Ultralytics YOLO27: Join the waitlist.
Get Started

Ultralytics YOLO NCNN Export#

Deploying computer vision models on devices with limited computational power, such as mobile or embedded systems, requires careful format selection. Using an optimized format ensures that even resource-constrained devices can handle advanced computer vision tasks efficiently.

Exporting to NCNN format allows you to optimize your Ultralytics YOLO26 models for lightweight device-based applications. This guide covers how to convert your models to NCNN format for improved performance on mobile and embedded devices.

Why Export to NCNN?#

NCNN high-performance neural network inference framework

The NCNN framework, developed by Tencent, is a high-performance neural network inference framework optimized specifically for mobile platforms, including mobile phones, embedded devices, and IoT devices. NCNN is compatible with a wide range of platforms, including Linux, Android, iOS, and macOS.

NCNN is known for its fast processing speed on mobile CPUs and enables rapid deployment of deep learning models to mobile platforms for AI-powered applications.

Key Features of NCNN Models#

NCNN models provide several key features that enable on-device machine learning, helping developers deploy models on mobile, embedded, and edge devices:

  • Efficient and High-Performance: NCNN models are lightweight and optimized for mobile and embedded devices like Raspberry Pi with limited resources, while maintaining high accuracy on computer vision tasks.

  • Quantization: NCNN supports quantization, a technique that reduces the numerical precision of model weights and activations to improve performance and reduce memory footprint.

  • Compatibility: NCNN models are compatible with popular deep learning frameworks including TensorFlow, Caffe, and ONNX, allowing developers to reuse existing models and workflows.

  • Ease of Use: NCNN provides tools for converting models between formats, which helps interoperability across development environments.

  • Vulkan GPU Acceleration: NCNN supports Vulkan for GPU-accelerated inference across multiple vendors including AMD, Intel, and other non-NVIDIA GPUs, enabling high-performance deployment on a wider range of hardware.

Deployment Options with NCNN#

NCNN models are compatible with a variety of deployment platforms:

  • Mobile Deployment: Optimized for Android and iOS, for efficient on-device inference inside mobile applications.

  • Embedded Systems and IoT Devices: Suited to resource-constrained devices like Raspberry Pi and NVIDIA Jetson. If standard PyTorch inference on a Raspberry Pi is too slow, NCNN can provide significant performance improvements.

  • Desktop and Server Deployment: Supports deployment across Linux, Windows, and macOS for development, training, and evaluation workflows.

Vulkan GPU Acceleration#

NCNN supports GPU acceleration through Vulkan, enabling high-performance inference on a wide range of GPUs including AMD, Intel, and other non-NVIDIA graphics cards. This is particularly useful for:

  • Cross-Vendor GPU Support: Unlike CUDA, which is limited to NVIDIA GPUs, Vulkan works across multiple GPU vendors.
  • Multi-GPU Systems: Select a specific Vulkan device in systems with multiple GPUs using device="vulkan:0", device="vulkan:1", etc.
  • Edge and Desktop Deployments: Use GPU acceleration on devices where CUDA is not available.

To use Vulkan acceleration, specify the Vulkan device when running inference:

Vulkan Inference
from ultralytics import YOLO

# Load the exported NCNN model
ncnn_model = YOLO("./yolo26n_ncnn_model")

# Run inference with Vulkan GPU acceleration (first Vulkan device)
results = ncnn_model("https://ultralytics.com/images/bus.jpg", device="vulkan:0")

# Use second Vulkan device in multi-GPU systems
results = ncnn_model("https://ultralytics.com/images/bus.jpg", device="vulkan:1")
Vulkan Requirements

Ensure you have Vulkan drivers installed for your GPU. Most modern GPU drivers include Vulkan support by default. You can verify Vulkan availability using tools like vulkaninfo on Linux or the Vulkan SDK on Windows.

Supported Tasks#

NCNN export supports all seven Ultralytics tasks. Semantic segmentation and depth estimation are available only with YOLO26, the only family that ships those heads.

TaskYOLOv8YOLO11YOLO26
Detectāœ…āœ…āœ…
Segmentāœ…āœ…āœ…
SemanticāŒāŒāœ…
DepthāŒāŒāœ…
Classifyāœ…āœ…āœ…
Poseāœ…āœ…āœ…
OBBāœ…āœ…āœ…

Export to NCNN: Converting Your YOLO26 Model#

You can expand model compatibility and deployment flexibility by converting YOLO26 models to NCNN format.

Installation#

To install the required packages, run:

Installation
# Install the required package for YOLO26
pip install ultralytics

For detailed instructions and best practices, see the Ultralytics Installation guide. If you encounter any difficulties, consult our Common Issues guide for solutions.

Usage#

All Ultralytics YOLO26 models support export out of the box, so you can integrate them into your preferred deployment workflow. You can view the full list of supported export formats and configuration options to choose the best setup for your application.

The NCNN format supports the Export, Predict, and Validate modes. Export your model, then load the exported model to run inference or validate its accuracy.

Export
from ultralytics import YOLO

# Load a YOLO26 model
model = YOLO("yolo26n.pt")

# Export the model to NCNN format
model.export(format="ncnn")  # creates 'yolo26n_ncnn_model'
Predict
from ultralytics import YOLO

# Load the exported NCNN model
model = YOLO("./yolo26n_ncnn_model")

# Run inference
results = model("https://ultralytics.com/images/bus.jpg")
Validate
from ultralytics import YOLO

# Load the exported NCNN model
model = YOLO("./yolo26n_ncnn_model")

# Validate accuracy on the COCO8 dataset
metrics = model.val(data="coco8.yaml")

Export Arguments#

ArgumentTypeDefaultDescription
formatstr'ncnn'Target format for the exported model, defining compatibility with various deployment environments.
imgszint or tuple640Desired image size for the model input. Can be an integer for square images or a tuple (height, width) for specific dimensions.
quantizeint or strNoneQuantization precision: 16 (FP16) halves the exported weights; 32/unset exports FP32 weights, which NCNN's CPU runtime still computes in FP16 by default where the hardware supports it. Replaces the deprecated half flag.
batchint1Specifies export model batch inference size or the max number of images the exported model will process concurrently in predict mode.
devicestrNoneSpecifies the device for exporting: GPU (device=0), CPU (device=cpu), MPS for Apple silicon (device=mps).

For more details about the export process, visit the Ultralytics documentation page on exporting.

Deploying Exported YOLO26 NCNN Models#

After exporting your Ultralytics YOLO26 models to NCNN format, you can deploy them using the YOLO("yolo26n_ncnn_model/") method as shown in the usage example above. For platform-specific deployment instructions, see the following resources:

  • Android: Build and integrate NCNN models for object detection in Android applications.

  • macOS: Deploy NCNN models on macOS systems.

  • Linux: Deploy NCNN models on Linux devices including Raspberry Pi and similar embedded systems.

  • Windows x64: Deploy NCNN models on Windows x64 using Visual Studio.

Summary#

This guide covered exporting Ultralytics YOLO26 models to NCNN format for improved efficiency and speed on resource-constrained devices.

FAQ#

  • To export your Ultralytics YOLO26 model to NCNN format:

    • Python: Use the export method from the YOLO class.

      from ultralytics import YOLO

# Load a YOLO26 model
model = YOLO("yolo26n.pt")

# Export to NCNN format
model.export(format="ncnn")  # creates 'yolo26n_ncnn_model'

    • CLI: Use the yolo export command.

      yolo export model=yolo26n.pt format=ncnn # creates 'yolo26n_ncnn_model'

    For detailed export options, see the Export documentation.

  • Exporting your Ultralytics YOLO26 models to NCNN offers several benefits:

    • Efficiency: NCNN models are optimized for mobile and embedded devices, ensuring high performance even with limited computational resources.
    • Quantization: NCNN supports techniques like quantization that improve model speed and reduce memory usage.
    • Broad Compatibility: You can deploy NCNN models on multiple platforms, including Android, iOS, Linux, and macOS.
    • Vulkan GPU Acceleration: Use GPU acceleration on AMD, Intel, and other non-NVIDIA GPUs via Vulkan for faster inference.

