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description: Optimize YOLO11 models for mobile and embedded devices by exporting to MNN format. |
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keywords: Ultralytics, YOLO11, MNN, model export, machine learning, deployment, mobile, embedded systems, deep learning, AI models |
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--- |
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|
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# MNN Export for YOLO11 Models and Deploy |
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|
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## MNN |
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<p align="center"> |
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<img width="100%" src="https://mnn-docs.readthedocs.io/en/latest/_images/architecture.png" alt="MNN architecture"> |
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</p> |
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|
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[MNN](https://github.com/alibaba/MNN) is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models and has industry-leading performance for inference and training on-device. At present, MNN has been integrated into more than 30 apps of Alibaba Inc, such as Taobao, Tmall, Youku, DingTalk, Xianyu, etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control. In addition, MNN is also used on embedded devices, such as IoT. |
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## Export to MNN: Converting Your YOLO11 Model |
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You can expand model compatibility and deployment flexibility by converting YOLO11 models to MNN format. |
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### Installation |
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To install the required packages, run: |
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!!! tip "Installation" |
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|
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=== "CLI" |
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```bash |
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# Install the required package for YOLO11 and MNN |
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pip install ultralytics |
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pip install MNN |
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``` |
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### Usage |
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Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLO11 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md). |
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!!! example "Usage" |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load the YOLO11 model |
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model = YOLO("yolo11n.pt") |
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# Export the model to MNN format |
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model.export(format="mnn") # creates 'yolo11n.mnn' |
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# Load the exported MNN model |
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mnn_model = YOLO("yolo11n.mnn") |
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# Run inference |
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results = mnn_model("https://ultralytics.com/images/bus.jpg") |
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``` |
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=== "CLI" |
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```bash |
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# Export a YOLO11n PyTorch model to MNN format |
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yolo export model=yolo11n.pt format=mnn # creates 'yolo11n.mnn' |
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# Run inference with the exported model |
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yolo predict model='yolo11n.mnn' source='https://ultralytics.com/images/bus.jpg' |
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``` |
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For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md). |
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### MNN-Only Inference |
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A function that relies solely on MNN for YOLO11 inference and preprocessing is implemented, providing both Python and C++ versions for easy deployment in any scenario. |
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!!! example "MNN" |
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=== "Python" |
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```python |
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import argparse |
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import MNN |
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import MNN.cv as cv2 |
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import MNN.numpy as np |
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def inference(model, img, precision, backend, thread): |
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config = {} |
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config["precision"] = precision |
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config["backend"] = backend |
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config["numThread"] = thread |
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rt = MNN.nn.create_runtime_manager((config,)) |
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# net = MNN.nn.load_module_from_file(model, ['images'], ['output0'], runtime_manager=rt) |
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net = MNN.nn.load_module_from_file(model, [], [], runtime_manager=rt) |
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original_image = cv2.imread(img) |
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ih, iw, _ = original_image.shape |
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length = max((ih, iw)) |
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scale = length / 640 |
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image = np.pad(original_image, [[0, length - ih], [0, length - iw], [0, 0]], "constant") |
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image = cv2.resize( |
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image, (640, 640), 0.0, 0.0, cv2.INTER_LINEAR, -1, [0.0, 0.0, 0.0], [1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0] |
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) |
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input_var = np.expand_dims(image, 0) |
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input_var = MNN.expr.convert(input_var, MNN.expr.NC4HW4) |
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output_var = net.forward(input_var) |
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output_var = MNN.expr.convert(output_var, MNN.expr.NCHW) |
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output_var = output_var.squeeze() |
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# output_var shape: [84, 8400]; 84 means: [cx, cy, w, h, prob * 80] |
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cx = output_var[0] |
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cy = output_var[1] |
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w = output_var[2] |
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h = output_var[3] |
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probs = output_var[4:] |
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# [cx, cy, w, h] -> [y0, x0, y1, x1] |
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x0 = cx - w * 0.5 |
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y0 = cy - h * 0.5 |
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x1 = cx + w * 0.5 |
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y1 = cy + h * 0.5 |
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boxes = np.stack([x0, y0, x1, y1], axis=1) |
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# get max prob and idx |
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scores = np.max(probs, 0) |
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class_ids = np.argmax(probs, 0) |
