+
+
+[](https://arxiv.org/abs/2502.12524) [](https://huggingface.co/spaces/sunsmarterjieleaf/yolov12) YOLOv12
+YOLOv12: Attention-Centric Real-Time Object Detectors
+ +[Yunjie Tian](https://sunsmarterjie.github.io/)1, [Qixiang Ye](https://people.ucas.ac.cn/~qxye?language=en)2, [David Doermann](https://cse.buffalo.edu/~doermann/)1 + +1 University at Buffalo, SUNY, 2 University of Chinese Academy of Sciences. + + +
+ 
+ Comparison with popular methods in terms of latency-accuracy (left) and FLOPs-accuracy (right) trade-offs
+
[](https://www.kaggle.com/code/jxxn03x/yolov12-on-custom-data) [](https://blog.roboflow.com/use-yolov12-with-roboflow/#deploy-yolov12-models-with-roboflow) [](https://openbayes.com/console/public/tutorials/A4ac4xNrUCQ)
+
+## Updates
+
+- 2025/03/18: Some guys are interested in the heatmap. See this [issue](https://github.com/sunsmarterjie/yolov12/issues/74).
+
+- 2025/03/09: **YOLOv12-turbo** is released: a faster YOLOv12 version.
+
+- 2025/02/24: Blogs: [ultralytics](https://docs.ultralytics.com/models/yolo12/), [LearnOpenCV](https://learnopencv.com/yolov12/). Thanks to them!
+
+- 2025/02/22: [YOLOv12 TensorRT CPP Inference Repo + Google Colab Notebook](https://github.com/mohamedsamirx/YOLOv12-TensorRT-CPP).
+
+- 2025/02/22: [Android deploy](https://github.com/mpj1234/ncnn-yolov12-android/tree/main) / [TensorRT-YOLO](https://github.com/laugh12321/TensorRT-YOLO) accelerates yolo12. Thanks to them!
+
+- 2025/02/21: Try yolo12 for classification, oriented bounding boxes, pose estimation, and instance segmentation at [ultralytics](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/12). Please pay attention to this [issue](https://github.com/sunsmarterjie/yolov12/issues/29). Thanks to them!
+
+- 2025/02/20: [Any computer or edge device?](https://github.com/roboflow/inference) / [ONNX CPP Version](https://github.com/mohamedsamirx/YOLOv12-ONNX-CPP). Thanks to them!
+
+- 2025/02/20: Train a yolov12 model on a custom dataset: [Blog](https://blog.roboflow.com/train-yolov12-model/) and [Youtube](https://www.youtube.com/watch?v=fksJmIMIfXo). / [Step-by-step instruction](https://youtu.be/dO8k5rgXG0M). Thanks to them!
+
+- 2025/02/19: [arXiv version](https://arxiv.org/abs/2502.12524) is public. [Demo](https://huggingface.co/spaces/sunsmarterjieleaf/yolov12) is available (try [Demo2](https://huggingface.co/spaces/sunsmarterjieleaf/yolov12_demo2) [Demo3](https://huggingface.co/spaces/sunsmarterjieleaf/yolov12_demo3) if busy).
+
+
+
+
+
+
+## Main Results
+
+**Turbo (default version)**:
+| Model | size+ Abstract +
+Enhancing the network architecture of the YOLO framework has been crucial for a long time but has focused on CNN-based improvements despite the proven superiority of attention mechanisms in modeling capabilities. This is because attention-based models cannot match the speed of CNN-based models. This paper proposes an attention-centric YOLO framework, namely YOLOv12, that matches the speed of previous CNN-based ones while harnessing the performance benefits of attention mechanisms. + +YOLOv12 surpasses all popular real-time object detectors in accuracy with competitive speed. For example, YOLOv12-N achieves 40.6% mAP with an inference latency of 1.64 ms on a T4 GPU, outperforming advanced YOLOv10-N / YOLOv11-N by 2.1%/1.2% mAP with a comparable speed. This advantage extends to other model scales. YOLOv12 also surpasses end-to-end real-time detectors that improve DETR, such as RT-DETR / RT-DETRv2: YOLOv12-S beats RT-DETR-R18 / RT-DETRv2-R18 while running 42% faster, using only 36% of the computation and 45% of the parameters. +(pixels) | mAPval
50-95 | Speed
T4 TensorRT10
| params
(M) | FLOPs
(G) | +| :----------------------------------------------------------------------------------- | :-------------------: | :-------------------:| :------------------------------:| :-----------------:| :---------------:| +| [YOLO12n](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12n.pt) | 640 | 40.4 | 1.60 | 2.5 | 6.0 | +| [YOLO12s](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12s.pt) | 640 | 47.6 | 2.42 | 9.1 | 19.4 | +| [YOLO12m](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12m.pt) | 640 | 52.5 | 4.27 | 19.6 | 59.8 | +| [YOLO12l](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12l.pt) | 640 | 53.8 | 5.83 | 26.5 | 82.4 | +| [YOLO12x](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12x.pt) | 640 | 55.4 | 10.38 | 59.3 | 184.6 | + +[**v1.0**](https://github.com/sunsmarterjie/yolov12/tree/V1.0): +| Model | size
(pixels) | mAPval
50-95 | Speed
T4 TensorRT10
| params
(M) | FLOPs
(G) | +| :----------------------------------------------------------------------------------- | :-------------------: | :-------------------:| :------------------------------:| :-----------------:| :---------------:| +| [YOLO12n](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12n.pt) | 640 | 40.6 | 1.64 | 2.6 | 6.5 | +| [YOLO12s](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12s.pt) | 640 | 48.0 | 2.61 | 9.3 | 21.4 | +| [YOLO12m](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12m.pt) | 640 | 52.5 | 4.86 | 20.2 | 67.5 | +| [YOLO12l](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12l.pt) | 640 | 53.7 | 6.77 | 26.4 | 88.9 | +| [YOLO12x](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12x.pt) | 640 | 55.2 | 11.79 | 59.1 | 199.0 | + +## Installation +``` +wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.3/flash_attn-2.7.3+cu11torch2.2cxx11abiFALSE-cp311-cp311-linux_x86_64.whl +conda create -n yolov12 python=3.11 +conda activate yolov12 +pip install -r requirements.txt +pip install -e . +``` + +## Validation +[`yolov12n`](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12n.pt) +[`yolov12s`](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12s.pt) +[`yolov12m`](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12m.pt) +[`yolov12l`](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12l.pt) +[`yolov12x`](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12x.pt) + +```python +from ultralytics import YOLO + +model = YOLO('yolov12{n/s/m/l/x}.pt') +model.val(data='coco.yaml', save_json=True) +``` + +## Training +```python +from ultralytics import YOLO + +model = YOLO('yolov12n.yaml') + +# Train the model +results = model.train( + data='coco.yaml', + epochs=600, + batch=256, + imgsz=640, + scale=0.5, # S:0.9; M:0.9; L:0.9; X:0.9 + mosaic=1.0, + mixup=0.0, # S:0.05; M:0.15; L:0.15; X:0.2 + copy_paste=0.1, # S:0.15; M:0.4; L:0.5; X:0.6 + device="0,1,2,3", +) + +# Evaluate model performance on the validation set +metrics = model.val() + +# Perform object detection on an image +results = model("path/to/image.jpg") +results[0].show() + +``` + +## Prediction +```python +from ultralytics import YOLO + +model = YOLO('yolov12{n/s/m/l/x}.pt') +model.predict() +``` + +## Export +```python +from ultralytics import YOLO + +model = YOLO('yolov12{n/s/m/l/x}.pt') +model.export(format="engine", half=True) # or format="onnx" +``` + + +## Demo + +``` +python app.py +# Please visit http://127.0.0.1:7860 +``` + + +## Acknowledgement + +The code is based on [ultralytics](https://github.com/ultralytics/ultralytics). Thanks for their excellent work! + +## Citation + +```BibTeX +@article{tian2025yolov12, + title={YOLOv12: Attention-Centric Real-Time Object Detectors}, + author={Tian, Yunjie and Ye, Qixiang and Doermann, David}, + journal={arXiv preprint arXiv:2502.12524}, + year={2025} +} +``` + diff --git a/yolov12/app.py b/yolov12/app.py