    For more details, see the Why Export to NCNN? section.

  • NCNN, developed by Tencent, is specifically optimized for mobile platforms. Key reasons to use NCNN include:

    • High Performance: Designed for efficient and fast processing on mobile CPUs.
    • Cross-Platform: Compatible with popular frameworks such as TensorFlow and ONNX, making it easier to convert and deploy models across different platforms.
    • Community Support: Active community support ensures continual improvements and updates.

    For more information, see the Key Features of NCNN Models section.

  • NCNN supports various platforms:

    • Mobile: Android, iOS.
    • Embedded Systems and IoT Devices: Devices like Raspberry Pi and NVIDIA Jetson.
    • Desktop and Servers: Linux, Windows, and macOS.

    For improved performance on Raspberry Pi, consider using NCNN format as detailed in our Raspberry Pi Guide.

  • To deploy your YOLO26 models on Android:

    1. Build for Android: Follow the NCNN Build for Android guide.
    2. Integrate with Your App: Use the NCNN Android SDK to integrate the exported model into your application for efficient on-device inference.

    For detailed instructions, see Deploying Exported YOLO26 NCNN Models.

    For more advanced guides and use cases, visit the Ultralytics deployment guide.

  • NCNN supports Vulkan for GPU acceleration on AMD, Intel, and other non-NVIDIA GPUs. To use Vulkan:

    from ultralytics import YOLO

# Load NCNN model and run with Vulkan GPU
model = YOLO("yolo26n_ncnn_model")
results = model("image.jpg", device="vulkan:0")  # Use first Vulkan device

    For multi-GPU systems, specify the device index (e.g., vulkan:1 for the second GPU). Ensure Vulkan drivers are installed for your GPU. See the Vulkan GPU Acceleration section for more details.

Contributors
GLglenn-jocher19RAraimbekovm3LAlakshanthad3RIRizwanMunawar2ONonuralpszr1FAFaerbit1PDpderrenger1MAMatthewNoyce1ABabirami-vina1

Comments37

  1. gpsamtGitHub(edited)

    I'm testing the use of ncnn on android. I converted a yolov11 model. I've tried every way to adjust the code, but I can't. The detections are not coming out correctly. The ncnn model on windows works as can be seen in the image. I'm sharing my native code in c++. If anyone can help me I'd really appreciate it.

    Image detected with yolo predict model=C:\Users\guips\Downloads\best_ncnn_model source="C:\Users\guips\Downloads\brasil\test822.jpg" save_txt=true test822 #include <jni.h> #include <android/log.h> #include <string> #include <vector> #include <algorithm> #include <opencv2/opencv.hpp> #include <ncnn/net.h> // #include <stb_image.h> // Uncomment if using stb_image

    #define LOG_TAG "NCNN_DETECTOR" #define LOGD(...) __android_log_print(ANDROID_LOG_DEBUG, LOG_TAG, VA_ARGS) #define LOGI(...) __android_log_print(ANDROID_LOG_INFO, LOG_TAG, VA_ARGS) #define LOGE(...) __android_log_print(ANDROID_LOG_ERROR, LOG_TAG, VA_ARGS)

    static ncnn::Net g_net; static std::vectorstd::string g_labels; static bool g_model_loaded = false;

    struct Detection { float x, y, width, height; int label; float prob; };

    static float sigmoid(float x) { return 1.0f / (1.0f + std::exp(-x)); }

    static float calculate_iou(const Detection& a, const Detection& b) { float x1 = std::max(a.x, b.x); float y1 = std::max(a.y, b.y); float x2 = std::min(a.x + a.width, b.x + b.width); float y2 = std::min(a.y + a.height, b.y + b.height);

    float inter_area = std::max(0.0f, x2 - x1) * std::max(0.0f, y2 - y1);
float union_area = a.width * a.height + b.width * b.height - inter_area;

return union_area > 0 ? inter_area / union_area : 0.0f;

    }

    extern "C" JNIEXPORT jboolean JNICALL Java_com_example_ncnnmy_1ok_MainActivity_loadModel( JNIEnv* env, jobject thiz, jstring param_path, jstring bin_path, jstring labels_path) {

    const char* param_path_str = env->GetStringUTFChars(param_path, nullptr);
const char* bin_path_str = env->GetStringUTFChars(bin_path, nullptr);
const char* labels_path_str = env->GetStringUTFChars(labels_path, nullptr);

LOGI("Starting model loading...");
LOGD("Param path: %s", param_path_str);
LOGD("Bin path: %s", bin_path_str);
LOGD("Labels path: %s", labels_path_str);

g_net.clear();
g_labels.clear();
g_model_loaded = false;

g_net.opt.num_threads = 4;
g_net.opt.use_vulkan_compute = false;
g_net.opt.use_fp16_packed = false;
g_net.opt.use_fp16_storage = false;
g_net.opt.use_fp16_arithmetic = false;

int ret = g_net.load_param(param_path_str);
if (ret != 0) {
    LOGE("Failed to load .param file (%d)", ret);
    env->ReleaseStringUTFChars(param_path, param_path_str);
    env->ReleaseStringUTFChars(bin_path, bin_path_str);
    env->ReleaseStringUTFChars(labels_path, labels_path_str);
    return JNI_FALSE;
}

ret = g_net.load_model(bin_path_str);
if (ret != 0) {
    LOGE("Failed to load .bin file (%d)", ret);
    env->ReleaseStringUTFChars(param_path, param_path_str);
    env->ReleaseStringUTFChars(bin_path, bin_path_str);
    env->ReleaseStringUTFChars(labels_path, labels_path_str);
    return JNI_FALSE;
}

FILE* fp = fopen(labels_path_str, "r");
if (!fp) {
    LOGE("Failed to open labels file: %s", labels_path_str);
    env->ReleaseStringUTFChars(param_path, param_path_str);
    env->ReleaseStringUTFChars(bin_path, bin_path_str);
    env->ReleaseStringUTFChars(labels_path, labels_path_str);
    return JNI_FALSE;
}

char buffer[256];
while (fgets(buffer, sizeof(buffer), fp) != nullptr) {
    std::string label(buffer);
    label.erase(std::remove(label.begin(), label.end(), '\n'), label.end());
    label.erase(std::remove(label.begin(), label.end(), '\r'), label.end());
    if (!label.empty()) {
        g_labels.push_back(label);
    }
}
fclose(fp);

if (g_labels.empty()) {
    LOGE("No labels loaded from %s", labels_path_str);
    env->ReleaseStringUTFChars(param_path, param_path_str);
    env->ReleaseStringUTFChars(bin_path, bin_path_str);
    env->ReleaseStringUTFChars(labels_path, labels_path_str);
    return JNI_FALSE;
}

LOGI("Model loaded successfully");
LOGI("Total labels loaded: %zu", g_labels.size());
g_model_loaded = true;

env->ReleaseStringUTFChars(param_path, param_path_str);
env->ReleaseStringUTFChars(bin_path, bin_path_str);
env->ReleaseStringUTFChars(labels_path, labels_path_str);
return JNI_TRUE;

    }

    extern "C" JNIEXPORT jstring JNICALL Java_com_example_ncnnmy_1ok_MainActivity_detect( JNIEnv* env, jobject thiz, jstring image_path, jdouble conf_threshold, jdouble nms_threshold) {

    LOGI("Running updated detect method (no debug image)");

if (!g_model_loaded) {
    LOGE("Model not loaded");
    return env->NewStringUTF("error:model_not_loaded");
}

const char* image_path_str = env->GetStringUTFChars(image_path, nullptr);
LOGI("Processing image: %s", image_path_str);

// Option 1: Use OpenCV (current)
cv::Mat image = cv::imread(image_path_str, cv::IMREAD_COLOR);
env->ReleaseStringUTFChars(image_path, image_path_str);