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result_ids = MNN.expr.nms(boxes, scores, 100, 0.45, 0.25) |
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print(result_ids.shape) |
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# nms result box, score, ids |
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result_boxes = boxes[result_ids] |
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result_scores = scores[result_ids] |
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result_class_ids = class_ids[result_ids] |
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for i in range(len(result_boxes)): |
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x0, y0, x1, y1 = result_boxes[i].read_as_tuple() |
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y0 = int(y0 * scale) |
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y1 = int(y1 * scale) |
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x0 = int(x0 * scale) |
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x1 = int(x1 * scale) |
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print(result_class_ids[i]) |
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cv2.rectangle(original_image, (x0, y0), (x1, y1), (0, 0, 255), 2) |
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cv2.imwrite("res.jpg", original_image) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--model", type=str, required=True, help="the yolo11 model path") |
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parser.add_argument("--img", type=str, required=True, help="the input image path") |
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parser.add_argument("--precision", type=str, default="normal", help="inference precision: normal, low, high, lowBF") |
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parser.add_argument( |
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"--backend", |
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type=str, |
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default="CPU", |
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help="inference backend: CPU, OPENCL, OPENGL, NN, VULKAN, METAL, TRT, CUDA, HIAI", |
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) |
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parser.add_argument("--thread", type=int, default=4, help="inference using thread: int") |
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args = parser.parse_args() |
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inference(args.model, args.img, args.precision, args.backend, args.thread) |
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``` |
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=== "CPP" |
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```cpp |
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#include <stdio.h> |
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#include <MNN/ImageProcess.hpp> |
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#include <MNN/expr/Module.hpp> |
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#include <MNN/expr/Executor.hpp> |
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#include <MNN/expr/ExprCreator.hpp> |
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#include <MNN/expr/Executor.hpp> |
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#include <cv/cv.hpp> |
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using namespace MNN; |
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using namespace MNN::Express; |
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using namespace MNN::CV; |
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int main(int argc, const char* argv[]) { |
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if (argc < 3) { |
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MNN_PRINT("Usage: ./yolo11_demo.out model.mnn input.jpg [forwardType] [precision] [thread]\n"); |
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return 0; |
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} |
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int thread = 4; |
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int precision = 0; |
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int forwardType = MNN_FORWARD_CPU; |
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if (argc >= 4) { |
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forwardType = atoi(argv[3]); |
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} |
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if (argc >= 5) { |
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precision = atoi(argv[4]); |
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} |
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if (argc >= 6) { |
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thread = atoi(argv[5]); |
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} |
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MNN::ScheduleConfig sConfig; |
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sConfig.type = static_cast<MNNForwardType>(forwardType); |
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sConfig.numThread = thread; |
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BackendConfig bConfig; |
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bConfig.precision = static_cast<BackendConfig::PrecisionMode>(precision); |
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sConfig.backendConfig = &bConfig; |
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std::shared_ptr<Executor::RuntimeManager> rtmgr = std::shared_ptr<Executor::RuntimeManager>(Executor::RuntimeManager::createRuntimeManager(sConfig)); |
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if(rtmgr == nullptr) { |
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MNN_ERROR("Empty RuntimeManger\n"); |
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return 0; |
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} |
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rtmgr->setCache(".cachefile"); |
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std::shared_ptr<Module> net(Module::load(std::vector<std::string>{}, std::vector<std::string>{}, argv[1], rtmgr)); |
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auto original_image = imread(argv[2]); |
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auto dims = original_image->getInfo()->dim; |
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int ih = dims[0]; |
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int iw = dims[1]; |
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int len = ih > iw ? ih : iw; |
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float scale = len / 640.0; |
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std::vector<int> padvals { 0, len - ih, 0, len - iw, 0, 0 }; |
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auto pads = _Const(static_cast<void*>(padvals.data()), {3, 2}, NCHW, halide_type_of<int>()); |
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auto image = _Pad(original_image, pads, CONSTANT); |
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image = resize(image, Size(640, 640), 0, 0, INTER_LINEAR, -1, {0., 0., 0.}, {1./255., 1./255., 1./255.}); |
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auto input = _Unsqueeze(image, {0}); |
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input = _Convert(input, NC4HW4); |
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auto outputs = net->onForward({input}); |
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auto output = _Convert(outputs[0], NCHW); |
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output = _Squeeze(output); |
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// output shape: [84, 8400]; 84 means: [cx, cy, w, h, prob * 80] |
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auto cx = _Gather(output, _Scalar<int>(0)); |
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auto cy = _Gather(output, _Scalar<int>(1)); |
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auto w = _Gather(output, _Scalar<int>(2)); |
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auto h = _Gather(output, _Scalar<int>(3)); |
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std::vector<int> startvals { 4, 0 }; |
