Accuracy Evaluation

Section

Overview

This page provides a comprehensive comparison of the accuracy of YOLOv5s models across various formats and conversion processes. It includes:

  • Validation metrics for the original PyTorch model, as well as the Quant8 and FP32 TFLite models converted with the open-source converter.

  • Evaluation results for the TFLite models converted using the NeuroPilot Converter tool.

  • Performance metrics for models evaluated on DLA devices, tested on the G700 platform.

Note

For better compatibility, it is recommended to use Python 3.7 when working with these models, as it has higher compatibility with certain libraries and frameworks.

This page provides an end-to-end example specifically for the YOLOv5s model. For additional information on other models, please visit the Model Hub for more details and resources.

../../../../../_images/sw_yocto_ml-guide_neuron-dev-flow_model-converter_acc-eval.png


Accuracy Comparison

YOLOv5s Model Accuracy Comparison

Model Validation Type

Pytorch Model (PC)

Int8 TFLite (Source, PC)

FP32 TFLite (Source, PC)

Int8 TFLite (MTK, PC)

FP32 TFLite (MTK, PC)

Int8 DLA (MTK, Device)

FP32 DLA (MTK, Device)

P (Precision)

0.709

0.723

0.669

0.659

0.669

0.633

0.667

R (Recall)

0.634

0.583

0.661

0.638

0.661

0.652

0.661

mAP@50

0.713

0.675

0.712

0.699

0.699

0.698

0.712

mAP@50-95

0.475

0.416

0.472

0.461

0.458

0.459

0.472

Note

  • Source: The TFLite model converted using an open source converter.

  • MTK: The TFLite model converted using the NeuroPilot Converter Tool.

  • PC: The model’s accuracy calculated on a PC.

  • Device: The model’s accuracy calculated on the G700 platform.

End-to-End Conversion Flow and Accuracy Evaluation

This section provides detailed steps and results for verifying the accuracy of YOLOv5s models in different formats and after various conversion processes.

Source Model Evaluation

Note

This evaluation is performed on a PC. Please ensure that the necessary dependencies and hardware requirements are met for successful execution.

  • PyTorch Model Evaluation:

    1. Get PyTorch source model

      git clone http://github.com/ultralytics/yolov5
cd yolov5
git reset --hard 485da42
pip install -r requirements.txt

    2. Evaluate the model

      python val.py --weights yolov5s.pt --data coco128.yaml --img 640

      Note

      Description of the parameters used in the above command:

      • --weights: Specifies the path to the PyTorch model weight file.

      • --data: Specifies the data configuration file.

      • --img: Specifies the input image size.

    3. Result

      Metric

      Value

      P (Precision)

      0.709

      R (Recall)

      0.634

      mAP@50(Mean Average Precision at IoU=0.50)

      0.713

      mAP@50-95(Mean Average Precision at IoU=0.50:0.95)

      0.475

      Note

      Description of the metrics in the result table:

      • P (Precision): The precision of the model, indicating the percentage of true positive predictions among all positive predictions.

      • R (Recall): The recall of the model, indicating the percentage of true positive predictions among all actual positives.

      • mAP@50 (Mean Average Precision at IoU=0.50): The mean average precision calculated at an Intersection over Union (IoU) threshold of 0.50.

      • mAP@50-95 (Mean Average Precision at IoU=0.50:0.95): The mean average precision calculated over multiple IoU thresholds ranging from 0.50 to 0.95.

TFLite Model Evaluation

Note

This evaluation is performed on a PC. Please ensure that the necessary dependencies and hardware requirements are met for successful execution.

From Open-Source Evaluation

  • INT8 Model:

    1. Export the model to TFLite with INT8 quantization:

      python export.py --weights yolov5s.pt --include tflite --int8

      Note

      Description of the parameters used in the export command:

      • --weights: Specifies the path to the PyTorch model weight file.

      • --include tflite Specifies that the model should be exported to TensorFlow Lite format.

      • --int8: Converts the model to INT8 quantized format; if not specified, the model will be converted to FP32 format by default.