new file mode 100644 index 0000000000000000000000000000000000000000..6e9fe089596c375ed2afbe91d12b046f6128ac59 --- /dev/null +++ b/yolov12/app.py @@ -0,0 +1,165 @@ +# -------------------------------------------------------- +# Based on yolov10 +# https://github.com/THU-MIG/yolov10/app.py +# --------------------------------------------------------' + +import gradio as gr +import cv2 +import tempfile +from ultralytics import YOLO + + +def yolov12_inference(image, video, model_id, image_size, conf_threshold): + model = YOLO(model_id) + if image: + results = model.predict(source=image, imgsz=image_size, conf=conf_threshold) + annotated_image = results[0].plot() + return annotated_image[:, :, ::-1], None + else: + video_path = tempfile.mktemp(suffix=".webm") + with open(video_path, "wb") as f: + with open(video, "rb") as g: + f.write(g.read()) + + cap = cv2.VideoCapture(video_path) + fps = cap.get(cv2.CAP_PROP_FPS) + frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + output_video_path = tempfile.mktemp(suffix=".webm") + out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'vp80'), fps, (frame_width, frame_height)) + + while cap.isOpened(): + ret, frame = cap.read() + if not ret: + break + + results = model.predict(source=frame, imgsz=image_size, conf=conf_threshold) + annotated_frame = results[0].plot() + out.write(annotated_frame) + + cap.release() + out.release() + + return None, output_video_path + + +def yolov12_inference_for_examples(image, model_path, image_size, conf_threshold): + annotated_image, _ = yolov12_inference(image, None, model_path, image_size, conf_threshold) + return annotated_image + + +def app(): + with gr.Blocks(): + with gr.Row(): + with gr.Column(): + image = gr.Image(type="pil", label="Image", visible=True) + video = gr.Video(label="Video", visible=False) + input_type = gr.Radio( + choices=["Image", "Video"], + value="Image", + label="Input Type", + ) + model_id = gr.Dropdown( + label="Model", + choices=[ + "yolov12n.pt", + "yolov12s.pt", + "yolov12m.pt", + "yolov12l.pt", + "yolov12x.pt", + ], + value="yolov12m.pt", + ) + image_size = gr.Slider( + label="Image Size", + minimum=320, + maximum=1280, + step=32, + value=640, + ) + conf_threshold = gr.Slider( + label="Confidence Threshold", + minimum=0.0, + maximum=1.0, + step=0.05, + value=0.25, + ) + yolov12_infer = gr.Button(value="Detect Objects") + + with gr.Column(): + output_image = gr.Image(type="numpy", label="Annotated Image", visible=True) + output_video = gr.Video(label="Annotated Video", visible=False) + + def update_visibility(input_type): + image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False) + video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True) + output_image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False) + output_video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True) + + return image, video, output_image, output_video + + input_type.change( + fn=update_visibility, + inputs=[input_type], + outputs=[image, video, output_image, output_video], + ) + + def run_inference(image, video, model_id, image_size, conf_threshold, input_type): + if input_type == "Image": + return yolov12_inference(image, None, model_id, image_size, conf_threshold) + else: + return yolov12_inference(None, video, model_id, image_size, conf_threshold) + + + yolov12_infer.click( + fn=run_inference, + inputs=[image, video, model_id, image_size, conf_threshold, input_type], + outputs=[output_image, output_video], + ) + + gr.Examples( + examples=[ + [ + "ultralytics/assets/bus.jpg", + "yolov12s.pt", + 640, + 0.25, + ], + [ + "ultralytics/assets/zidane.jpg", + "yolov12x.pt", + 640, + 0.25, + ], + ], + fn=yolov12_inference_for_examples, + inputs=[ + image, + model_id, + image_size, + conf_threshold, + ], + outputs=[output_image], + cache_examples='lazy', + ) + +gradio_app = gr.Blocks() +with gradio_app: + gr.HTML( + """ +
+ YOLOv12: Attention-Centric Real-Time Object Detectors +
+ """) + gr.HTML( + """ ++ arXiv | github +
+ """) + with gr.Row(): + with gr.Column(): + app() +if __name__ == '__main__': + gradio_app.launch() diff --git a/yolov12/assets/tradeoff.svg b/yolov12/assets/tradeoff.svg new file mode 100644 index 0000000000000000000000000000000000000000..0525b58a059513148cc22e079711b5f4c9cc72ef --- /dev/null +++ b/yolov12/assets/tradeoff.svg @@ -0,0 +1,3328 @@ + + + + diff --git a/yolov12/assets/tradeoff_turbo.svg b/yolov12/assets/tradeoff_turbo.svg new file mode 100644 index 0000000000000000000000000000000000000000..e9aaaea2c084b0adb2d4a945158f52d88fb9d779 --- /dev/null +++ b/yolov12/assets/tradeoff_turbo.svg @@ -0,0 +1,3322 @@ + + + + diff --git a/yolov12/data.yaml b/yolov12/data.yaml new file mode 100644 index 0000000000000000000000000000000000000000..00094ac07fa789e1412cfe5ab517430e7db97ea1 --- /dev/null +++ b/yolov12/data.yaml @@ -0,0 +1,6 @@ +names: +- apple +- damaged_apple +nc: 2 +train: ../train/images +val: ../validation/images diff --git a/yolov12/detect.py b/yolov12/detect.py new file mode 100644 index 0000000000000000000000000000000000000000..4b77fc1dde8429abfe6f39f4f35793377de3569d --- /dev/null +++ b/yolov12/detect.py @@ -0,0 +1,37 @@ +import subprocess +import os + +def run_yolov12_inference(img_path): + project_dir = "static/uploaded" + result_dir = os.path.join(project_dir, "yolov12_output") + + command = [ + "python", "D:/fruit/yolov12/yolo12/yolov12/detect.py", + "--weights", "D:/fruit/yolov12/yolo12/yolov12/runs/detect/train7/weights/best.pt", + "--source", img_path, + "--conf", "0.25", + "--save-txt", + "--save-conf", + "--project", project_dir, + "--name", "yolov12_output", + "--exist-ok" + ] + + subprocess.run(command, check=True) + + # Try reading the prediction txt file + file_name = os.path.basename(img_path) + label_file = os.path.join(result_dir, "labels", file_name.replace(".jpg", ".txt")) + + is_damaged = False + try: + with open(label_file, "r") as f: + for line in f: + class_id = int(line.split()[0]) + if class_id == 0: # assume damaged_apple is class 0 + is_damaged = True + break + except FileNotFoundError: + pass + + return is_damaged diff --git a/yolov12/docker/Dockerfile b/yolov12/docker/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..2cfbfd352e9ce13bdcfc772f8a381af84a852b2a --- /dev/null +++ b/yolov12/docker/Dockerfile @@ -0,0 +1,92 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +# Builds ultralytics/ultralytics:latest image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics +# Image is CUDA-optimized for YOLO11 single/multi-GPU training and inference + +# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch or nvcr.io/nvidia/pytorch:23.03-py3 +FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime + +# Set environment variables +# Avoid DDP error "MKL_THREADING_LAYER=INTEL is incompatible with libgomp.so.1 library" https://github.com/pytorch/pytorch/issues/37377 +ENV PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + PIP_NO_CACHE_DIR=1 \ + PIP_BREAK_SYSTEM_PACKAGES=1 \ + MKL_THREADING_LAYER=GNU \ + OMP_NUM_THREADS=1 + +# Downloads to user config dir +ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \ + /root/.config/Ultralytics/ + +# Install linux packages +# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package +# libsm6 required by libqxcb to create QT-based windows for visualization; set 'QT_DEBUG_PLUGINS=1' to test in docker +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + gcc git zip unzip wget curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 libsm6 \ + && rm -rf /var/lib/apt/lists/* + +# Security updates +# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796 +RUN apt upgrade --no-install-recommends -y openssl tar + +# Create working directory +WORKDIR /ultralytics + +# Copy contents and configure git +COPY . . +RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config +ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt . + +# Install pip packages +RUN pip install uv +# Note -cu12 must be used with tensorrt +RUN uv pip install --system -e ".