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auto start = _Const(static_cast<void*>(startvals.data()), {2}, NCHW, halide_type_of<int>()); |
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std::vector<int> sizevals { -1, -1 }; |
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auto size = _Const(static_cast<void*>(sizevals.data()), {2}, NCHW, halide_type_of<int>()); |
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auto probs = _Slice(output, start, size); |
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// [cx, cy, w, h] -> [y0, x0, y1, x1] |
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auto x0 = cx - w * _Const(0.5); |
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auto y0 = cy - h * _Const(0.5); |
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auto x1 = cx + w * _Const(0.5); |
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auto y1 = cy + h * _Const(0.5); |
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auto boxes = _Stack({x0, y0, x1, y1}, 1); |
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auto scores = _ReduceMax(probs, {0}); |
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auto ids = _ArgMax(probs, 0); |
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auto result_ids = _Nms(boxes, scores, 100, 0.45, 0.25); |
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auto result_ptr = result_ids->readMap<int>(); |
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auto box_ptr = boxes->readMap<float>(); |
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auto ids_ptr = ids->readMap<int>(); |
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auto score_ptr = scores->readMap<float>(); |
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for (int i = 0; i < 100; i++) { |
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auto idx = result_ptr[i]; |
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if (idx < 0) break; |
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auto x0 = box_ptr[idx * 4 + 0] * scale; |
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auto y0 = box_ptr[idx * 4 + 1] * scale; |
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auto x1 = box_ptr[idx * 4 + 2] * scale; |
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auto y1 = box_ptr[idx * 4 + 3] * scale; |
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auto class_idx = ids_ptr[idx]; |
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auto score = score_ptr[idx]; |
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rectangle(original_image, {x0, y0}, {x1, y1}, {0, 0, 255}, 2); |
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} |
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if (imwrite("res.jpg", original_image)) { |
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MNN_PRINT("result image write to `res.jpg`.\n"); |
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} |
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rtmgr->updateCache(); |
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return 0; |
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} |
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``` |
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## Summary |
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In this guide, we introduce how to export the Ultralytics YOLO11 model to MNN and use MNN for inference. |
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For more usage, please refer to the [MNN documentation](https://mnn-docs.readthedocs.io/en/latest). |
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## FAQ |
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### How do I export Ultralytics YOLO11 models to MNN format? |
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To export your Ultralytics YOLO11 model to MNN format, follow these steps: |
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!!! example "Export" |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load the YOLO11 model |
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model = YOLO("yolo11n.pt") |
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# Export to MNN format |
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model.export(format="mnn") # creates 'yolo11n.mnn' with fp32 weight |
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model.export(format="mnn", half=True) # creates 'yolo11n.mnn' with fp16 weight |
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model.export(format="mnn", int8=True) # creates 'yolo11n.mnn' with int8 weight |
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``` |
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=== "CLI" |
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```bash |
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yolo export model=yolo11n.pt format=mnn # creates 'yolo11n.mnn' with fp32 weight |
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yolo export model=yolo11n.pt format=mnn half=True # creates 'yolo11n.mnn' with fp16 weight |
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yolo export model=yolo11n.pt format=mnn int8=True # creates 'yolo11n.mnn' with int8 weight |
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``` |
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For detailed export options, check the [Export](../modes/export.md) page in the documentation. |
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### How do I predict with an exported YOLO11 MNN model? |
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To predict with an exported YOLO11 MNN model, use the `predict` function from the YOLO class. |
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!!! example "Predict" |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load the YOLO11 MNN model |
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model = YOLO("yolo11n.mnn") |
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# Export to MNN format |
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results = mnn_model("https://ultralytics.com/images/bus.jpg") # predict with `fp32` |
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results = mnn_model("https://ultralytics.com/images/bus.jpg", half=True) # predict with `fp16` if device support |
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for result in results: |
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result.show() # display to screen |
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result.save(filename="result.jpg") # save to disk |
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``` |
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=== "CLI" |
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```bash |
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yolo predict model='yolo11n.mnn' source='https://ultralytics.com/images/bus.jpg' # predict with `fp32` |
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yolo predict model='yolo11n.mnn' source='https://ultralytics.com/images/bus.jpg' --half=True # predict with `fp16` if device support |
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``` |
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### What platforms are supported for MNN? |
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MNN is versatile and supports various platforms: |
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- **Mobile**: Android, iOS, Harmony. |
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- **Embedded Systems and IoT Devices**: Devices like Raspberry Pi and NVIDIA Jetson. |
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- **Desktop and Servers**: Linux, Windows, and macOS. |
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### How can I deploy Ultralytics YOLO11 MNN models on Mobile Devices? |
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To deploy your YOLO11 models on Mobile devices: |
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1. **Build for Android**: Follow the [MNN Android](https://github.com/alibaba/MNN/tree/master/project/android). |
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2. **Build for iOS**: Follow the [MNN iOS](https://github.com/alibaba/MNN/tree/master/project/ios). |
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3. **Build for Harmony**: Follow the [MNN Harmony](https://github.com/alibaba/MNN/tree/master/project/harmony). |
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