/*
// Option 2: Use stb_image (uncomment to test)
int width, height, channels;
unsigned char* image_data = stbi_load(image_path_str, &width, &height, &channels, 3);
env->ReleaseStringUTFChars(image_path, image_path_str);
if (!image_data) {
    LOGE("Failed to load image with stb_image");
    return env->NewStringUTF("error:image_load_failed");
}
cv::Mat image(height, width, CV_8UC3, image_data);
*/

if (image.empty()) {
    LOGE("Failed to load image");
    // stbi_image_free(image_data); // Uncomment if using stb_image
    return env->NewStringUTF("error:image_load_failed");
}

LOGD("Input image: %dx%d, channels=%d", image.cols, image.rows, image.channels());

const int target_size = 640;
int img_w = image.cols;
int img_h = image.rows;

float scale = std::min((float)target_size / img_w, (float)target_size / img_h);
int new_w = static_cast<int>(img_w * scale);
int new_h = static_cast<int>(img_h * scale);
int dx = (target_size - new_w) / 2;
int dy = (target_size - new_h) / 2;

cv::Mat resized;
cv::resize(image, resized, cv::Size(new_w, new_h));
cv::Mat input_mat(target_size, target_size, CV_8UC3, cv::Scalar(114, 114, 114));
resized.copyTo(input_mat(cv::Rect(dx, dy, new_w, new_h)));

cv::cvtColor(input_mat, input_mat, cv::COLOR_BGR2RGB);
input_mat.convertTo(input_mat, CV_32FC3, 1.0 / 255.0);

ncnn::Mat in = ncnn::Mat::from_pixels(input_mat.data, ncnn::Mat::PIXEL_RGB, target_size, target_size);

// stbi_image_free(image_data); // Uncomment if using stb_image

ncnn::Extractor ex = g_net.create_extractor();
ex.input("in0", in);
ncnn::Mat out;
ex.extract("out0", out);

if (out.empty()) {
    LOGE("Empty output from model");
    return env->NewStringUTF("error:empty_model_output");
}

LOGD("Output dimensions: w=%d, h=%d, c=%d", out.w, out.h, out.c);

int num_preds, num_classes;
bool is_decoupled = false;

if (out.c == 1) {
    LOGD("Possible decoupled heads detected, checking for additional outputs...");
    ncnn::Mat boxes, scores, classes;
    ex.extract("boxes", boxes);
    ex.extract("scores", scores);
    ex.extract("classes", classes);
    if (!boxes.empty() && !scores.empty() && !classes.empty()) {
        is_decoupled = true;
        LOGD("Decoupled heads detected: boxes=%dx%d, scores=%dx%d, classes=%dx%d",
             boxes.w, boxes.c, scores.w, scores.c, classes.w, classes.c);
        return env->NewStringUTF("error:decoupled_heads_not_implemented");
    }
}

if (!is_decoupled) {
    if (out.c >= 5) {
        num_preds = out.h * out.w;
        num_classes = out.c - 5;
        LOGD("Interpreting as [1, num_predictions, 5 + num_classes]: num_preds=%d, num_classes=%d",
             num_preds, num_classes);
    } else if (out.h >= 5) {
        num_preds = out.w;
        num_classes = out.h - 5;
        LOGD("Interpreting as [1, 5 + num_classes, num_predictions]: num_preds=%d, num_classes=%d",
             num_preds, num_classes);
    } else {
        LOGE("Invalid output dimensions: w=%d, h=%d, c=%d", out.w, out.h, out.c);
        return env->NewStringUTF("error:invalid_output_dimensions");
    }
}

if (num_classes != g_labels.size()) {
    LOGE("Mismatch between model outputs (%d) and labels (%zu)",
         num_classes, g_labels.size());
    return env->NewStringUTF("error:model_labels_mismatch");
}

std::vector<Detection> detections;
int stride = (out.c >= 5) ? out.c : out.h;
const float* data = (float*)out.data;

// Log raw outputs
if (num_preds > 0) {
    LOGD("Raw output sample: x=%.3f, y=%.3f, w=%.3f, h=%.3f, obj=%.3f",
         data[0], data[1], data[2], data[3], data[4]);
    // Log first class score
    if (num_classes > 0) {
        LOGD("Raw class score sample: class_0=%.3f", data[5]);
    }
}

for (int i = 0; i < num_preds; i++) {
    const float* pred = data + i * stride;

    float obj_score = sigmoid(pred[4]);
    if (obj_score < conf_threshold) continue;

    int class_id = 0;
    float max_score = sigmoid(pred[5]);
    for (int j = 1; j < num_classes; j++) {
        float score = sigmoid(pred[5 + j]);
        if (score > max_score) {
            max_score = score;
            class_id = j;
        }
    }

    float confidence = obj_score * max_score;
    if (confidence < conf_threshold) continue;

    // Normalize coordinates with sigmoid (assuming logits)
    float x_center = sigmoid(pred[0]) * target_size;
    float y_center = sigmoid(pred[1]) * target_size;
    float width = sigmoid(pred[2]) * target_size;
    float height = sigmoid(pred[3]) * target_size;

    // Adjust for letterbox
    float x = (x_center - width / 2 - dx) / scale;
    float y = (y_center - height / 2 - dy) / scale;
    width /= scale;
    height /= scale;

    LOGD("Prediction %d: x_center=%.2f, y_center=%.2f, width=%.2f, height=%.2f",
         i, x_center, y_center, width, height);
    LOGD("Transformed %d: x=%.2f, y=%.2f, width=%.2f, height=%.2f",
         i, x, y, width, height);

    // Validate coordinates
    if (x < 0 || y < 0 || width <= 0 || height <= 0 || x + width > img_w || y + height > img_h) {
        LOGD("Skipping invalid detection %d: x=%.2f, y=%.2f, w=%.2f, h=%.2f",
             i, x, y, width, height);
        continue;
    }

    // Minimum size filter
    if (width < 50 || height < 50) {
        LOGD("Skipping small detection %d: w=%.2f, h=%.2f", i, width, height);
        continue;
    }

    detections.push_back({x, y, width, height, class_id, confidence});
}

// NMS
std::sort(detections.begin(), detections.end(), [](const Detection& a, const Detection& b) {
    return a.prob > b.prob;
});

std::vector<bool> suppressed(detections.size(), false);
std::vector<Detection> final_detections;

for (size_t i = 0; i < detections.size(); i++) {
    if (suppressed[i]) continue;
    final_detections.push_back(detections[i]);

    for (size_t j = i + 1; j < detections.size(); j++) {
        if (suppressed[j]) continue;
        float iou = calculate_iou(detections[i], detections[j]);
        if (iou > nms_threshold) {
            suppressed[j] = true;
        }
    }
}

LOGD("Detections after NMS: %zu", final_detections.size());

// Format results
std::string result_str;
for (const auto& det : final_detections) {
    if (!result_str.empty()) result_str += ";";
    char buf[256];
    snprintf(buf, sizeof(buf), "%s:%.2f,%.0f,%.0f,%.0f,%.0f",
             g_labels[det.label].c_str(), det.prob,
             det.x, det.y, det.width, det.height);
    result_str += buf;
}

return env->NewStringUTF(result_str.empty() ? "none" : result_str.c_str());

    }

    Here is a log of the output of the flutter application.