    2. Validate Model:

      python val.py --weights yolov5s-int8.tflite --data coco128.yaml --img 640

    3. Result:

      Metric

      Value

      P (Precision)

      0.723

      R (Recall)

      0.583

      mAP@50(Mean Average Precision at IoU=0.50)

      0.675

      mAP@50-95(Mean Average Precision at IoU=0.50:0.95)

      0.416

  • FP32 Model:

    1. Download the required scripts:

      Before applying patch, download and extract the necessary scripts and patches:

      wget https://mediatek-aiot.s3.ap-southeast-1.amazonaws.com/aiot/download/model-zoo/scripts/model_conversion_YOLOv5s_example_20240916.zip
unzip -j model_conversion_YOLOv5s_example_20240916.zip

    2. Export the model to TFLite with FP32 precision:

      git apply export_fp32.patch
python export.py --weights yolov5s.pt --include tflite

      Note

      The export_fp32.patch modifies the export script to support exporting the model in FP32 (32-bit float) TFLite format instead of FP16 (16-bit float). The changes include:

      • Changing the output filename to indicate FP32 format.

      • Updating the supported types to use tf.float32 for higher precision.

    3. Validate Model:

      python val.py --weights yolov5s-fp32.tflite --data coco128.yaml --img 640

    4. Result:

      Metric

      Value

      P (Precision)

      0.669

      R (Recall)

      0.661

      mAP@50(Mean Average Precision at IoU=0.50)

      0.712

      mAP@50-95(Mean Average Precision at IoU=0.50:0.95)

      0.472

From NeuroPilot Converter Tool

  • INT8 Model:

    Before you begin, ensure that the NeuroPilot Converter Tool is installed. If you haven’t installed it yet, please follow the instructions in the “Install and Verify NeuroPilot Converter Tool” section of the same guide.

    1. Export and convert the model using the following commands:

      git apply Fix_yolov5_mtk_tflite_issue.patch
python export.py --weights yolov5s.pt --img-size 640 640 --include torchscript

    2. Prepare the calibration dataset:

      Run the following command to prepare the calibration data:

      python prepare_calibration_data.py

    3. Convert the model to INT8 TFLite format:

      After preparing the calibration data, convert the model to INT8 format:

      python convert_to_quant_tflite.py

      Note

      The Fix_yolov5_mtk_tflite_issue.patch adds support for MTK TensorFlow Lite (MTK TFLite) in the YOLOv5 model export script. It includes:

      • Adding mtk_tflite as a supported export format.

      • Modifying the Detect module’s forward method to only include convolution operations.

      • Implementing post-processing operations for MTK TFLite.

      • Extending the DetectMultiBackend class to handle MTK TFLite models.

    4. Validate The MTK INT8 TFLite Model:

      python val.py --weights yolov5s_int8_mtk.tflite --data coco128.yaml --img 640

    5. Result:

      Metric

      Value

      P (Precision)

      0.659

      R (Recall)

      0.638

      mAP@50(Mean Average Precision at IoU=0.50)

      0.699

      mAP@50-95(Mean Average Precision at IoU=0.50:0.95)

      0.461

  • FP32 Model:

    1. Export and convert the model:

      python export.py --weights yolov5s.pt --img-size 640 640 --include torchscript
python convert_to_tflite.py

    2. Validate the MTK FP32 TFLite model:

      python val.py --weights yolov5s_mtk.tflite --data coco128.yaml --img 640

    3. Result:

      Metric

      Value

      P (Precision)

      0.669

      R (Recall)

      0.661

      mAP@50(Mean Average Precision at IoU=0.50)

      0.699

      mAP@50-95(Mean Average Precision at IoU=0.50:0.95)

      0.458

DLA Model Evaluation

Note

This evaluation is performed on the G700 platform.

To ensure compatibility with your device, please download and use NeuroPilot SDK version 6. Other versions might not be fully supported

  • NeuroPilot SDK tools Download:

    1. Download NeuroPilot SDK All-In-One Bundle:

      Visit the download page: NeuroPilot Downloads

    2. Extract the Bundle:

      tar zxvf neuropilot-sdk-basic-<version>.tar.gz

    3. Setting Environment Variables:

      export LD_LIBRARY_PATH=/path/to/neuropilot-sdk-basic-<version>/neuron_sdk/host/lib

  • INT8 DLA Model:

    1. INT8 TFLite Model convert to DLA format:

      /path/to/neuropilot-sdk-basic-<version>/neuron_sdk/host/bin/ncc-tflite --arch=mdla3.0 yolov5s_int8_mtk.tflite

    2. Prepare and push the files to the device:

      python prepare_evaluation_dataset.py
adb shell mkdir /tmp/yolov5
adb shell mkdir /tmp/yolov5/device_outputs
adb push yolov5s_int8_mtk.dla /tmp/yolov5
adb push evaluation_dataset /tmp/yolov5
adb push run_device.sh /tmp/yolov5

    3. Run the evaluation on the device:

      adb shell
cd /tmp/yolov5
sh run_device.sh

    4. Pull the results and validate:

      adb pull /tmp/yolov5/device_outputs .
python val_int8_inference.py --weights yolov5s_int8_mtk.tflite

      Note

      The val_int8_inference.py specialized script for validating INT8 quantized TFLite models on MediaTek hardware, with dedicated handling for quantized inputs and outputs. It tightly integrates with MediaTek’s TFLite executor and includes dequantization processes.

    5. Result:

      Metric

      Value

      P (Precision)

      0.633

      R (Recall)

      0.652

      mAP@50(Mean Average Precision at IoU=0.50)

      0.698

      mAP@50-95(Mean Average Precision at IoU=0.50:0.95)

      0.459

  • FP32 DLA Model:

    1. Setting Environment Variables:

      export LD_LIBRARY_PATH=/path/to/neuropilot-sdk-basic-<version>/neuron_sdk/host/lib

    2. INT8 TFLite Model convert to DLA format:

      /path/to/neuropilot-sdk-basic-<version>/neuron_sdk/host/bin/ncc-tflite --arch=mdla3.0 --relax-fp32 yolov5s_mtk.tflite

    3. Prepare and push the files to the device:

      python prepare_evaluation_dataset_fp32.py
adb shell mkdir /tmp/yolov5
adb shell mkdir /tmp/yolov5/device_outputs_fp32
adb push yolov5s_mtk.dla /tmp/yolov5
adb push evaluation_dataset_fp32 /tmp/yolov5
adb push run_device_for_fp32.sh /tmp/yolov5

    4. Run the evaluation on the device:

      adb shell
cd /tmp/yolov5
sh run_device_for_fp32.sh

    5. Pull the results and validate:

      adb pull /tmp/yolov5/device_outputs_fp32 .
python val_fp32_inference.py --weights yolov5s_mtk.tflite

      Note

      The val_fp32_inference.py specialized script for validating FP32 TFLite models on MediaTek hardware, featuring integration with MediaTek’s TFLite executor and support for binary input/output processing, with a specific focus on FP32 TFLite inference.

    6. Result:

      Metric

      Value

      P (Precision)

      0.667

      R (Recall)

      0.661

      mAP@50(Mean Average Precision at IoU=0.50)

      0.712

      mAP@50-95(Mean Average Precision at IoU=0.50:0.95)

      0.472

Trouble Shooting

Resolving PyTorch Version Compatibility

During the process of converting the model to TFLite format using the following command:

python convert_to_quant_tflite.py

You might encounter the following error:

RuntimeError: `PyTorchConverter` only supports 2.0.0 > torch >= 1.3.0. Detected an installation of version v2.4.0+cu121. Please install the supported version.

Cause: This error occurs because the installed PyTorch version is incompatible with the PyTorchConverter. The converter requires a PyTorch version between 1.3.0 and 2.0.0.

Solution: To resolve this issue, install a compatible version of PyTorch by running the following command:

pip3 install torch==1.9.0 torchvision==0.10.0

This ensures that the correct version of PyTorch is used for the conversion process.

Resolving NCC-TFLite Shared Library Error

During the process of converting the TFLite model to DLA format using the following command:

ncc-tflite --arch=mdla3.0 yolov5s_int8_mtk.tflite

You might encounter the following error:

../../neuropilot-sdk-basic-6.0.5-build20240103/neuron_sdk/host/bin/ncc-tflite: error while loading shared libraries: libtinfo.so.5: cannot open shared object file: No such file or directory

Cause: This error occurs because the libtinfo.so.5 library is missing from your system.

Solution: To resolve this issue, install the missing library by running the following command:

sudo apt-get install libtinfo5