[export]" tensorrt-cu12 "albumentations>=1.4.6" comet pycocotools + +# Run exports to AutoInstall packages +# Edge TPU export fails the first time so is run twice here +RUN yolo export model=tmp/yolo11n.pt format=edgetpu imgsz=32 || yolo export model=tmp/yolo11n.pt format=edgetpu imgsz=32 +RUN yolo export model=tmp/yolo11n.pt format=ncnn imgsz=32 +# Requires <= Python 3.10, bug with paddlepaddle==2.5.0 https://github.com/PaddlePaddle/X2Paddle/issues/991 +RUN uv pip install --system "paddlepaddle>=2.6.0" x2paddle +# Fix error: `np.bool` was a deprecated alias for the builtin `bool` segmentation error in Tests +RUN uv pip install --system numpy==1.23.5 + +# Remove extra build files +RUN rm -rf tmp /root/.config/Ultralytics/persistent_cache.json + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/ultralytics:latest && sudo docker build -f docker/Dockerfile -t $t . && sudo docker push $t + +# Pull and Run with access to all GPUs +# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t + +# Pull and Run with access to GPUs 2 and 3 (inside container CUDA devices will appear as 0 and 1) +# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus '"device=2,3"' $t + +# Pull and Run with local directory access +# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/shared/datasets:/datasets $t + +# Kill all +# sudo docker kill $(sudo docker ps -q) + +# Kill all image-based +# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/ultralytics:latest) + +# DockerHub tag update +# t=ultralytics/ultralytics:latest tnew=ultralytics/ultralytics:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew + +# Clean up +# sudo docker system prune -a --volumes + +# Update Ubuntu drivers +# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/ + +# DDP test +# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3 + +# GCP VM from Image +# docker.io/ultralytics/ultralytics:latest diff --git a/yolov12/docker/Dockerfile-arm64 b/yolov12/docker/Dockerfile-arm64 new file mode 100644 index 0000000000000000000000000000000000000000..dce273203810a5d36cd7d35c22f9c3c532e8b59d --- /dev/null +++ b/yolov12/docker/Dockerfile-arm64 @@ -0,0 +1,58 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +# Builds ultralytics/ultralytics:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics +# Image is aarch64-compatible for Apple M1, M2, M3, Raspberry Pi and other ARM architectures + +# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu with "FROM arm64v8/ubuntu:22.04" (deprecated) +# Start FROM Debian image for arm64v8 https://hub.docker.com/r/arm64v8/debian (new) +FROM arm64v8/debian:bookworm-slim + +# Set environment variables +ENV PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + PIP_NO_CACHE_DIR=1 \ + PIP_BREAK_SYSTEM_PACKAGES=1 + +# Downloads to user config dir +ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \ + /root/.config/Ultralytics/ + +# Install linux packages +# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package +# pkg-config and libhdf5-dev (not included) are needed to build 'h5py==3.11.0' aarch64 wheel required by 'tensorflow' +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + python3-pip git zip unzip wget curl htop gcc libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 \ + && rm -rf /var/lib/apt/lists/* + +# Create working directory +WORKDIR /ultralytics + +# Copy contents and configure git +COPY . . +RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config +ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt . + +# Install pip packages +RUN pip install uv +RUN uv pip install --system -e ".[export]" --break-system-packages + +# Creates a symbolic link to make 'python' point to 'python3' +RUN ln -sf /usr/bin/python3 /usr/bin/python + +# Remove extra build files +RUN rm -rf /root/.config/Ultralytics/persistent_cache.json + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/ultralytics:latest-arm64 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-arm64 -t $t . && sudo docker push $t + +# Run +# t=ultralytics/ultralytics:latest-arm64 && sudo docker run -it --ipc=host $t + +# Pull and Run +# t=ultralytics/ultralytics:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host $t + +# Pull and Run with local volume mounted +# t=ultralytics/ultralytics:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/shared/datasets:/datasets $t diff --git a/yolov12/docker/Dockerfile-conda b/yolov12/docker/Dockerfile-conda new file mode 100644 index 0000000000000000000000000000000000000000..aa1dff53bf08aba2bf04f2385bcdd770308446ad --- /dev/null +++ b/yolov12/docker/Dockerfile-conda @@ -0,0 +1,50 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +# Builds ultralytics/ultralytics:latest-conda image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics +# Image is optimized for Ultralytics Anaconda (https://anaconda.org/conda-forge/ultralytics) installation and usage + +# Start FROM miniconda3 image https://hub.docker.com/r/continuumio/miniconda3 +FROM continuumio/miniconda3:latest + +# Set environment variables +ENV PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + PIP_NO_CACHE_DIR=1 \ + PIP_BREAK_SYSTEM_PACKAGES=1 + +# Downloads to user config dir +ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \ + /root/.config/Ultralytics/ + +# Install linux packages +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + libgl1 \ + && rm -rf /var/lib/apt/lists/* + +# Copy contents +ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt . + +# Install conda packages +# mkl required to fix 'OSError: libmkl_intel_lp64.so.2: cannot open shared object file: No such file or directory' +RUN conda config --set solver libmamba && \ + conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia && \ + conda install -c conda-forge ultralytics mkl + # conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=12.1 ultralytics mkl + +# Remove extra build files +RUN rm -rf /root/.config/Ultralytics/persistent_cache.json + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/ultralytics:latest-conda && sudo docker build -f docker/Dockerfile-cpu -t $t . && sudo docker push $t + +# Run +# t=ultralytics/ultralytics:latest-conda && sudo docker run -it --ipc=host $t + +# Pull and Run +# t=ultralytics/ultralytics:latest-conda && sudo docker pull $t && sudo docker run -it --ipc=host $t + +# Pull and Run with local volume mounted +# t=ultralytics/ultralytics:latest-conda && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/shared/datasets:/datasets $t diff --git a/yolov12/docker/Dockerfile-cpu b/yolov12/docker/Dockerfile-cpu new file mode 100644 index 0000000000000000000000000000000000000000..79d5d50b707f98d845204a91bbffb3e296c84bf0 --- /dev/null +++ b/yolov12/docker/Dockerfile-cpu @@ -0,0 +1,62 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics +# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLO11 deployments + +# Use official Python base image for reproducibility (3.11.10 for export and 3.12.6 for inference) +FROM python:3.11.10-slim-bookworm + +# Set environment