    [ ] D/NCNN_DETECTOR( 9180): Prediction 3730: x_center=640.00, y_center=640.00, width=640.00, height=640.00 [ ] D/NCNN_DETECTOR( 9180): Transformed 3730: x=320.00, y=240.00, width=640.00, height=640.00 [ ] D/NCNN_DETECTOR( 9180): Skipping invalid detection 3730: x=320.00, y=240.00, w=640.00, h=640.00 [ ] D/NCNN_DETECTOR( 9180): Prediction 3731: x_center=640.00, y_center=640.00, width=640.00, height=640.00 [ ] D/NCNN_DETECTOR( 9180): Transformed 3731: x=320.00, y=240.00, width=640.00, height=640.00 [ ] D/NCNN_DETECTOR( 9180): Skipping invalid detection 3731: x=320.00, y=240.00, w=640.00, h=640.00 [ ] D/NCNN_DETECTOR( 9180): Prediction 3732: x_center=640.00, y_center=640.00, width=640.00, height=640.00 [ ] D/NCNN_DETECTOR( 9180): Transformed 3732: x=320.00, y=240.00, width=640.00, height=640.00 [ ] D/NCNN_DETECTOR( 9180): Skipping invalid detection 3732: x=320.00, y=240.00, w=640.00, h=640.00 [ ] D/NCNN_DETECTOR( 9180): Detections after NMS: 0 [ ] I/flutter ( 9180): Detection result: none [ ] I/flutter ( 9180): STATUS: No objects detected [ ] E/libEGL ( 9180): called unimplemented OpenGL ES API

    image

    1. Glenn JocherUltralytics(edited)

      @gpsamt looking at your logs, the issue appears to be with output format parsing. YOLO11 NCNN outputs have no separate objectness score, and the class scores are already sigmoid-activated, so don't apply sigmoid again. Try parsing each prediction like this:

      // Box is x_center, y_center, width, height (no sigmoid needed)
float x_center = pred[0];
float y_center = pred[1];
float width = pred[2];
float height = pred[3];
// pred[4 .. 4+nc) are the per-class scores; take the max class as the confidence

// Convert from center format to corner format
float x = (x_center - width / 2 - dx) / scale;
float y = (y_center - height / 2 - dy) / scale;

      Also verify your model's actual output format by checking if coordinates are normalized (0-1) or absolute (0-640). The repeated 640.00 values suggest coordinate parsing issues. For official Android deployment examples, refer to the NCNN Android documentation.

  2. aram199922GitHub

    Hey everyone, I have trained yolo11n.pt pretrained model on my custom dataset. Run the detection on a video and got quite good results. Now trying to export ncnn with the given guide, files are being generated correctly, however the detection on the same video seems corrupted. I am trying to detect potholes on road, however ncnn model predict 300 potholes on each frame. Any suggestion?

    1. aram199922GitHub

      One more note is that I have trained my model with 736p, 640p modes are converting fine. But does this mean I can't conver higher resolution trained model to ncnn?

    2. Glenn JocherUltralytics

      @aram199922 The issue with your NCNN model detecting 300 potholes likely relates to confidence thresholds. When exporting to NCNN, default thresholds may not transfer properly. Try explicitly setting a higher confidence threshold during inference:

      # Load the NCNN model
ncnn_model = YOLO("your_model_ncnn_model")

# Run inference with higher confidence threshold
results = ncnn_model("video.mp4", conf=0.5)  # Adjust this value as needed

      Regarding your 736p model - yes, you can export higher resolution models to NCNN by specifying the custom size during export:

      model = YOLO("your_trained_model.pt")
model.export(format="ncnn", imgsz=736)  # Use your training resolution

      This ensures the exported model maintains the same input dimensions you used during training. Let me know if this helps resolve the issue!

    3. gpsamtGitHub

      Hello. Does anyone have any example of implementing the converted ncnn model in android?

    1. Glenn JocherUltralytics(edited)

      @PabloBR99 YOLO12 isn't recommended for NCNN export due to training instability and performance limitations. For NCNN deployments, we strongly recommend using YOLO26 (or YOLO11), which offer optimized performance and stability across all platforms. You can find the NCNN export guide in our documentation.

  4. gpsamtGitHub

    I am exporting a YOLOv11s model to ncnn. My model has 4 classes, however, when I perform the export, it appears that it is identifying 3 classes: PyTorch: starting from '/content/drive/MyDrive/IAGeneration/yolo11/runs/detect/train3/weights/best.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 8, 8400) (18.3 MB). In Android Studio, this message appears:

    2025-03-04 17:08:58.490 3853-3853 NcnnDetector com.example.agroinsect D Starting detection 2025-03-04 17:08:58.502 3853-3853 NcnnDetector com.example.test D Param file size: 22768 bytes 2025-03-04 17:08:58.504 3853-3853 NcnnDetector com.example.test D Load param result: 0 2025-03-04 17:08:58.504 3853-3853 NcnnDetector com.example.test D Bin file size: 10633344 bytes 2025-03-04 17:08:58.596 3853-3853 NcnnDetector com.example.test D Load model result: 10633344 2025-03-04 17:08:58.597 3853-3853 NcnnDetector com.example.test D Failed to load model file

    [ +82 ms] D/CompatChangeReporter( 3853): Compat change id reported: 312399441; UID 10209; state: ENABLED [ +11 ms] D/nativeloader( 3853): Load /data/app/~~yVwXpMN1F9boaEz-oh0Bpw==/com.example.test-J2_0Fz9v3HSf0USV8uxz6Q==/lib/x86_64/libncnn_detector.so using ns clns-7 from class loader (caller=/data/app/~~yVwXpMN1F9boaEz-oh0Bpw==/com.example.test-J2_0Fz9v3HSf0USV8uxz6Q==/base.apk!classes4.dex): ok [ ] D/NcnnDetector( 3853): Library ncnn_detector loaded successfully [ ] D/NcnnDetector( 3853): Starting detection [ +10 ms] D/NcnnDetector( 3853): Param file size: 22768 bytes [ +2 ms] D/NcnnDetector( 3853): Load param result: 0 [ ] D/NcnnDetector( 3853): Bin file size: 10633344 bytes [ +97 ms] D/NcnnDetector( 3853): Load model result: 10633344 [ +1 ms] D/NcnnDetector( 3853): Failed to load model file [ ] I/flutter ( 3853): Native detect result: null [ ] I/flutter ( 3853): Detections received: [] [ ] I/flutter ( 3853): No detections found.

    I have repeated the process several times, but I have not been able to resolve it. I found this answer: 'The current problem (Load model result: 37825744) suggests difficulties in exporting or compatibility.'"

    1. Glenn JocherUltralytics

      @gpsamt thank you for reporting this issue. Let's address both aspects systematically:

      1. Class Count Mismatch:
        The output shape (1,8,8400) suggests your model is correctly configured for 4 classes (4 classes + 4 box coordinates = 8). This indicates the export itself is successful. The Android implementation might need adjustment to handle the 4-class output format.

      2. Model Loading Failure:
        The "Failed to load model file" error typically indicates either:

      • Missing/corrupted .param or .bin files in deployment
      • Architecture mismatch between PyTorch and NCNN versions
      • Incorrect Android NCNN implementation

      We recommend:

      1. Verify successful local inference with your NCNN model using yolo predict model='yolo11n_ncnn_model' before Android deployment
      2. Ensure you're using the latest NCNN Android library from Tencent's NCNN GitHub repo
      3. Double-check that all NCNN model files are properly bundled in your Android assets

      For specific Android implementation guidance, please consult NCNN's official Android deployment documentation.

