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+# YOLOv10
+
+## Model description
+
+YOLOv10, built on the Ultralytics Python package by researchers at Tsinghua University, introduces a new approach to real-time object detection, addressing both the post-processing and model architecture deficiencies found in previous YOLO versions. By eliminating non-maximum suppression (NMS) and optimizing various model components, YOLOv10 achieves state-of-the-art performance with significantly reduced computational overhead. Extensive experiments demonstrate its superior accuracy-latency trade-offs across multiple model scales.
+
+## Step 1: Installation
+
+```bash
+# CentOS
+yum install -y mesa-libGL
+# Ubuntu
+apt install -y libgl1-mesa-glx
+```
+
+## Step 2: Preparing datasets
+
+Go to visit [COCO official website](https://cocodataset.org/#download), then select the COCO dataset you want to download.
+
+Take coco2017 dataset as an example, specify `/path/to/coco2017` to your COCO path in later training process, the unzipped dataset path structure sholud look like:
+
+```bash
+coco2017
+├── annotations
+│   ├── instances_train2017.json
+│   ├── instances_val2017.json
+│   └── ...
+├── train2017
+│   ├── 000000000009.jpg
+│   ├── 000000000025.jpg
+│   └── ...
+├── val2017
+│   ├── 000000000139.jpg
+│   ├── 000000000285.jpg
+│   └── ...
+├── train2017.txt
+├── val2017.txt
+└── ...
+```
+
+```bash
+# make soft link to coco dataset
+mkdir -p datasets/
+ln -s /PATH/TO/COCO ./datasets/coco
+```
+
+## Step 3: Training
+
+```bash
+# get yolov10 code
+git clone https://github.com/THU-MIG/yolov10.git
+cd yolov10
+sed -i 's/^torch/# torch/g' requirements.txt
+pip install -r requirements.txt
+```
+
+### Multiple GPU training
+
+```bash
+yolo detect train data=coco.yaml model=yolov10n.yaml epochs=500 batch=256 imgsz=640 device=0,1,2,3,4,5,6,7
+```
+
+## Reference
+
+[YOLOv10](https://github.com/THU-MIG/yolov10)