add:yolov10

README.md

@@ -45,6 +45,7 @@ Here are some examples that can be run:
 
 | Example | Type | Model/Data Size | Docker Image Size | Command |
 | ------------------------------------------------ | ------------------------ | --------------- | ---------- | --------------------------------------- |
+| 🆕 [yolov10](/reComputer/scripts/yolov10/README.md) | Computer Vision | 7.2M | 5.74 GB | `reComputer run yolov10` |
 | 🆕 llama3 | Text (LLM) | 4.9GB | 10.5GB | `reComputer run llama3` |
 | 🆕 [ollama](https://github.com/ollama/ollama) | Inference Server | * | 10.5GB | `reComputer run ollama` |
 | LLaVA | Text + Vision (VLM) | 13GB | 14.4GB | `reComputer run llava` |
@@ -53,7 +54,7 @@ Here are some examples that can be run:
 | nanoowl | Vision Transformers(ViT) | 613MB | 15.1GB | `reComputer run nanoowl` |
 | [nanodb](../reComputer/scripts/nanodb/readme.md) | Vector Database | 76GB | 7.0GB | `reComputer run nanodb` |
 | whisper | Audio | 1.5GB | 6.0GB | `reComputer run whisper` |
-| [🆕 yolov8-rail-inspection](/reComputer/scripts/yolov8-rail-inspection/readme.md) |Computer Vision | 6M | 13.8GB | `reComputer run yolov8-rail-inspection` |
+| [yolov8-rail-inspection](/reComputer/scripts/yolov8-rail-inspection/readme.md) |Computer Vision | 6M | 13.8GB | `reComputer run yolov8-rail-inspection` |
 
 > Note: You should have enough space to run example, like `LLaVA`, at least `27.4GB` totally
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "jetson-examples"
-version = "0.1.1"
+version = "0.1.2"
 authors = [{ name = "luozhixin", email = "[email protected]" }]
 description = "Running Gen AI models and applications on NVIDIA Jetson devices with one-line command"
 readme = "README.md"

reComputer/__init__.py

@@ -1 +1 @@
-__version__ = "0.1.1"
+__version__ = "0.1.2"

reComputer/scripts/yolov10/Dockerfile

@@ -0,0 +1,14 @@
+FROM dustynv/l4t-pytorch:r35.3.1
+WORKDIR /opt
+
+RUN pip3 install --no-cache-dir --verbose gradio==4.31.5
+
+RUN git clone https://github.com/THU-MIG/yolov10.git && \
+ cd yolov10 && \
+ sed -i '/opencv-python>=4.6.0/ s/^/# /' pyproject.toml && \ 
+ sed -i '/torch>=1.8.0/ s/^/# /' pyproject.toml && \ 
+ sed -i '/torchvision>=0.9.0/ s/^/# /' pyproject.toml && \ 
+ pip3 install -e . && \
+ mkdir weights 
+
+CMD cd /opt/yolov10 && ls weights && python3 app.py

reComputer/scripts/yolov10/README.md

@@ -0,0 +1,63 @@
+# AudioCraft Deployment on Jetson in One Line 
+
+## Hello
+
+💡 Here's an example of quickly deploying YOLOv10 on a Jetson device.
+
+🔥 Hightlights:
+- **Yolov10** is a state-of-the-art real-time object detection model. 🚀🔍
+- **Jetson-examples** is a toolkit designed to deploy containerized applications on NVIDIA Jetson devices. ✨
+- **Jetson** is powerful AI hardware platform for edge computing.💻
+
+🛠️ Follow the tutorial below to quickly experience the performance of YOLOv10 on edge computing devices.
+
+<div align="center">
+ <img alt="yolov10" width="1200px" src="./assets/webui.png">
+</div>
+
+## Getting Started
+
+- install **jetson-examples** by pip:
+ ```sh
+ pip3 install jetson-examples
+ ```
+- restart reComputer 
+ ```sh
+ sudo restart
+ ```
+- run audiocraft on jetson in one line:
+ ```sh
+ reComputer run yolov10
+ ```
+- Please visit http:https://127.0.0.1:7860
+
+## Change Model
+
+This example will automatically download the YOLOv10s model at startup. If you want to try different models, please use the following command to download the model and then select the appropriate model through the WebUI.
+
+
+> **Note:** You can also download the model via a browser and copy the model to `/home/$USER/reComputer/yolov10/weights`.
+
+| Model | Download Command |
+|:-----:|:----:|
+| [YOLOv10-N](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10n.pt) | `sudo wget -P /home/$USER/reComputer/yolov10/weights https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10n.pt` |
+| [YOLOv10-S](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt) | `sudo wget -P /home/$USER/reComputer/yolov10/weights https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt` |
+| [YOLOv10-M](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10m.pt) | `sudo wget -P /home/$USER/reComputer/yolov10/weights https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10m.pt` |
+| [YOLOv10-B](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10b.pt) | `sudo wget -P /home/$USER/reComputer/yolov10/weights https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10b.pt` |
+| [YOLOv10-L](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10l.pt) | `sudo wget -P /home/$USER/reComputer/yolov10/weights https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10l.pt` |
+| [YOLOv10-X](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10x.pt) | `sudo wget -P /home/$USER/reComputer/yolov10/weights https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10x.pt` | 
+
+
+## Build Docker Image
+Our provided container is built based on the `jetson-container`. This example provide a Dockerfile, allowing you to build a more suitable container according to your needs.
+
+```sh
+sudo docker build -t yolov10-jetson .
+```
+
+> **Note:** Additionally, you can train models, test models, and export models within the Docker container environment. For detailed information, please refer to here.
+
+## Reference
+- https://github.com/THU-MIG/yolov10
+- https://github.com/dusty-nv/jetson-containers
+