    2. gpsamtGitHub(edited)

      I made several attempts and ended up not proceeding, however I really wanted to test my trained model running on android with ncnn. I made an export on windows of a yolo11 model trained in colab. To test the converted model yolo --> ncnn I did a test with an image with two objects. C:\Users\guips\Downloads>yolo predict model=C:\Users\guips\Downloads\best_ncnn_model source="C:\Users\guips\Downloads\brasil\test129.jpg" save_txt=true WARNING āš ļø Unable to automatically guess model task, assuming 'task=detect'. Explicitly define task for your model, i.e. 'task=detect', 'segment', 'classify','pose' or 'obb'. Ultralytics 8.3.105 šŸš€ Python-3.11.9 torch-2.6.0+cpu CPU (12th Gen Intel Core(TM) i7-12700KF) Loading C:\Users\guips\Downloads\best_ncnn_model for NCNN inference...

      image 1/1 C:\Users\guips\Downloads\brasil\test129.jpg: 640x640 2 Car, 103.0ms Speed: 20.4ms preprocess, 103.0ms inference, 24.2ms postprocess per image at shape (1, 3, 640, 640) Results saved to runs\detect\predict6 1 label saved to runs\detect\predict6\labels šŸ’” Learn more at https://docs.ultralytics.com/modes/predict
      When I try to run the same image in the android application with flutter I can't. In TFLITE the output was (1,8,8400) Now it seems to be outputting (1,9,8400). I'm not able to work with the output data. I/flutter (14031): Labels loaded: 4

      E/libEGL (14031): called unimplemented OpenGL ES API I/flutter (14031): Storage permission denied D/InsetsController(14031): hide(ime(), fromIme=false) I/ImeTracker(14031): com.example.ncnnmy_ok:e29c5e1e: onCancelled at PHASE_CLIENT_ALREADY_HIDDEN E/libEGL (14031): called unimplemented OpenGL ES API D/InsetsController(14031): hide(ime(), fromIme=false) I/ImeTracker(14031): com.example.ncnnmy_ok:6f2d251e: onCancelled at PHASE_CLIENT_ALREADY_HIDDEN E/libEGL (14031): called unimplemented OpenGL ES API D/CompatChangeReporter(14031): Compat change id reported: 312399441; UID 10223; state: ENABLED I/flutter (14031): Image dimensions: 680.0x453.0 I/flutter (14031): Running inference on /data/user/0/com.example.ncnnmy_ok/cache/a6b569b8-5ff1-4466-ad32-49121fb24aa4/38.jpg D/MainActivity(14031): Received method call: detect I/NCNN (14031): Detecting on image: /data/user/0/com.example.ncnnmy_ok/cache/a6b569b8-5ff1-4466-ad32-49121fb24aa4/38.jpg I/NCNN (14031): Original image size: 680x453 I/NCNN (14031): Output dimensions: w=8400, h=9, c=1 I/NCNN (14031): Output saved to /data/user/0/com.example.ncnnmy_ok/cache/a6b569b8-5ff1-4466-ad32-49121fb24aa4/38.jpg_ncnn_output.txt W/NCNN (14031): Output has 4424 channels, expected 8. Using first 8 channels W/NCNN (14031): Invalid box at idx=9: w=0.000000, h=0.000000 W/NCNN (14031): Invalid box at idx=15: w=-0.010868, h=-0.063400 W/NCNN (14031): Invalid box at idx=16: w=-0.630245, h=-0.447460 W/NCNN (14031): Invalid box at idx=17: w=-0.685141, h=-0.837424 . . . W/NCNN (14031): Invalid box at idx=347: w=-0.077838, h=0.846499 F/libc (14031): Fatal signal 11 (SIGSEGV), code 2 (SEGV_ACCERR), fault addr 0x71c0c2499110 in tid 14031 (ample.ncnnmy_ok), pid 14031 (ample.ncnnmy_ok)