variables +ENV PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + PIP_NO_CACHE_DIR=1 \ + PIP_BREAK_SYSTEM_PACKAGES=1 + +# Downloads to user config dir +ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \ + /root/.config/Ultralytics/ + +# Install linux packages +# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + python3-pip git zip unzip wget curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 \ + && rm -rf /var/lib/apt/lists/* + +# Create working directory +WORKDIR /ultralytics + +# Copy contents and configure git +COPY . . +RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config +ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt . + +# Install pip packages +RUN pip install uv +RUN uv pip install --system -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu --index-strategy unsafe-first-match + +# Run exports to AutoInstall packages +RUN yolo export model=tmp/yolo11n.pt format=edgetpu imgsz=32 +RUN yolo export model=tmp/yolo11n.pt format=ncnn imgsz=32 +# Requires Python<=3.10, bug with paddlepaddle==2.5.0 https://github.com/PaddlePaddle/X2Paddle/issues/991 +RUN uv pip install --system "paddlepaddle>=2.6.0" x2paddle + +# Remove extra build files +RUN rm -rf tmp /root/.config/Ultralytics/persistent_cache.json + +# Set default command to bash +CMD ["/bin/bash"] + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/ultralytics:latest-cpu && sudo docker build -f docker/Dockerfile-cpu -t $t . && sudo docker push $t + +# Run +# t=ultralytics/ultralytics:latest-cpu && sudo docker run -it --ipc=host --name NAME $t + +# Pull and Run +# t=ultralytics/ultralytics:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host --name NAME $t + +# Pull and Run with local volume mounted +# t=ultralytics/ultralytics:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/shared/datasets:/datasets $t diff --git a/yolov12/docker/Dockerfile-jetson-jetpack4 b/yolov12/docker/Dockerfile-jetson-jetpack4 new file mode 100644 index 0000000000000000000000000000000000000000..e11279dad90804528e09029e18bbad971ced2652 --- /dev/null +++ b/yolov12/docker/Dockerfile-jetson-jetpack4 @@ -0,0 +1,70 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +# Builds ultralytics/ultralytics:jetson-jetpack4 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics +# Supports JetPack4.x for YOLO11 on Jetson Nano, TX2, Xavier NX, AGX Xavier + +# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-cuda +FROM nvcr.io/nvidia/l4t-cuda:10.2.460-runtime + +# Set environment variables +ENV PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 + +# Downloads to user config dir +ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \ + /root/.config/Ultralytics/ + +# Add NVIDIA repositories for TensorRT dependencies +RUN wget -q -O - https://repo.download.nvidia.com/jetson/jetson-ota-public.asc | apt-key add - && \ + echo "deb https://repo.download.nvidia.com/jetson/common r32.7 main" > /etc/apt/sources.list.d/nvidia-l4t-apt-source.list && \ + echo "deb https://repo.download.nvidia.com/jetson/t194 r32.7 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list + +# Install dependencies +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + git python3.8 python3.8-dev python3-pip python3-libnvinfer libopenmpi-dev libopenblas-base libomp-dev gcc \ + && rm -rf /var/lib/apt/lists/* + +# Create symbolic links for python3.8 and pip3 +RUN ln -sf /usr/bin/python3.8 /usr/bin/python3 +RUN ln -s /usr/bin/pip3 /usr/bin/pip + +# Create working directory +WORKDIR /ultralytics + +# Copy contents and configure git +COPY . . +RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config +ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt . + +# Download onnxruntime-gpu 1.8.0 and tensorrt 8.2.0.6 +# Other versions can be seen in https://elinux.org/Jetson_Zoo and https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048 +ADD https://nvidia.box.com/shared/static/gjqofg7rkg97z3gc8jeyup6t8n9j8xjw.whl onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl +ADD https://forums.developer.nvidia.com/uploads/short-url/hASzFOm9YsJx6VVFrDW1g44CMmv.whl tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl + +# Install pip packages +RUN python3 -m pip install --upgrade pip +RUN python3 -m pip install uv +RUN uv pip install --system \ + onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl \ + tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl +RUN uv pip install --system -e ".[export]" + +# Remove extra build files +RUN rm -rf *.whl /root/.config/Ultralytics/persistent_cache.json + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack4 -t $t . && sudo docker push $t + +# Run +# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker run -it --ipc=host $t + +# Pull and Run +# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host $t + +# Pull and Run with NVIDIA runtime +# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t diff --git a/yolov12/docker/Dockerfile-jetson-jetpack5 b/yolov12/docker/Dockerfile-jetson-jetpack5 new file mode 100644 index 0000000000000000000000000000000000000000..bfedb6e0cf2bfa44b7d315f8b0814819e6892245 --- /dev/null +++ b/yolov12/docker/Dockerfile-jetson-jetpack5 @@ -0,0 +1,57 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +# Builds ultralytics/ultralytics:jetson-jetson-jetpack5 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics +# Supports JetPack5.1.2 for YOLO11 on Jetson Xavier NX, AGX Xavier, AGX Orin, Orin Nano and Orin NX + +# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-jetpack +FROM nvcr.io/nvidia/l4t-jetpack:r35.4.1 + +# Set environment variables +ENV PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + PIP_NO_CACHE_DIR=1 \ + PIP_BREAK_SYSTEM_PACKAGES=1 + +# Downloads to user config dir +ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \ + /root/.config/Ultralytics/ + +# Install dependencies +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + git python3-pip libopenmpi-dev libopenblas-base libomp-dev \ + && rm -rf /var/lib/apt/lists/* + +# Create working directory +WORKDIR /ultralytics + +# Copy contents and configure git +COPY . . +RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config +ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt . + +# Pip install onnxruntime-gpu, torch, torchvision and ultralytics +RUN python3 -m pip install --upgrade pip uv +RUN uv pip install --system \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/onnxruntime_gpu-1.18.0-cp38-cp38-linux_aarch64.whl \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.16.2+c6f3977-cp38-cp38-linux_aarch64.whl + +RUN uv pip install --system -e ".