_prompt "[570,200,230,400]"
```
```shell
python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --box_prompt "[570,200,230,400]"
```
- Segment objects near specific points:
```shell
python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --point_prompt "[[520,360],[620,300]]" --point_label "[1,0]"
```
```shell
python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --point_prompt "[[520,360],[620,300]]" --point_label "[1,0]"
```
Additionally, you can try FastSAM through a [Colab demo](https://colab.research.google.com/drive/1oX14f6IneGGw612WgVlAiy91UHwFAvr9?usp=sharing) or on the [HuggingFace web demo](https://huggingface.co/spaces/An-619/FastSAM) for a visual experience.

2
docs/en/models/index.md
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@ -19,7 +19,7 @@ Here are some of the key models supported:
5. **[YOLOv7](yolov7.md)**: Updated YOLO models released in 2022 by the authors of YOLOv4.
6. **[YOLOv8](yolov8.md) NEW 🚀**: The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
7. **[YOLOv9](yolov9.md)**: An experimental model trained on the Ultralytics [YOLOv5](yolov5.md) codebase implementing Programmable Gradient Information (PGI).
8. **[YOLOv10](yolov10.md)**: By Tsinghua University, featuring NMS-free training and efficiency-accuracy driven architecture, delivering state-of-the-art performance and latency.
8. **[YOLOv10](yolov10.md)**: By Tsinghua University, featuring NMS-free training and efficiency-accuracy driven architecture, delivering state-of-the-art performance and latency.
9. **[Segment Anything Model (SAM)](sam.md)**: Meta's Segment Anything Model (SAM).
10. **[Mobile Segment Anything Model (MobileSAM)](mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University.
11. **[Fast Segment Anything Model (FastSAM)](fast-sam.md)**: FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.

10
docs/en/models/mobile-sam.md
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@ -21,8 +21,8 @@ MobileSAM is trained on a single GPU with a 100k dataset (1% of the original ima
This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|------------|-----------------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------|
| MobileSAM | [mobile_sam.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/mobile_sam.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
| ---------- | --------------------------------------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ |
| MobileSAM | [mobile_sam.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/mobile_sam.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
## Adapting from SAM to MobileSAM
@ -33,21 +33,21 @@ MobileSAM performs comparably to the original SAM and retains the same pipeline
The following table provides a comparison of ViT-based image encoders:
| Image Encoder | Original SAM | MobileSAM |
|---------------|--------------|-----------|
| ------------- | ------------ | --------- |
| Parameters | 611M | 5M |
| Speed | 452ms | 8ms |
Both the original SAM and MobileSAM utilize the same prompt-guided mask decoder:
| Mask Decoder | Original SAM | MobileSAM |
|--------------|--------------|-----------|
| ------------ | ------------ | --------- |
| Parameters | 3.876M | 3.876M |
| Speed | 4ms | 4ms |
Here is the comparison of the whole pipeline:
| Whole Pipeline (Enc+Dec) | Original SAM | MobileSAM |
|--------------------------|--------------|-----------|
| ------------------------ | ------------ | --------- |
| Parameters | 615M | 9.66M |
| Speed | 456ms | 12ms |

7
docs/en/models/rtdetr.md
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@ -7,6 +7,7 @@ keywords: RT-DETR, Baidu, Vision Transformer, real-time object detection, Paddle
# Baidu's RT-DETR: A Vision Transformer-Based Real-Time Object Detector
## Overview
Real-Time Detection Transformer (RT-DETR), developed by Baidu, is a cutting-edge end-to-end object detector that provides real-time performance while maintaining high accuracy. It is based on the idea of DETR (the NMS-free framework), meanwhile introducing conv-based backbone and an efficient hybrid encoder to gain real-time speed. RT-DETR efficiently processes multiscale features by decoupling intra-scale interaction and cross-scale fusion. The model is highly adaptable, supporting flexible adjustment of inference speed using different decoder layers without retraining. RT-DETR excels on accelerated backends like CUDA with TensorRT, outperforming many other real-time object detectors.
<p align="center">
@ -74,9 +75,9 @@ This example provides simple RT-DETR training and inference examples. For full d
This table presents the model types, the specific pre-trained weights, the tasks supported by each model, and the various modes ([Train](../modes/train.md) , [Val](../modes/val.md), [Predict](../modes/predict.md), [Export](../modes/export.md)) that are supported, indicated by ✅ emojis.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|---------------------|-------------------------------------------------------------------------------------------|----------------------------------------|-----------|------------|----------|--------|
| RT-DETR Large | [rtdetr-l.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/rtdetr-l.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| RT-DETR Extra-Large | [rtdetr-x.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/rtdetr-x.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| ------------------- | ----------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
| RT-DETR Large | [rtdetr-l.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/rtdetr-l.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| RT-DETR Extra-Large | [rtdetr-x.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/rtdetr-x.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
## Citations and Acknowledgements

10
docs/en/models/sam.md
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@ -30,9 +30,9 @@ For an in-depth look at the Segment Anything Model and the SA-1B dataset, please
This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|------------|-------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------|
| SAM base | [sam_b.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/sam_b.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
| SAM large | [sam_l.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/sam_l.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
| ---------- | ----------------------------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ |
| SAM base | [sam_b.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/sam_b.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
| SAM large | [sam_l.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/sam_l.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
## How to Use SAM: Versatility and Power in Image Segmentation
@ -135,7 +135,7 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
Here we compare Meta's smallest SAM model, SAM-b, with Ultralytics smallest segmentation model, [YOLOv8n-seg](../tasks/segment.md):
| Model | Size | Parameters | Speed (CPU) |
|------------------------------------------------|----------------------------|------------------------|----------------------------|
| ---------------------------------------------- | -------------------------- | ---------------------- | -------------------------- |
| Meta's SAM-b | 358 MB | 94.7 M | 51096 ms/im |
| [MobileSAM](mobile-sam.md) | 40.7 MB | 10.1 M | 46122 ms/im |
| [FastSAM-s](fast-sam.md) with YOLOv8 backbone | 23.7 MB | 11.8 M | 115 ms/im |
@ -192,7 +192,7 @@ To auto-annotate your dataset with the Ultralytics framework, use the `auto_anno
```
| Argument | Type | Description | Default |
|------------|---------------------|---------------------------------------------------------------------------------------------------------|--------------|
| ---------- | ------------------- | ------------------------------------------------------------------------------------------------------- | ------------ |
| data | str | Path to a folder containing images to be annotated. | |
| det_model | str, optional | Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'. | 'yolov8x.pt' |
| sam_model | str, optional | Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'. | 'sam_b.pt' |