      Build fingerprint: 'google/sdk_gphone64_x86_64/emu64xa:16/BP22.250221.010/13193326:user/release-keys' Revision: '0' ABI: 'x86_64' Timestamp: 2025-04-15 01:29:54.291432600+0000 Process uptime: 156s Cmdline: com.example.ncnnmy_ok pid: 14031, tid: 14031, name: ample.ncnnmy_ok >>> com.example.ncnnmy_ok <<< uid: 10223 signal 11 (SIGSEGV), code 2 (SEGV_ACCERR), fault addr 0x000071c0c2499110 rax 000071c0c2499100 rbx 000071c247d33be0 rcx 0000000000000004 rdx 0000000000000008 r8 00007ffc6ffc4080 r9 00007ffc6ffc40a0 r10 0000000000e8cada r11 0000000000000213 r12 00007ffc6ff903b0 r13 000071c247d33c88 r14 00007ffc6ff90718 r15 000071c247d33be0 rdi 00007ffc6ff8f840 rsi 0000000000000167 rbp 00007ffc6ff8f970 rsp 00007ffc6ff8f8b0 rip 000071c053d969ea 90 total frames backtrace: #00 pc 00000000000109ea /data/app/~~ygC8oOQ2RMgj_IeB2wDRKw==/com.example.ncnnmy_ok-1Q_3JHdbdZSe1RwVvc5qjQ==/lib/x86_64/libnative-lib.so (BuildId: 63a90d5f64ab095e3ac958c5db79324dfa15e937) #01 pc 000000000000fd3a /data/app/~~ygC8oOQ2RMgj_IeB2wDRKw==/com.example.ncnnmy_ok-1Q_3JHdbdZSe1RwVvc5qjQ==/lib/x86_64/libnative-lib.so (BuildId: 63a90d5f64ab095e3ac958c5db79324dfa15e937) #02 pc 000000000000e8aa /data/app/~~ygC8oOQ2RMgj_IeB2wDRKw==/com.example.ncnnmy_ok-1Q_3JHdbdZSe1RwVvc5qjQ==/lib/x86_64/libnative-lib.so (Java_com_example_ncnnmy_1ok_MainActivity_detect+2634) (BuildId: 63a90d5f64ab095e3ac958c5db79324dfa15e937) #03 pc 000000000022c80b /apex/com.android.art/lib64/libart.so (art_quick_generic_jni_trampoline+219) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #04 pc 0000000000211dd4 /apex/com.android.art/lib64/libart.so (art_quick_invoke_stub+756) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #05 pc 0000000000461715 /apex/com.android.art/lib64/libart.so (art::ArtMethod::Invoke(art::Thread*, unsigned int*, unsigned int, art::JValue*, char const*)+181) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #06 pc 00000000005f74b2 /apex/com.android.art/lib64/libart.so (bool art::interpreter::DoCall<false>(art::ArtMethod*, art::Thread*, art::ShadowFrame&, art::Instruction const*, unsigned short, bool, art::JValue*)+2402) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #07 pc 0000000000233611 /apex/com.android.art/lib64/libart.so (void art::interpreter::ExecuteSwitchImplCpp<false>(art::interpreter::SwitchImplContext*)+10625) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #08 pc 000000000022eb25 /apex/com.android.art/lib64/libart.so (ExecuteSwitchImplAsm+5) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #09 pc 00000000000006a4 anonymous:71c395b1d000 (com.example.ncnnmy_ok.MainActivity.configureFlutterEngine$lambda$0+0) #10 pc 00000000005ef3f2 /apex/com.android.art/lib64/libart.so (art::interpreter::Execute(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame&, art::JValue, bool, bool) (.__uniq.112435418011751916792819755956732575238)+466) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #11 pc 00000000005f6706 /apex/com.android.art/lib64/libart.so (art::interpreter::ArtInterpreterToInterpreterBridge(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame*, art::JValue*)+102) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #12 pc 00000000005f7498 /apex/com.android.art/lib64/libart.so (bool art::interpreter::DoCall<false>(art::ArtMethod*, art::Thread*, art::ShadowFrame&, art::Instruction const*, unsigned short, bool, art::JValue*)+2376) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #13 pc 0000000000234b05 /apex/com.android.art/lib64/libart.so (void art::interpreter::ExecuteSwitchImplCpp<false>(art::interpreter::SwitchImplContext*)+15989) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #14 pc 000000000022eb25 /apex/com.android.art/lib64/libart.so (ExecuteSwitchImplAsm+5) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #15 pc 0000000000000604 anonymous:71c395b1d000 (com.example.ncnnmy_ok.MainActivity.$r8$lambda$8rE-0sywqH2S4u1lhbBM5kfBEYE+0) #16 pc 00000000005ef3f2 /apex/com.android.art/lib64/libart.so (art::interpreter::Execute(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame&, art::JValue, bool, bool) (.__uniq.112435418011751916792819755956732575238)+466) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #17 pc 00000000005f6706 /apex/com.android.art/lib64/libart.so (art::interpreter::ArtInterpreterToInterpreterBridge(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame*, art::JValue*)+102) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #18 pc 00000000005f7498 /apex/com.android.art/lib64/libart.so (bool art::interpreter::DoCall<false>(art::ArtMethod*, art::Thread*, art::ShadowFrame&, art::Instruction const*, unsigned short, bool, art::JValue*)+2376) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #19 pc 0000000000234b05 /apex/com.android.art/lib64/libart.so (void art::interpreter::ExecuteSwitchImplCpp<false>(art::interpreter::SwitchImplContext*)+15989) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #20 pc 000000000022eb25 /apex/com.android.art/lib64/libart.so (ExecuteSwitchImplAsm+5) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #21 pc 00000000000005b8 anonymous:71c395b1d000 (com.example.ncnnmy_ok.MainActivity$$ExternalSyntheticLambda0.onMethodCall+0) #22 pc 00000000005ef3f2 /apex/com.android.art/lib64/libart.so (art::interpreter::Execute(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame&, art::JValue, bool, bool) (.__uniq.112435418011751916792819755956732575238)+466) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #23 pc 00000000005f6706 /apex/com.android.art/lib64/libart.so (art::interpreter::ArtInterpreterToInterpreterBridge(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame*, art::JValue*)+102) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #24 pc 00000000005f7498 /apex/com.android.art/lib64/libart.so (bool art::interpreter::DoCall<false>(art::ArtMethod*, art::Thread*, art::ShadowFrame&, art::Instruction const*, unsigned short, bool, art::JValue*)+2376) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #25 pc 00000000002346e5 /apex/com.android.art/lib64/libart.so (void art::interpreter::ExecuteSwitchImplCpp<false>(art::interpreter::SwitchImplContext*)+14933) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #26 pc 000000000022eb25 /apex/com.android.art/lib64/libart.so (ExecuteSwitchImplAsm+5) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #27 pc 00000000001bfabc anonymous:71c054260000 (io.flutter.plugin.common.MethodChannel$IncomingMethodCallHandler.onMessage+0) #28 pc 00000000005ef3f2 /apex/com.android.art/lib64/libart.so (art::interpreter::Execute(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame&, art::JValue, bool, bool) (.__uniq.112435418011751916792819755956732575238)+466) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #29 pc 00000000005f6706 /apex/com.android.art/lib64/libart.so (art::interpreter::ArtInterpreterToInterpreterBridge(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame*, art::JValue*)+102) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #30 pc 00000000005f7498 /apex/com.android.art/lib64/libart.so (bool art::interpreter::DoCall<false>(art::ArtMethod*, art::Thread*, art::ShadowFrame&, art::Instruction const*, unsigned short, bool, art::JValue*)+2376) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #31 pc 00000000002346e5 /apex/com.android.art/lib64/libart.so (void art::interpreter::ExecuteSwitchImplCpp<false>(art::interpreter::SwitchImplContext*)+14933) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #32 pc 000000000022eb25 /apex/com.android.art/lib64/libart.so (ExecuteSwitchImplAsm+5) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #33 pc 00000000001b1954 anonymous:71c054260000 (io.flutter.embedding.engine.dart.DartMessenger.invokeHandler+0) #34 pc 00000000005ef3f2 /apex/com.android.art/lib64/libart.so (art::interpreter::Execute(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame&, art::JValue, bool, bool) (.__uniq.112435418011751916792819755956732575238)+466) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #35 pc 00000000005f6706 /apex/com.android.art/lib64/libart.so (art::interpreter::ArtInterpreterToInterpreterBridge(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame*, art::JValue*)+102) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #36 pc 00000000005f7498 /apex/com.android.art/lib64/libart.so (bool art::interpreter::DoCall<false>(art::ArtMethod*, art::Thread*, art::ShadowFrame&, art::Instruction const*, unsigned short, bool, art::JValue*)+2376) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #37 pc 0000000000233611 /apex/com.android.art/lib64/libart.so (void art::interpreter::ExecuteSwitchImplCpp<false>(art::interpreter::SwitchImplContext*)+10625) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #38 pc 000000000022eb25 /apex/com.android.art/lib64/libart.so (ExecuteSwitchImplAsm+5) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #39 pc 00000000001b19d8 anonymous:71c054260000 (io.flutter.embedding.engine.dart.DartMessenger.lambda$dispatchMessageToQueue$0$io-flutter-embedding-engine-dart-DartMessenger+0) #40 pc 00000000005ef3f2 /apex/com.android.art/lib64/libart.so (art::interpreter::Execute(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame&, art::JValue, bool, bool) (.__uniq.112435418011751916792819755956732575238)+466) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #41 pc 00000000005f6706 /apex/com.android.art/lib64/libart.so (art::interpreter::ArtInterpreterToInterpreterBridge(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame*, art::JValue*)+102) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #42 pc 00000000005f827f /apex/com.android.art/lib64/libart.so (bool art::interpreter::DoCall<true>(art::ArtMethod*, art::Thread*, art::ShadowFrame&, art::Instruction const*, unsigned short, bool, art::JValue*)+2319) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #43 pc 000000000023482d /apex/com.android.art/lib64/libart.so (void art::interpreter::ExecuteSwitchImplCpp<false>(art::interpreter::SwitchImplContext*)+15261) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #44 pc 000000000022eb25 /apex/com.android.art/lib64/libart.so (ExecuteSwitchImplAsm+5) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #45 pc 00000000001b1168 anonymous:71c054260000 (io.flutter.embedding.engine.dart.DartMessenger$$ExternalSyntheticLambda0.run+0) #46 pc 00000000005ef3f2 /apex/com.android.art/lib64/libart.so (art::interpreter::Execute(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame&, art::JValue, bool, bool) (.__uniq.112435418011751916792819755956732575238)+466) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #47 pc 00000000009978fb /apex/com.android.art/lib64/libart.so (artQuickToInterpreterBridge+907) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #48 pc 000000000022c99c /apex/com.android.art/lib64/libart.so (art_quick_to_interpreter_bridge+140) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #49 pc 0000000000160efc [anon_shmem:dalvik-jit-code-cache] (offset 0x2000000) (android.os.Handler.handleCallback+156) #50 pc 00000000000718af [anon_shmem:dalvik-jit-code-cache] (offset 0x2000000) (android.os.Handler.dispatchMessage+159) #51 pc 000000000006e9a4 [anon_shmem:dalvik-jit-code-cache] (offset 0x2000000) (android.os.Looper.loopOnce+1348) #52 pc 0000000000212136 /apex/com.android.art/lib64/libart.so (art_quick_invoke_static_stub+806) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #53 pc 000000000046175c /apex/com.android.art/lib64/libart.so (art::ArtMethod::Invoke(art::Thread*, unsigned int*, unsigned int, art::JValue*, char const*)+252) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #54 pc 00000000005f74b2 /apex/com.android.art/lib64/libart.so (bool art::interpreter::DoCall<false>(art::ArtMethod*, art::Thread*, art::ShadowFrame&, art::Instruction const*, unsigned short, bool, art::JValue*)+2402) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #55 pc 0000000000234b05 /apex/com.android.art/lib64/libart.so (void art::interpreter::ExecuteSwitchImplCpp<false>(art::interpreter::SwitchImplContext*)+15989) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #56 pc 000000000022eb25 /apex/com.android.art/lib64/libart.so (ExecuteSwitchImplAsm+5) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #57 pc 000000000027c140 /system/framework/framework.jar (offset 0x1274000) (android.os.Looper.loop+0) #58 pc 00000000005ef3f2 /apex/com.android.art/lib64/libart.so (art::interpreter::Execute(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame&, art::JValue, bool, bool) (.__uniq.112435418011751916792819755956732575238)+466) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #59 pc 00000000005f6706 /apex/com.android.art/lib64/libart.so (art::interpreter::ArtInterpreterToInterpreterBridge(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame*, art::JValue*)+102) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #60 pc 00000000005f7498 /apex/com.android.art/lib64/libart.so (bool art::interpreter::DoCall<false>(art::ArtMethod*, art::Thread*, art::ShadowFrame&, art::Instruction const*, unsigned short, bool, art::JValue*)+2376) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #61 pc 0000000000234b05 /apex/com.android.art/lib64/libart.so (void art::interpreter::ExecuteSwitchImplCpp<false>(art::interpreter::SwitchImplContext*)+15989) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #62 pc 000000000022eb25 /apex/com.android.art/lib64/libart.so (ExecuteSwitchImplAsm+5) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #63 pc 00000000001f1254 /system/framework/framework.jar (offset 0x7000) (android.app.ActivityThread.main+0) #64 pc 00000000005ef3f2 /apex/com.android.art/lib64/libart.so (art::interpreter::Execute(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame&, art::JValue, bool, bool) (.__uniq.112435418011751916792819755956732575238)+466) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #65 pc 00000000009978fb /apex/com.android.art/lib64/libart.so (artQuickToInterpreterBridge+907) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #66 pc 000000000022c99c /apex/com.android.art/lib64/libart.so (art_quick_to_interpreter_bridge+140) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #67 pc 0000000000212136 /apex/com.android.art/lib64/libart.so (art_quick_invoke_static_stub+806) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #68 pc 000000000046175c /apex/com.android.art/lib64/libart.so (art::ArtMethod::Invoke(art::Thread*, unsigned int*, unsigned int, art::JValue*, char const*)+252) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #69 pc 000000000088b130 /apex/com.android.art/lib64/libart.so (_jobject* art::InvokeMethod<(art::PointerSize)8>(art::ScopedObjectAccessAlreadyRunnable const&, _jobject*, _jobject*, _jobject*, unsigned long)+2240) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #70 pc 00000000007e0f1a /apex/com.android.art/lib64/libart.so (art::Method_invoke(_JNIEnv*, _jobject*, _jobject*, _jobjectArray*) (.__uniq.165753521025965369065708152063621506277)+42) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #71 pc 000000000022c80b /apex/com.android.art/lib64/libart.so (art_quick_generic_jni_trampoline+219) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #72 pc 0000000000211dd4 /apex/com.android.art/lib64/libart.so (art_quick_invoke_stub+756) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #73 pc 0000000000461715 /apex/com.android.art/lib64/libart.so (art::ArtMethod::Invoke(art::Thread*, unsigned int*, unsigned int, art::JValue*, char const*)+181) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #74 pc 00000000005f74b2 /apex/com.android.art/lib64/libart.so (bool art::interpreter::DoCall<false>(art::ArtMethod*, art::Thread*, art::ShadowFrame&, art::Instruction const*, unsigned short, bool, art::JValue*)+2402) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #75 pc 0000000000233611 /apex/com.android.art/lib64/libart.so (void art::interpreter::ExecuteSwitchImplCpp<false>(art::interpreter::SwitchImplContext*)+10625) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #76 pc 000000000022eb25 /apex/com.android.art/lib64/libart.so (ExecuteSwitchImplAsm+5) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #77 pc 000000000027f6ec /system/framework/framework.jar (offset 0x2430000) (com.android.internal.os.RuntimeInit$MethodAndArgsCaller.run+0) #78 pc 00000000005ef3f2 /apex/com.android.art/lib64/libart.so (art::interpreter::Execute(art::Thread*, art::CodeItemDataAccessor const&, art::ShadowFrame&, art::JValue, bool, bool) (.__uniq.112435418011751916792819755956732575238)+466) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #79 pc 00000000009978fb /apex/com.android.art/lib64/libart.so (artQuickToInterpreterBridge+907) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #80 pc 000000000022c99c /apex/com.android.art/lib64/libart.so (art_quick_to_interpreter_bridge+140) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #81 pc 00000000007fc865 /system/framework/x86_64/boot-framework.oat (com.android.internal.os.ZygoteInit.main+2693) (BuildId: 5539d75c58ae9a874c8bdd56a20d5ad37b7a2757) #82 pc 0000000000212136 /apex/com.android.art/lib64/libart.so (art_quick_invoke_static_stub+806) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #83 pc 000000000046175c /apex/com.android.art/lib64/libart.so (art::ArtMethod::Invoke(art::Thread*, unsigned int*, unsigned int, art::JValue*, char const*)+252) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #84 pc 000000000088bc46 /apex/com.android.art/lib64/libart.so (art::JValue art::InvokeWithVarArgs<_jmethodID*>(art::ScopedObjectAccessAlreadyRunnable const&, _jobject*, _jmethodID*, __va_list_tag*)+518) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #85 pc 000000000072f31a /apex/com.android.art/lib64/libart.so (art::JNI<true>::CallStaticVoidMethodV(_JNIEnv*, _jclass*, _jmethodID*, __va_list_tag*)+602) (BuildId: 53a0485d03b4e7011d7d555e293a17af) #86 pc 00000000000eefae /system/lib64/libandroid_runtime.so (_JNIEnv::CallStaticVoidMethod(_jclass*, _jmethodID*, ...)+126) (BuildId: 0e1c658a58fcbbaafa02d3d78a368ee8) #87 pc 000000000011b496 /system/lib64/libandroid_runtime.so (android::AndroidRuntime::start(char const*, android::Vectorandroid::String8 const&, bool)+966) (BuildId: 0e1c658a58fcbbaafa02d3d78a368ee8) #88 pc 0000000000004708 /system/bin/app_process64 (main+1640) (BuildId: e2134dffba60b5f40425e9c838419a02) #89 pc 0000000000054ea5 /apex/com.android.runtime/lib64/bionic/libc.so (__libc_init+117) (BuildId: 2fa253bed5a3b3aee8fce00edb55a139) Lost connection to device.