[export]" + +# Remove extra build files +RUN rm -rf *.whl /root/.config/Ultralytics/persistent_cache.json + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack5 -t $t . && sudo docker push $t + +# Run +# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker run -it --ipc=host $t + +# Pull and Run +# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker pull $t && sudo docker run -it --ipc=host $t + +# Pull and Run with NVIDIA runtime +# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t diff --git a/yolov12/docker/Dockerfile-jetson-jetpack6 b/yolov12/docker/Dockerfile-jetson-jetpack6 new file mode 100644 index 0000000000000000000000000000000000000000..fa6ec651b0a64ed23d88feade5b9df018dd0f8ab --- /dev/null +++ b/yolov12/docker/Dockerfile-jetson-jetpack6 @@ -0,0 +1,58 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +# Builds ultralytics/ultralytics:jetson-jetpack6 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics +# Supports JetPack6.1 for YOLO11 on Jetson AGX Orin, Orin NX and Orin Nano Series + +# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-jetpack +FROM nvcr.io/nvidia/l4t-jetpack:r36.4.0 + +# Set environment variables +ENV PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + PIP_NO_CACHE_DIR=1 \ + PIP_BREAK_SYSTEM_PACKAGES=1 + +# Downloads to user config dir +ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \ + /root/.config/Ultralytics/ + +# Install dependencies +ADD https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/arm64/cuda-keyring_1.1-1_all.deb . +RUN dpkg -i cuda-keyring_1.1-1_all.deb && \ + apt-get update && \ + apt-get install -y --no-install-recommends \ + git python3-pip libopenmpi-dev libopenblas-base libomp-dev libcusparselt0 libcusparselt-dev \ + && rm -rf /var/lib/apt/lists/* + +# Create working directory +WORKDIR /ultralytics + +# Copy contents and configure git +COPY . . +RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config +ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt . + +# Pip install onnxruntime-gpu, torch, torchvision and ultralytics +RUN python3 -m pip install --upgrade pip uv +RUN uv pip install --system \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/onnxruntime_gpu-1.20.0-cp310-cp310-linux_aarch64.whl \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-2.5.0a0+872d972e41.nv24.08-cp310-cp310-linux_aarch64.whl \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.20.0a0+afc54f7-cp310-cp310-linux_aarch64.whl +RUN uv pip install --system -e ".[export]" + +# Remove extra build files +RUN rm -rf *.whl /root/.config/Ultralytics/persistent_cache.json + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack6 -t $t . && sudo docker push $t + +# Run +# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker run -it --ipc=host $t + +# Pull and Run +# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker pull $t && sudo docker run -it --ipc=host $t + +# Pull and Run with NVIDIA runtime +# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t diff --git a/yolov12/docker/Dockerfile-jupyter b/yolov12/docker/Dockerfile-jupyter new file mode 100644 index 0000000000000000000000000000000000000000..c458ff8848056d44566c67bbdb3ebb9108bcb508 --- /dev/null +++ b/yolov12/docker/Dockerfile-jupyter @@ -0,0 +1,33 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +# Builds ultralytics/ultralytics:latest-jupyter image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics +# Image provides JupyterLab interface for interactive YOLO development and includes tutorial notebooks + +# Start from Python-based Ultralytics image for full Python environment +FROM ultralytics/ultralytics:latest-python + +# Install JupyterLab for interactive development +RUN uv pip install --system jupyterlab + +# Create persistent data directory structure +RUN mkdir /data + +# Configure YOLO directories +RUN mkdir /data/{datasets,weights,runs} && \ + yolo settings datasets_dir="/data/datasets" weights_dir="/data/weights" runs_dir="/data/runs" + +# Start JupyterLab with tutorial notebook +ENTRYPOINT ["/usr/local/bin/jupyter", "lab", "--allow-root", "--ip=*", "/ultralytics/examples/tutorial.ipynb"] + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/ultralytics:latest-jupyter && sudo docker build -f docker/Dockerfile-jupyter -t $t . && sudo docker push $t + +# Run +# t=ultralytics/ultralytics:latest-jupyter && sudo docker run -it --ipc=host -p 8888:8888 $t + +# Pull and Run +# t=ultralytics/ultralytics:latest-jupyter && sudo docker pull $t && sudo docker run -it --ipc=host -p 8888:8888 $t + +# Pull and Run with local volume mounted +# t=ultralytics/ultralytics:latest-jupyter && sudo docker pull $t && sudo docker run -it --ipc=host -p 8888:8888 -v "$(pwd)"/datasets:/data/datasets $t diff --git a/yolov12/docker/Dockerfile-python b/yolov12/docker/Dockerfile-python new file mode 100644 index 0000000000000000000000000000000000000000..796d18879243c1313850245fd97acc6f4b32e37b --- /dev/null +++ b/yolov12/docker/Dockerfile-python @@ -0,0 +1,59 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics +# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLO11 deployments + +# Use official Python base image for reproducibility (3.11.10 for export and 3.12.6 for inference) +FROM python:3.11.10-slim-bookworm + +# Set environment variables +ENV PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + PIP_NO_CACHE_DIR=1 \ + PIP_BREAK_SYSTEM_PACKAGES=1 + +# Downloads to user config dir +ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \ + https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \ + /root/.config/Ultralytics/ + +# Install linux packages +# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + python3-pip git zip unzip wget curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 \ + && rm -rf /var/lib/apt/lists/* + +# Create working directory +WORKDIR /ultralytics + +# Copy contents and configure git +COPY . . +RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config +ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt . + +# Install pip packages +RUN pip install uv +RUN uv pip install --system -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu --index-strategy unsafe-first-match + +# Run exports to AutoInstall packages +RUN yolo export model=tmp/yolo11n.pt format=edgetpu imgsz=32 +RUN yolo export model=tmp/yolo11n.pt format=ncnn imgsz=32 +# Requires Python<=3.10, bug with paddlepaddle==2.5.0 https://github.com/PaddlePaddle/X2Paddle/issues/991 +RUN uv pip install --system "paddlepaddle>=2.6.0" x2paddle + +# Remove extra build files +RUN rm -rf tmp /root/.config/Ultralytics/persistent_cache.json + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/ultralytics:latest-python && sudo docker build -f docker/Dockerfile-python -t $t . && sudo docker push $t + +# Run +# t=ultralytics/ultralytics:latest-python && sudo docker run -it --ipc=host $t + +# Pull and Run +# t=ultralytics/ultralytics:latest-python && sudo docker pull $t && sudo docker run -it --ipc=host $t + +# Pull and Run with local volume mounted +# t=ultralytics/ultralytics:latest-python && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/shared/datasets:/datasets $t diff --git a/yolov12/docker/Dockerfile-runner b/yolov12/docker/Dockerfile-runner new file mode 100644 index 0000000000000000000000000000000000000000..5de5ee06507bc249872acc612ade577a3afcf000 --- /dev/null +++ b/yolov12/docker/Dockerfile-runner @@ -0,0 +1,44 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +# Builds GitHub actions CI runner image for deployment to DockerHub https://hub.docker.com/r/ultralytics/ultralytics +# Image is CUDA-optimized for YOLO11 single/multi-GPU training and inference tests + +# Start FROM Ultralytics GPU image +FROM ultralytics/ultralytics:latest + +# Set environment variables +ENV PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + PIP_NO_CACHE_DIR=1 \ + PIP_BREAK_SYSTEM_PACKAGES=1 \ + RUNNER_ALLOW_RUNASROOT=1 \ + DEBIAN_FRONTEND=noninteractive + +# Set the working directory +WORKDIR /actions-runner + +# Download and unpack the latest runner from https://github.com/actions/runner +RUN FILENAME=actions-runner-linux-x64-2.320.0.tar.gz && \ + curl -o $FILENAME -L https://github.com/actions/runner/releases/download/v2.320.0/$FILENAME && \ + tar xzf $FILENAME && \ + rm $FILENAME + +# Install runner dependencies +RUN uv pip install --system pytest-cov +RUN ./bin/installdependencies.sh && \ + apt-get -y install libicu-dev + +# Inline ENTRYPOINT command to configure and start runner with default TOKEN and NAME +ENTRYPOINT sh -c './config.sh --url https://github.com/ultralytics/ultralytics \ + --token ${GITHUB_RUNNER_TOKEN:-TOKEN} \ + --name ${GITHUB_RUNNER_NAME:-NAME} \ + --labels gpu-latest \ + --replace && \ + ./run.sh' + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/ultralytics:latest-runner && sudo docker build -f docker/Dockerfile-runner -t $t . && sudo docker push $t + +# Pull and Run in detached mode with access to GPUs 0 and 1 +# t=ultralytics/ultralytics:latest-runner && sudo docker run -d -e GITHUB_RUNNER_TOKEN=TOKEN -e GITHUB_RUNNER_NAME=NAME --ipc=host --gpus '"device=0,1"' $t diff --git a/yolov12/examples/README.md b/yolov12/examples/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ee06d337b62c6432e8944b838c0fc05f74e62562 --- /dev/null +++ b/yolov12/examples/README.md @@ -0,0 +1,40 @@ +## Ultralytics Examples + +This directory features a collection of real-world applications and walkthroughs, provided as either Python files or notebooks. Explore the examples below to see how YOLO can be integrated into various applications. + +### Ultralytics YOLO Example Applications + +| Title | Format | Contributor | +| ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------ | ----------------------------------------------------------------------------------------- | +| [YOLO ONNX Detection Inference with C++](./YOLOv8-CPP-Inference) | C++/ONNX | [Justas Bartnykas](https://github.com/JustasBart) | +| [YOLO OpenCV ONNX Detection Python](./YOLOv8-OpenCV-ONNX-Python) | OpenCV/Python/ONNX | [Farid Inawan](https://github.com/frdteknikelektro) | +| [YOLO C# ONNX-Runtime](https://github.com/dme-compunet/YoloSharp) | .NET/ONNX-Runtime | [Compunet](https://github.com/dme-compunet) | +| [YOLO .Net ONNX Detection C#](https://www.nuget.org/packages/Yolov8.Net) | C# .Net | [Samuel Stainback](https://github.com/sstainba) | +| [YOLOv8 on NVIDIA Jetson(TensorRT and DeepStream)](https://wiki.seeedstudio.com/YOLOv8-DeepStream-TRT-Jetson/) | Python | [Lakshantha](https://github.com/lakshanthad) | +| [YOLOv8 ONNXRuntime Python](./YOLOv8-ONNXRuntime) | Python/ONNXRuntime | [Semih Demirel](https://github.com/semihhdemirel) | +| [RTDETR ONNXRuntime Python](./RTDETR-ONNXRuntime-Python) | Python/ONNXRuntime | [Semih Demirel](https://github.com/semihhdemirel) | +| [YOLOv8 ONNXRuntime CPP](./YOLOv8-ONNXRuntime-CPP) | C++/ONNXRuntime | [DennisJcy](https://github.com/DennisJcy), [Onuralp Sezer](https://github.com/onuralpszr) | +| [RTDETR ONNXRuntime C#](https://github.com/Kayzwer/yolo-cs/blob/master/RTDETR.cs) | C#/ONNX | [Kayzwer](https://github.com/Kayzwer) | +| [YOLOv8 SAHI Video Inference](https://github.com/RizwanMunawar/ultralytics/blob/main/examples/YOLOv8-SAHI-Inference-Video/yolov8_sahi.py) | Python | [Muhammad Rizwan Munawar](https://github.com/RizwanMunawar) | +| [YOLOv8 Region Counter](https://github.com/RizwanMunawar/ultralytics/blob/main/examples/YOLOv8-Region-Counter/yolov8_region_counter.py) | Python | [Muhammad Rizwan Munawar](https://github.com/RizwanMunawar) | +| [YOLOv8 Segmentation ONNXRuntime Python](./YOLOv8-Segmentation-ONNXRuntime-Python) | Python/ONNXRuntime | [jamjamjon](https://github.com/jamjamjon) | +| [YOLOv8 LibTorch CPP](./YOLOv8-LibTorch-CPP-Inference) | C++/LibTorch | [Myyura](https://github.com/Myyura) | +| [YOLOv8 OpenCV INT8 TFLite Python](./YOLOv8-TFLite-Python) | Python | [Wamiq Raza](https://github.com/wamiqraza) | +| [YOLOv8 All Tasks ONNXRuntime Rust](./YOLOv8-ONNXRuntime-Rust) | Rust/ONNXRuntime | [jamjamjon](https://github.com/jamjamjon) | +| [YOLOv8 OpenVINO CPP](./YOLOv8-OpenVINO-CPP-Inference) | C++/OpenVINO | [Erlangga Yudi Pradana](https://github.com/rlggyp) | +| [YOLOv5-YOLO11 ONNXRuntime Rust](./YOLO-Series-ONNXRuntime-Rust) | Rust/ONNXRuntime | [jamjamjon](https://github.com/jamjamjon) | + +### How to Contribute + +We greatly appreciate contributions from the community, including examples, applications, and guides. If you'd like to contribute, please follow these guidelines: + +1. **Create a pull request (PR)** with the title prefix `[Example]`, adding your new example folder to the `examples/` directory within the repository. +2. **Ensure your project adheres to the following standards:** + - Makes use of the `ultralytics` package. + - Includes a `README.md` with clear instructions for setting up and running the example. + - Avoids adding large files or dependencies unless they are absolutely necessary for the example. + - Contributors should be willing to provide support for their examples and address related issues. + +For more detailed information and guidance on contributing, please visit our [contribution documentation](https://docs.ultralytics.com/help/contributing/). + +If you encounter any questions or concerns regarding these guidelines, feel free to open a PR or an issue in the repository, and we will assist you in the contribution process. diff --git a/yolov12/examples/RTDETR-ONNXRuntime-Python/README.md b/yolov12/examples/RTDETR-ONNXRuntime-Python/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1861da8295dcb7e1eb34392006839fbae693db42 --- /dev/null +++ b/yolov12/examples/RTDETR-ONNXRuntime-Python/README.md @@ -0,0 +1,43 @@ +# RTDETR - ONNX Runtime + +This project implements RTDETR using ONNX Runtime. + +## Installation + +To run this project, you need to install the required dependencies. The following instructions will guide you through the installation process. + +### Installing Required Dependencies + +You can install the required dependencies by running the following command: + +```bash +pip install -r requirements.txt +``` + +### Installing `onnxruntime-gpu` + +If you have an NVIDIA GPU and want to leverage GPU acceleration, you can install the onnxruntime-gpu package using the following command: + +```bash +pip install onnxruntime-gpu +``` + +Note: Make sure you have the appropriate GPU drivers installed on your system. + +### Installing `onnxruntime` (CPU version) + +If you don't have an NVIDIA GPU or prefer to use the CPU version of onnxruntime, you can install the onnxruntime package using the following command: + +```bash +pip install onnxruntime +``` + +### Usage + +After successfully installing the required packages, you can run the RTDETR implementation using the following command: + +```bash +python main.py --model rtdetr-l.onnx --img image.jpg --conf-thres 0.5 --iou-thres 0.5 +``` + +Make sure to replace rtdetr-l.onnx with the path to your RTDETR ONNX model file, image.jpg with the path to your input image, and adjust the confidence threshold (conf-thres) and IoU threshold (iou-thres) values as needed. diff --git a/yolov12/examples/RTDETR-ONNXRuntime-Python/main.py b/yolov12/examples/RTDETR-ONNXRuntime-Python/main.py new file mode 100644 index 0000000000000000000000000000000000000000..d794a7d648b8ce107015142c11ca2a7b1935590b --- /dev/null +++ b/yolov12/examples/RTDETR-ONNXRuntime-Python/main.py @@ -0,0 +1,222 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +import argparse + +import cv2 +import numpy as np +import onnxruntime as ort +import torch + +from ultralytics.utils import ASSETS, yaml_load +from ultralytics.utils.checks import check_requirements, check_yaml + + +class RTDETR: + """RTDETR object detection model class for handling inference and visualization.""" + + def __init__(self, model_path, img_path, conf_thres=0.5, iou_thres=0.5): + """ + Initializes the RTDETR object with the specified