10
docs/en/models/yolo-nas.md
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@ -23,7 +23,7 @@ Developed by Deci AI, YOLO-NAS is a groundbreaking object detection foundational
Experience the power of next-generation object detection with the pre-trained YOLO-NAS models provided by Ultralytics. These models are designed to deliver top-notch performance in terms of both speed and accuracy. Choose from a variety of options tailored to your specific needs:
| Model | mAP | Latency (ms) |
|------------------|-------|--------------|
| ---------------- | ----- | ------------ |
| YOLO-NAS S | 47.5 | 3.21 |
| YOLO-NAS M | 51.55 | 5.85 |
| YOLO-NAS L | 52.22 | 7.87 |
@ -90,10 +90,10 @@ We offer three variants of the YOLO-NAS models: Small (s), Medium (m), and Large
Below is a detailed overview of each model, including links to their pre-trained weights, the tasks they support, and their compatibility with different operating modes.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|------------|-----------------------------------------------------------------------------------------------|----------------------------------------|-----------|------------|----------|--------|
| YOLO-NAS-s | [yolo_nas_s.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolo_nas_s.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ |
| YOLO-NAS-m | [yolo_nas_m.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolo_nas_m.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ |
| YOLO-NAS-l | [yolo_nas_l.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolo_nas_l.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ |
| ---------- | --------------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLO-NAS-s | [yolo_nas_s.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolo_nas_s.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ |
| YOLO-NAS-m | [yolo_nas_m.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolo_nas_m.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ |
| YOLO-NAS-l | [yolo_nas_l.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolo_nas_l.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ |
## Citations and Acknowledgements