      These are the first layers of the .param file and the last ones.

      7767517 275 327 Input in0 0 1 in0 Convolution conv_0 1 1 in0 1 0=32 1=3 11=3 12=1 13=2 14=1 2=1 3=2 4=1 5=1 6=864 Swish silu_84 1 1 1 2 Convolution conv_1 1 1 2 3 0=64 1=3 11=3 12=1 13=2 14=1 2=1 3=2 4=1 5=1 6=18432 Swish silu_85 1 1 3 4 Convolution conv_2 1 1 4 5 0=64 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=4096

      Last layer

      Split splitncnn_27 1 2 313 314 315 BinaryOp add_16 2 1 310 312 316 0=0 Split splitncnn_28 1 2 316 317 318 BinaryOp add_17 2 1 314 317 319 0=0 BinaryOp div_18 1 1 319 320 0=3 1=1 2=2.000000e+00 BinaryOp sub_19 2 1 318 315 321 0=1 Concat cat_21 2 1 320 321 322 0=0 Reshape reshape_167 1 1 252 323 0=8400 1=1 BinaryOp mul_20 2 1 322 323 324 0=2 Sigmoid sigmoid_163 1 1 303 325 Concat cat_22 2 1 324 325 out0 0=0

      I thank you in advance for your attention and help.

  5. UltralyticsAssistantGitHubUltralytics

    šŸ‘‹ Hello, thank you for your interest in Ultralytics šŸš€! It's exciting to see discussions around optimizing YOLO models for mobile and embedded devices. The link you shared regarding NCNN integration is a great starting point for exploring this topic: https://docs.ultralytics.com/zh/integrations/ncnn/.

    If you're encountering issues or have specific queries regarding the NCNN export or deployment, here are a few recommendations:

    1. If this is a šŸ› Bug Report, please provide a minimum reproducible example so our team can better understand and debug any potential issues.

    2. If this is an ā“ integration or optimization question, please provide as much context as possible, such as your model configuration, device specifications, and any logs or error messages.

    For further learning and exploration, you may also find these resources helpful:

    • Integration Examples: Comprehensive guides for integrating YOLO with various platforms and tools.
    • NCNN Documentation: Specific details about NCNN and its benefits for resource-constrained environments.