parameters. + + Args: + model_path: Path to the ONNX model file. + img_path: Path to the input image. + conf_thres: Confidence threshold for object detection. + iou_thres: IoU threshold for non-maximum suppression + """ + self.model_path = model_path + self.img_path = img_path + self.conf_thres = conf_thres + self.iou_thres = iou_thres + + # Set up the ONNX runtime session with CUDA and CPU execution providers + self.session = ort.InferenceSession(model_path, providers=["CUDAExecutionProvider", "CPUExecutionProvider"]) + self.model_input = self.session.get_inputs() + self.input_width = self.model_input[0].shape[2] + self.input_height = self.model_input[0].shape[3] + + # Load class names from the COCO dataset YAML file + self.classes = yaml_load(check_yaml("coco8.yaml"))["names"] + + # Generate a color palette for drawing bounding boxes + self.color_palette = np.random.uniform(0, 255, size=(len(self.classes), 3)) + + def draw_detections(self, box, score, class_id): + """ + Draws bounding boxes and labels on the input image based on the detected objects. + + Args: + box: Detected bounding box. + score: Corresponding detection score. + class_id: Class ID for the detected object. + + Returns: + None + """ + # Extract the coordinates of the bounding box + x1, y1, x2, y2 = box + + # Retrieve the color for the class ID + color = self.color_palette[class_id] + + # Draw the bounding box on the image + cv2.rectangle(self.img, (int(x1), int(y1)), (int(x2), int(y2)), color, 2) + + # Create the label text with class name and score + label = f"{self.classes[class_id]}: {score:.2f}" + + # Calculate the dimensions of the label text + (label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1) + + # Calculate the position of the label text + label_x = x1 + label_y = y1 - 10 if y1 - 10 > label_height else y1 + 10 + + # Draw a filled rectangle as the background for the label text + cv2.rectangle( + self.img, + (int(label_x), int(label_y - label_height)), + (int(label_x + label_width), int(label_y + label_height)), + color, + cv2.FILLED, + ) + + # Draw the label text on the image + cv2.putText( + self.img, label, (int(label_x), int(label_y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA + ) + + def preprocess(self): + """ + Preprocesses the input image before performing inference. + + Returns: + image_data: Preprocessed image data ready for inference. + """ + # Read the input image using OpenCV + self.img = cv2.imread(self.img_path) + + # Get the height and width of the input image + self.img_height, self.img_width = self.img.shape[:2] + + # Convert the image color space from BGR to RGB + img = cv2.cvtColor(self.img, cv2.COLOR_BGR2RGB) + + # Resize the image to match the input shape + img = cv2.resize(img, (self.input_width, self.input_height)) + + # Normalize the image data by dividing it by 255.0 + image_data = np.array(img) / 255.0 + + # Transpose the image to have the channel dimension as the first dimension + image_data = np.transpose(image_data, (2, 0, 1)) # Channel first + + # Expand the dimensions of the image data to match the expected input shape + image_data = np.expand_dims(image_data, axis=0).astype(np.float32) + + # Return the preprocessed image data + return image_data + + def bbox_cxcywh_to_xyxy(self, boxes): + """ + Converts bounding boxes from (center x, center y, width, height) format to (x_min, y_min, x_max, y_max) format. + + Args: + boxes (numpy.ndarray): An array of shape (N, 4) where each row represents + a bounding box in (cx, cy, w, h) format. + + Returns: + numpy.ndarray: An array of shape (N, 4) where each row represents + a bounding box in (x_min, y_min, x_max, y_max) format. + """ + # Calculate half width and half height of the bounding boxes + half_width = boxes[:, 2] / 2 + half_height = boxes[:, 3] / 2 + + # Calculate the coordinates of the bounding boxes + x_min = boxes[:, 0] - half_width + y_min = boxes[:, 1] - half_height + x_max = boxes[:, 0] + half_width + y_max = boxes[:, 1] + half_height + + # Return the bounding boxes in (x_min, y_min, x_max, y_max) format + return np.column_stack((x_min, y_min, x_max, y_max)) + + def postprocess(self, model_output): + """ + Postprocesses the model output to extract detections and draw them on the input image. + + Args: + model_output: Output of the model inference. + + Returns: + np.array: Annotated image with detections. + """ + # Squeeze the model output to remove unnecessary dimensions + outputs = np.squeeze(model_output[0]) + + # Extract bounding boxes and scores from the model output + boxes = outputs[:, :4] + scores = outputs[:, 4:] + + # Get the class labels and scores for each detection + labels = np.argmax(scores, axis=1) + scores = np.max(scores, axis=1) + + # Apply confidence threshold to filter out low-confidence detections + mask = scores > self.conf_thres + boxes, scores, labels = boxes[mask], scores[mask], labels[mask] + + # Convert bounding boxes to (x_min, y_min, x_max, y_max) format + boxes = self.bbox_cxcywh_to_xyxy(boxes) + + # Scale bounding boxes to match the original image dimensions + boxes[:, 0::2] *= self.img_width + boxes[:, 1::2] *= self.img_height + + # Draw detections on the image + for box, score, label in zip(boxes, scores, labels): + self.draw_detections(box, score, label) + + # Return the annotated image + return self.img + + def main(self): + """ + Executes the detection on the input image using the ONNX model. + + Returns: + np.array: Output image with annotations. + """ + # Preprocess the image for model input + image_data = self.preprocess() + + # Run the model inference + model_output = self.session.run(None, {self.model_input[0].name: image_data}) + + # Process and return the model output + return self.postprocess(model_output) + + +if __name__ == "__main__": + # Set up argument parser for command-line arguments + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, default="rtdetr-l.onnx", help="Path to the ONNX model file.") + parser.add_argument("--img", type=str, default=str(ASSETS / "bus.jpg"), help="Path to the input image.") + parser.add_argument("--conf-thres", type=float, default=0.5, help="Confidence threshold for object detection.") + parser.add_argument("--iou-thres", type=float, default=0.5, help="IoU threshold for non-maximum suppression.") + args = parser.parse_args() + + # Check for dependencies and set up ONNX runtime + check_requirements("onnxruntime-gpu" if torch.cuda.is_available() else "onnxruntime") + + # Create the detector instance with specified parameters + detection = RTDETR(args.model, args.img, args.conf_thres, args.iou_thres) + + # Perform detection and get the output image + output_image = detection.main() + + # Display the annotated output image + cv2.namedWindow("Output", cv2.WINDOW_NORMAL) + cv2.imshow("Output", output_image) + cv2.waitKey(0) diff --git a/yolov12/examples/YOLO-Series-ONNXRuntime-Rust/Cargo.toml b/yolov12/examples/YOLO-Series-ONNXRuntime-Rust/Cargo.toml new file mode 100644 index 0000000000000000000000000000000000000000..048ece887dfb31850fb0d5079e922e21486ba8c1 --- /dev/null +++ b/yolov12/examples/YOLO-Series-ONNXRuntime-Rust/Cargo.toml @@ -0,0 +1,14 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +[package] +name = "YOLO-ONNXRuntime-Rust" +version = "0.1.0" +edition = "2021" +authors = ["Jamjamjon
+
+
+### 2. \[Optional\] Install CUDA, CuDNN, and TensorRT
+
+- The CUDA execution provider requires CUDA version `12.x`.
+- The TensorRT execution provider requires both CUDA `12.x` and TensorRT `10.x`.