24
docs/en/models/yolo-world.md
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@ -48,20 +48,20 @@ This section details the models available with their specific pre-trained weight
All the YOLOv8-World weights have been directly migrated from the official [YOLO-World](https://github.com/AILab-CVC/YOLO-World) repository, highlighting their excellent contributions.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|-----------------|---------------------------------------------------------------------------------------------------------|----------------------------------------|-----------|------------|----------|--------|
| YOLOv8s-world | [yolov8s-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8s-worldv2 | [yolov8s-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8m-world | [yolov8m-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8m-worldv2 | [yolov8m-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8l-world | [yolov8l-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8l-worldv2 | [yolov8l-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8x-world | [yolov8x-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8x-worldv2 | [yolov8x-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| --------------- | ------------------------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv8s-world | [yolov8s-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8s-worldv2 | [yolov8s-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8m-world | [yolov8m-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8m-worldv2 | [yolov8m-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8l-world | [yolov8l-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8l-worldv2 | [yolov8l-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8x-world | [yolov8x-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8x-worldv2 | [yolov8x-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
## Zero-shot Transfer on COCO Dataset
| Model Type | mAP | mAP50 | mAP75 |
|-----------------|------|-------|-------|
| --------------- | ---- | ----- | ----- |
| yolov8s-world | 37.4 | 52.0 | 40.6 |
| yolov8s-worldv2 | 37.7 | 52.2 | 41.0 |
| yolov8m-world | 42.0 | 57.0 | 45.6 |
@ -272,7 +272,7 @@ This approach provides a powerful means of customizing state-of-the-art object d
- Train data
| Dataset | Type | Samples | Boxes | Annotation Files |
|-------------------------------------------------------------------|-----------|---------|-------|--------------------------------------------------------------------------------------------------------------------------------------------|
| ----------------------------------------------------------------- | --------- | ------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| [Objects365v1](https://opendatalab.com/OpenDataLab/Objects365_v1) | Detection | 609k | 9621k | [objects365_train.json](https://opendatalab.com/OpenDataLab/Objects365_v1) |
| [GQA](https://nlp.stanford.edu/data/gqa/images.zip) | Grounding | 621k | 3681k | [final_mixed_train_no_coco.json](https://huggingface.co/GLIPModel/GLIP/blob/main/mdetr_annotations/final_mixed_train_no_coco.json) |
| [Flickr30k](https://shannon.cs.illinois.edu/DenotationGraph/) | Grounding | 149k | 641k | [final_flickr_separateGT_train.json](https://huggingface.co/GLIPModel/GLIP/blob/main/mdetr_annotations/final_flickr_separateGT_train.json) |
@ -280,7 +280,7 @@ This approach provides a powerful means of customizing state-of-the-art object d
- Val data
| Dataset | Type | Annotation Files |
|---------------------------------------------------------------------------------------------------------|-----------|--------------------------------------------------------------------------------------------------------|
| ------------------------------------------------------------------------------------------------------- | --------- | ------------------------------------------------------------------------------------------------------ |
| [LVIS minival](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/lvis.yaml) | Detection | [minival.txt](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/lvis.yaml) |
### Launch training from scratch

8
docs/en/models/yolov10.md
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@ -45,11 +45,11 @@ 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-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-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 |
@ -91,7 +91,7 @@ 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 |

8
docs/en/models/yolov3.md
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@ -33,10 +33,10 @@ The YOLOv3 series, including YOLOv3, YOLOv3-Ultralytics, and YOLOv3u, are design
All three models support a comprehensive set of modes, ensuring versatility in various stages of model deployment and development. These modes include [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), providing users with a complete toolkit for effective object detection.
| Model Type | Tasks Supported | Inference | Validation | Training | Export |
|--------------------|----------------------------------------|-----------|------------|----------|--------|
| YOLOv3 | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv3-Ultralytics | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv3u | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| ------------------ | -------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv3 | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv3-Ultralytics | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv3u | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
This table provides an at-a-glance view of the capabilities of each YOLOv3 variant, highlighting their versatility and suitability for various tasks and operational modes in object detection workflows.

4
docs/en/models/yolov5.md
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@ -25,8 +25,8 @@ YOLOv5u represents an advancement in object detection methodologies. Originating
The YOLOv5u models, with various pre-trained weights, excel in [Object Detection](../tasks/detect.md) tasks. They support a comprehensive range of modes, making them suitable for diverse applications, from development to deployment.
| Model Type | Pre-trained Weights | Task | Inference | Validation | Training | Export |
|------------|-----------------------------------------------------------------------------------------------------------------------------|----------------------------------------|-----------|------------|----------|--------|
| YOLOv5u | `yolov5nu`, `yolov5su`, `yolov5mu`, `yolov5lu`, `yolov5xu`, `yolov5n6u`, `yolov5s6u`, `yolov5m6u`, `yolov5l6u`, `yolov5x6u` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| ---------- | --------------------------------------------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv5u | `yolov5nu`, `yolov5su`, `yolov5mu`, `yolov5lu`, `yolov5xu`, `yolov5n6u`, `yolov5s6u`, `yolov5m6u`, `yolov5l6u`, `yolov5x6u` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
This table provides a detailed overview of the YOLOv5u model variants, highlighting their applicability in object detection tasks and support for various operational modes such as [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This comprehensive support ensures that users can fully leverage the capabilities of YOLOv5u models in a wide range of object detection scenarios.