    Upgrade

    Ensure you're using the latest Ultralytics package and dependencies by upgrading your environment with the following command:

    pip install -U ultralytics

    Environments

    To experiment with YOLO in diverse, GPU-enabled environments, check out the following setups:

    Status

    Ultralytics CI

    This badge indicates the status of Ultralytics CI tests, which ensure the reliability of YOLO models across various environments. A green badge means all tests are successful āœ….

    Lastly, this is an automated response to guide you as quickly as possible, but rest assured that an Ultralytics engineer will follow up soon to assist you further. Feel free to share additional information or questions in the meantime šŸ˜Š.

    1. haitao5676

      I have created a single target tracking model for deployment on Android devices. How can I export the ncnn file for deployment using my own model?

    2. haitao5676

      I have resolved the issue of using the model trained by ncnn in Python and am now preparing to deploy Java references for Android. Excuse me!

    1. Glenn JocherUltralytics

      @xiao5676wen to export your trained YOLO11 .pt model to NCNN format, load your model using the YOLO class in Python and then call the export method with format="ncnn". Example:

      from ultralytics import YOLO

model = YOLO("path_to_your_trained_model.pt")
model.export(format="ncnn")  # Creates the NCNN model

      For additional details, refer to the NCNN export documentation.

  7. trungnb34GitHub

    Hi everyone NCNN can be inferred from the int8 model I have a question. when converting Yolo model to int8 . Can ultralytics support it and Does the accuracy decrease much? Thanks

    1. Glenn JocherUltralytics(edited)

      @trungnb34 Ultralytics can export YOLO models to NCNN in FP16 with quantize=16, but INT8 quantization is not currently supported for NCNN exports (quantize=8 is rejected for this format). For details, refer to the Export to NCNN guide.

    2. robert-hardmanGitHub

      Hello ultralytics==8.3.53 Like it doesn't support int8 yet

      File "C:\Users***\AppData\Local\Programs\Python\Python312\Lib\site-packages\ultralytics\engine\exporter.py", line 253, in call validate_args(fmt, self.args, fmts_dict["Arguments"][flags.index(True) + 1]) File "C:\Users***AppData\Local\Programs\Python\Python312\Lib\site-packages\ultralytics\engine\exporter.py", line 144, in validate_args assert arg in valid_args, f"ERROR \u274c\ufe0f argument '{arg}' is not supported for format='{format}'" ^^^^^^^^^^^^^^^^^ AssertionError: ERROR \u274c\ufe0f argument 'int8' is not supported for format='ncnn'

    3. Glenn JocherUltralytics

      @robert-hardman thank you for raising this issue. Currently, INT8 quantization is not supported for NCNN exports in Ultralytics. For updates on supported features, keep an eye on our Export documentation. Let us know if you have further questions!

      1
  8. amrboef66GitHub

    Hellooo, im trying to run interference on a raspberry Pi 5, now indeed NCNN has the fastest time, but i need it to be even quicker, so i tried reducing the image format from 640 to 480, since my model does not require such a large image size, but when i do that it misidentifies and puts bounding boxes everywhere on screen. How do i reduce image size while still allowing the model to run accurately?

    Also, do you have any thoughts on how i can make it quicker, perhaps a different method? I am already using Nano model and int8 quantisation.

    Thank you so much!

    1. Glenn JocherUltralytics

      @amrboef66 to improve accuracy with a smaller image size, consider fine-tuning your model on images of the desired size. Additionally, you might explore using a more efficient model architecture or further optimizing your inference pipeline.

  9. abdullah000123GitHub

    Can we directly export model in ncnn while training or we only can convert model which i obtained after training ie converting from pt to ncnn if we can convert directly while training whats is benefit in case of accuracy and also give me code if we can export directly while training model

    1. Paula DerrengerUltralytics

      @abdullah000123 you can only export a YOLOv8 model to NCNN format after training is complete. Direct export during training is not supported. Exporting post-training ensures that the model is fully trained and optimized before conversion, which does not impact accuracy. For exporting, you can use the following command after training:

      from ultralytics import YOLO
model = YOLO("yolov8n.pt")
model.export(format="ncnn")

      For more details, please refer to the NCNN export guide.

  10. woerstcaseGitHub

    Hey there!

    I tried and on my Mac M2 Mac the inference time got from around 60ms to 30ms. And in a custom Model even faster.

    But now i have the "problem" that the BBoxes are very flickering. Is there a good technique to make the bbox drawing even more smooth. I tried some approaches like drawing over 5 Frames for e.g. But if i have same classes on the image the bbox starts wandering trough the image.

    best regards

    1. Glenn JocherUltralytics

      Hey there! šŸ‘‹

      Great to hear about the speed improvements on your Mac M2! For smoothing out the flickering BBoxes, one approach is to apply a moving average or some form of temporal smoothing to the bounding box coordinates across frames. This helps in making the position more stable. A simple example could be:

      # Assuming bbox is a list of bounding box coordinates for the current frame
# prev_bboxes is a list of previous bounding boxes (could store N previous states)

smoothed_bbox = current_bbox * alpha + prev_bbox * (1 - alpha)

      alpha is a weighting factor that determines the balance between the current and previous frame's information (e.g., alpha=0.6). This approach assumes prev_bbox is the bounding box coordinate from the previous frame. Adjust alpha based on your preference for smoothness vs. responsiveness.

      If you're handling multiple objects of the same class, consider incorporating an object tracker that assigns unique IDs to each object. This way, you can apply the smoothing individually to each tracked object, preventing the "wandering" effect across objects of the same class.

      Hope this helps! Let us know if you have any more questions. šŸ˜Š

      Best regards

      1
    2. woerstcaseGitHub

      Worked for me! With my custom Model the inference time is around 15ms!

      This is my smoothing function:

      #Smoothing function def smooth_bbox(current_bbox, prev_bbox): if prev_bbox is None: return current_bbox smoothed_bbox = [(c * alpha + p * (1 - alpha)) for c, p in zip(current_bbox, prev_bbox)] return smoothed_bbox

      ....#later in the code i use it like this

          for box, confidence, cls in zip(boxes, confidences, classes):
        x1, y1, x2, y2 = map(int, box[:4])  # Extract bounding box coordinates
        class_name = names[int(cls)]
        
        # Generate a unique key for each bbox assuming no tracking ID available
        bbox_key = f"{x1}_{y1}_{x2}_{y2}"
        
        # Apply smoothing
        current_bbox = [x1, y1, x2, y2]
        if bbox_key in prev_bboxes:
            smoothed_coords = smooth_bbox(current_bbox, prev_bboxes[bbox_key])
        else:
            smoothed_coords = current_bbox
        
        # Update prev_bboxes
        prev_bboxes[bbox_key] = smoothed_coords
        
        # Calculate the center of the smoothed bbox
        center_x = int((smoothed_coords[0] + smoothed_coords[2]) / 2)
        center_y = int((smoothed_coords[1] + smoothed_coords[3]) / 2)

      best regards

    3. Glenn JocherUltralytics

      Hey there!

      Great to see your impressive inference times, and thanks for sharing your smoothing function! šŸš€ It looks like a solid approach. To address the flickering and "wandering" of bounding boxes for objects of the same class, you might consider integrating an object tracking algorithm like SORT or DeepSORT that assigns unique IDs to each object. This way, you can ensure that the smoothing you apply is object-specific.

      Here's a small tweak to include object tracking by simply creating a unique key based on the object's class and center coordinates, assuming you'd implement tracking to assign IDs:

      # Assuming bbox_key generation after object tracking assigns a unique track_id
bbox_key = f"{class_name}_{track_id}"

# Use this key for previous bbox retrieval and smoothing application

      This tweak can help keep the smoothing consistent across consecutive frames for the same object, reducing "wandering."

      Hope this helps make your bboxes smooth as silk! Let me know if you have other questions or if there's more we can help with.

      Happy coding! šŸ˜Š

      Best regards