reComputer/scripts/yolov10/clean.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+
+sudo docker rmi youjiang9977/yolov10-jetson:5.1.1
+sudo rm -rf /home/$USER/reComputer/yolov10

reComputer/scripts/yolov10/init.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+
+
+# check L4T_VERSION
+# reference https://github.com/dusty-nv/jetson-containers/blob/master/jetson_containers/l4t_version.sh
+ALLOWED_L4T_VERSIONS=("35.3.1")
+
+ARCH=$(uname -i)
+if [ $ARCH = "aarch64" ]; then
+ L4T_VERSION_STRING=$(head -n 1 /etc/nv_tegra_release)
+
+ if [ -z "$L4T_VERSION_STRING" ]; then
+ L4T_VERSION_STRING=$(dpkg-query --showformat='${Version}' --show nvidia-l4t-core)
+ L4T_VERSION_ARRAY=(${L4T_VERSION_STRING//./ }) 
+ L4T_RELEASE=${L4T_VERSION_ARRAY[0]}
+ L4T_REVISION=${L4T_VERSION_ARRAY[1]}
+ else
+ L4T_RELEASE=$(echo $L4T_VERSION_STRING | cut -f 2 -d ' ' | grep -Po '(?<=R)[^;]+')
+ L4T_REVISION=$(echo $L4T_VERSION_STRING | cut -f 2 -d ',' | grep -Po '(?<=REVISION: )[^;]+')
+ fi
+
+ L4T_REVISION_MAJOR=${L4T_REVISION:0:1}
+ L4T_REVISION_MINOR=${L4T_REVISION:2:1}
+
+ L4T_VERSION="$L4T_RELEASE.$L4T_REVISION"
+ echo $L4T_VERSION
+elif [ $ARCH != "x86_64" ]; then
+ echo "unsupported architecture: $ARCH"
+ exit 1
+fi
+
+echo "L4T_VERSION: $L4T_VERSION"
+if [[ ! " ${ALLOWED_L4T_VERSIONS[@]} " =~ " ${L4T_VERSION} " ]]; then
+ echo "L4T_VERSION is not in the allowed versions list. Exiting."
+ exit 1
+fi
+
+# make dirs
+BASE_PATH=/home/$USER/reComputer
+sudo mkdir -p $BASE_PATH/yolov10/weights
+sudo mkdir -p $BASE_PATH/yolov10/run
+echo "create workspace at $BASE_PATH/yolov10"
+
+# download models
+echo "download yolov10 models"
+WEIGHTS_FILE=$BASE_PATH/yolov10/weights/yolov10s.pt
+if [ ! -f $WEIGHTS_FILE ]; then
+ sudo wget -P $BASE_PATH/yolov10/weights https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt
+else
+ echo "Weights file already exists: $WEIGHTS_FILE"
+fi
+

reComputer/scripts/yolov10/run.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+sudo docker run -it --rm --net=host --runtime nvidia \
+ -v /var/run/docker.sock:/var/run/docker.sock \
+ -v /home/$USER/reComputer/yolov10/weights:/opt/yolov10/weights \
+ -v /home/$USER/reComputer/yolov10/runs:/opt/yolov10/runs \
+ youjiang9977/yolov10-jetson:5.1.1