+
+### 3. \[Optional\] Install ffmpeg
+
+To view video frames and save video inferences, install `rust-ffmpeg`. For instructions, see:
+[https://github.com/zmwangx/rust-ffmpeg/wiki/Notes-on-building#dependencies](https://github.com/zmwangx/rust-ffmpeg/wiki/Notes-on-building#dependencies)
+
+## Get Started
+
+```Shell
+# customized
+cargo run -r -- --task detect --ver v8 --nc 6 --model xxx.onnx # YOLOv8
+
+# Classify
+cargo run -r -- --task classify --ver v5 --scale s --width 224 --height 224 --nc 1000 # YOLOv5
+cargo run -r -- --task classify --ver v8 --scale n --width 224 --height 224 --nc 1000 # YOLOv8
+cargo run -r -- --task classify --ver v11 --scale n --width 224 --height 224 --nc 1000 # YOLO11
+
+# Detect
+cargo run -r -- --task detect --ver v5 --scale n # YOLOv5
+cargo run -r -- --task detect --ver v6 --scale n # YOLOv6
+cargo run -r -- --task detect --ver v7 --scale t # YOLOv7
+cargo run -r -- --task detect --ver v8 --scale n # YOLOv8
+cargo run -r -- --task detect --ver v9 --scale t # YOLOv9
+cargo run -r -- --task detect --ver v10 --scale n # YOLOv10
+cargo run -r -- --task detect --ver v11 --scale n # YOLO11
+cargo run -r -- --task detect --ver rtdetr --scale l # RTDETR
+
+# Pose
+cargo run -r -- --task pose --ver v8 --scale n # YOLOv8-Pose
+cargo run -r -- --task pose --ver v11 --scale n # YOLO11-Pose
+
+# Segment
+cargo run -r -- --task segment --ver v5 --scale n # YOLOv5-Segment
+cargo run -r -- --task segment --ver v8 --scale n # YOLOv8-Segment
+cargo run -r -- --task segment --ver v11 --scale n # YOLOv8-Segment
+cargo run -r -- --task segment --ver v8 --model yolo/FastSAM-s-dyn-f16.onnx # FastSAM
+
+# OBB
+cargo run -r -- --ver v8 --task obb --scale n --width 1024 --height 1024 --source images/dota.png # YOLOv8-Obb
+cargo run -r -- --ver v11 --task obb --scale n --width 1024 --height 1024 --source images/dota.png # YOLO11-Obb
+```
+
+**`cargo run -- --help` for more options**
+
+For more details, please refer to [usls-yolo](https://github.com/jamjamjon/usls/tree/main/examples/yolo).
diff --git a/yolov12/examples/YOLO-Series-ONNXRuntime-Rust/src/main.rs b/yolov12/examples/YOLO-Series-ONNXRuntime-Rust/src/main.rs
new file mode 100644
index 0000000000000000000000000000000000000000..3c71a253108414198462e84a5ae3854a9535486e
--- /dev/null
+++ b/yolov12/examples/YOLO-Series-ONNXRuntime-Rust/src/main.rs
@@ -0,0 +1,236 @@
+use anyhow::Result;
+use clap::Parser;
+
+use usls::{
+ models::YOLO, Annotator, DataLoader, Device, Options, Viewer, Vision, YOLOScale, YOLOTask,
+ YOLOVersion, COCO_SKELETONS_16,
+};
+
+#[derive(Parser, Clone)]
+#[command(author, version, about, long_about = None)]
+pub struct Args {
+ /// Path to the ONNX model
+ #[arg(long)]
+ pub model: OptionYou have two options to link the ONNXRuntime library:
+ +- **Option 1: Manual Linking** + + - For detailed setup, consult the [ONNX Runtime linking documentation](https://ort.pyke.io/setup/linking). + - **Linux or macOS**: + 1. Download the ONNX Runtime package from the [Releases page](https://github.com/microsoft/onnxruntime/releases). + 2. Set up the library path by exporting the `ORT_DYLIB_PATH` environment variable: + ```shell + export ORT_DYLIB_PATH=/path/to/onnxruntime/lib/libonnxruntime.so.1.19.0 + ``` + +- **Option 2: Automatic Download** + - Use the `--features auto` flag to handle downloading automatically: + ```shell + cargo run -r --example yolo --features auto + ``` + +
+
+This example demonstrates how to perform inference using YOLOv8 in C++ with ONNX Runtime and OpenCV's API.
+
+## Benefits ✨
+
+- Friendly for deployment in the industrial sector.
+- Faster than OpenCV's DNN inference on both CPU and GPU.
+- Supports FP32 and FP16 CUDA acceleration.
+
+## Note ☕
+
+1. Benefit for Ultralytics' latest release, a `Transpose` op is added to the YOLOv8 model, while make v8 and v5 has the same output shape. Therefore, you can run inference with YOLOv5/v7/v8 via this project.
+
+## Exporting YOLOv8 Models 📦
+
+To export YOLOv8 models, use the following Python script:
+
+```python
+from ultralytics import YOLO
+
+# Load a YOLOv8 model
+model = YOLO("yolov8n.pt")
+
+# Export the model
+model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)
+```
+
+Alternatively, you can use the following command for exporting the model in the terminal
+
+```bash
+yolo export model=yolov8n.pt opset=12 simplify=True dynamic=False format=onnx imgsz=640,640
+```
+
+## Exporting YOLOv8 FP16 Models 📦
+
+```python
+import onnx
+from onnxconverter_common import float16
+
+model = onnx.load(R"YOUR_ONNX_PATH")
+model_fp16 = float16.convert_float_to_float16(model)
+onnx.save(model_fp16, R"YOUR_FP16_ONNX_PATH")
+```
+
+## Download COCO.yaml file 📂
+
+In order to run example, you also need to download coco.yaml. You can download the file manually from [here](https://raw.githubusercontent.com/ultralytics/ultralytics/main/ultralytics/cfg/datasets/coco.yaml)
+
+## Dependencies ⚙️
+
+| Dependency | Version |
+| -------------------------------- | ------------- |
+| Onnxruntime(linux,windows,macos) | >=1.14.1 |
+| OpenCV | >=4.0.0 |
+| C++ Standard | >=17 |
+| Cmake | >=3.5 |
+| Cuda (Optional) | >=11.4 \<12.0 |
+| cuDNN (Cuda required) | =8 |
+
+Note: The dependency on C++17 is due to the usage of the C++17 filesystem feature.
+
+Note (2): Due to ONNX Runtime, we need to use CUDA 11 and cuDNN 8. Keep in mind that this requirement might change in the future.
+
+## Build 🛠️
+
+1. Clone the repository to your local machine.
+
+2. Navigate to the root directory of the repository.
+
+3. Create a build directory and navigate to it:
+
+ ```console
+ mkdir build && cd build
+ ```
+
+4. Run CMake to generate the build files:
+
+ ```console
+ cmake ..
+ ```
+
+ **Notice**:
+
+ If you encounter an error indicating that the `ONNXRUNTIME_ROOT` variable is not set correctly, you can resolve this by building the project using the appropriate command tailored to your system.
+
+ ```console
+ # compiled in a win32 system
+ cmake -D WIN32=TRUE ..
+ # compiled in a linux system
+ cmake -D LINUX=TRUE ..
+ # compiled in an apple system
+ cmake -D APPLE=TRUE ..
+ ```
+
+5. Build the project:
+
+ ```console
+ make
+ ```
+
+6. The built executable should now be located in the `build` directory.
+
+## Usage 🚀
+
+```c++
+//change your param as you like
+//Pay attention to your device and the onnx model type(fp32 or fp16)
+DL_INIT_PARAM params;
+params.rectConfidenceThreshold = 0.1;
+params.iouThreshold = 0.5;
+params.modelPath = "yolov8n.onnx";
+params.imgSize = { 640, 640 };
+params.cudaEnable = true;
+params.modelType = YOLO_DETECT_V8;
+yoloDetector->CreateSession(params);
+Detector(yoloDetector);
+```
diff --git a/yolov12/examples/YOLOv8-ONNXRuntime-CPP/inference.cpp b/yolov12/examples/YOLOv8-ONNXRuntime-CPP/inference.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..a65391f5d7d116befac6f1cb2151891cca5a1c5d
--- /dev/null
+++ b/yolov12/examples/YOLOv8-ONNXRuntime-CPP/inference.cpp
@@ -0,0 +1,375 @@
+#include "inference.h"
+#include