12
docs/en/models/yolov6.md
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@ -75,12 +75,12 @@ This example provides simple YOLOv6 training and inference examples. For full do
The YOLOv6 series offers a range of models, each optimized for high-performance [Object Detection](../tasks/detect.md). These models cater to varying computational needs and accuracy requirements, making them versatile for a wide array of applications.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|------------|---------------------|----------------------------------------|-----------|------------|----------|--------|
| YOLOv6-N | `yolov6-n.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-S | `yolov6-s.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-M | `yolov6-m.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-L | `yolov6-l.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-L6 | `yolov6-l6.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| ---------- | ------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv6-N | `yolov6-n.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-S | `yolov6-s.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-M | `yolov6-m.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-L | `yolov6-l.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-L6 | `yolov6-l6.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
This table provides a detailed overview of the YOLOv6 model variants, highlighting their capabilities in object detection tasks and their compatibility with various operational modes such as [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This comprehensive support ensures that users can fully leverage the capabilities of YOLOv6 models in a broad range of object detection scenarios.

12
docs/en/models/yolov8.md
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@ -37,12 +37,12 @@ The YOLOv8 series offers a diverse range of models, each specialized for specifi
Each variant of the YOLOv8 series is optimized for its respective task, ensuring high performance and accuracy. Additionally, these models are compatible with various operational modes including [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), facilitating their use in different stages of deployment and development.
| Model | Filenames | Task | Inference | Validation | Training | Export |
|-------------|----------------------------------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------|
| YOLOv8 | `yolov8n.pt` `yolov8s.pt` `yolov8m.pt` `yolov8l.pt` `yolov8x.pt` | [Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-seg | `yolov8n-seg.pt` `yolov8s-seg.pt` `yolov8m-seg.pt` `yolov8l-seg.pt` `yolov8x-seg.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-pose | `yolov8n-pose.pt` `yolov8s-pose.pt` `yolov8m-pose.pt` `yolov8l-pose.pt` `yolov8x-pose.pt` `yolov8x-pose-p6.pt` | [Pose/Keypoints](../tasks/pose.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-obb | `yolov8n-obb.pt` `yolov8s-obb.pt` `yolov8m-obb.pt` `yolov8l-obb.pt` `yolov8x-obb.pt` | [Oriented Detection](../tasks/obb.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-cls | `yolov8n-cls.pt` `yolov8s-cls.pt` `yolov8m-cls.pt` `yolov8l-cls.pt` `yolov8x-cls.pt` | [Classification](../tasks/classify.md) | ✅ | ✅ | ✅ | ✅ |
| ----------- | -------------------------------------------------------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv8 | `yolov8n.pt` `yolov8s.pt` `yolov8m.pt` `yolov8l.pt` `yolov8x.pt` | [Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-seg | `yolov8n-seg.pt` `yolov8s-seg.pt` `yolov8m-seg.pt` `yolov8l-seg.pt` `yolov8x-seg.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-pose | `yolov8n-pose.pt` `yolov8s-pose.pt` `yolov8m-pose.pt` `yolov8l-pose.pt` `yolov8x-pose.pt` `yolov8x-pose-p6.pt` | [Pose/Keypoints](../tasks/pose.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-obb | `yolov8n-obb.pt` `yolov8s-obb.pt` `yolov8m-obb.pt` `yolov8l-obb.pt` `yolov8x-obb.pt` | [Oriented Detection](../tasks/obb.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-cls | `yolov8n-cls.pt` `yolov8s-cls.pt` `yolov8m-cls.pt` `yolov8l-cls.pt` `yolov8x-cls.pt` | [Classification](../tasks/classify.md) | ✅ | ✅ | ✅ | ✅ |
This table provides an overview of the YOLOv8 model variants, highlighting their applicability in specific tasks and their compatibility with various operational modes such as Inference, Validation, Training, and Export. It showcases the versatility and robustness of the YOLOv8 series, making them suitable for a variety of applications in computer vision.

6
docs/en/models/yolov9.md
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@ -153,9 +153,9 @@ This example provides simple YOLOv9 training and inference examples. For full do
The YOLOv9 series offers a range of models, each optimized for high-performance [Object Detection](../tasks/detect.md). These models cater to varying computational needs and accuracy requirements, making them versatile for a wide array of applications.
| Model | Filenames | Tasks | Inference | Validation | Training | Export |
|------------|-----------------------------------|----------------------------------------------|-----------|------------|----------|--------|
| YOLOv9 | `yolov9c.pt` `yolov9e.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv9-seg | `yolov9c-seg.pt` `yolov9e-seg.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ |
| ---------- | --------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv9 | `yolov9c.pt` `yolov9e.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv9-seg | `yolov9c-seg.pt` `yolov9e-seg.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ |
This table provides a detailed overview of the YOLOv9 model variants, highlighting their capabilities in object detection tasks and their compatibility with various operational modes such as [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This comprehensive support ensures that users can fully leverage the capabilities of YOLOv9 models in a broad range of object detection scenarios.