YOLOv8s Models

Overview

YOLOv8s is a variant of the YOLOv8 (You Only Look Once version 8) family of object detection models, recognized for its advancements in speed, accuracy, and ease of use. Developed by Ultralytics, YOLOv8 represents the latest iteration in the YOLO series, building upon the successes of previous versions such as YOLOv4 and YOLOv5, with a focus on modern deep learning practices and integration with popular frameworks.

Model Conversion Flow

Precondition

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.

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.

Clone the YOLOv5 repository:

The export script needed for conversion is available in the YOLOv5 repository. Clone it using the following command:
```
git clone https://github.com/ultralytics/yolov5.git
cd yolov5
git reset --hard 485da42
```
Install Python packages and dependencies:
```
pip3 install -r requirements.txt
pip3 install torch==1.9.0 torchvision==0.10.0
```
Note

The mtk_converter.PyTorchConverter only supports PyTorch versions between 1.3.0 and 2.0.0. The detected version v2.3.1+cu121 is not within this supported range, causing a runtime error. Therefore, it is necessary to install a compatible version of PyTorch and torchvision to ensure compatibility.

Get Source Model

Download the YOLOv8s model:

Use the following wget command to download the YOLOv8s model into the YOLOv5 source code directory:
```
wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt
```
Export the PyTorch model to TorchScript:

Use the following command to convert the model from PyTorch format to TorchScript:
```
python3 export.py --weights yolov8s.pt --img-size 640 640 --include torchscript
```

Converting Model for Deployment

Quant8 Conversion Process

Convert to TFLite format:

The following script demonstrates how to convert the YOLOv8s model to a quantized TFLite format:

Data Generation: A generator function creates random input data for calibration.
Model Loading: The YOLOv8s model is loaded from a TorchScript file.
Quantization: The model is configured for quantization with specified input value ranges.
Conversion: The quantized model is converted to TFLite format and saved.

python3 convert_to_tflite_quantized.py

import mtk_converter
import numpy as np

def data_gen():
    for i in range(100):
        yield [np.random.randn(1, 3, 640, 640).astype(np.float32)]

converter = mtk_converter.PyTorchConverter.from_script_module_file(
    'yolov8s.torchscript',  [[1, 3, 640, 640]],
)
converter.quantize = True
converter.input_value_ranges = [(-1.0, 1.0)]
converter.calibration_data_gen = data_gen
_ = converter.convert_to_tflite(output_file='yolov8s_quant.tflite')

Convert to DLA format:

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
```
4. TFLite Model convert to DLA format:
  
  Use the NeuroPilot Converter Tool to convert your TFLite model into the DLA format. The following example shows how to convert an INT8 TFLite model to DLA format using the specified architecture (mdla3.0):
```
/path/to/neuropilot-sdk-basic-<version>/neuron_sdk/host/bin/ncc-tflite --arch=mdla3.0 yolov8s_quant.tflite
```

Note

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

FP32 Conversion Process

Convert to TFLite format:

The following script demonstrates how to convert the YOLOv8s model to a non-quantized (FP32) TFLite format:

Data Generation: Similar to the quantization process, a generator function creates random input data for conversion.
Model Loading: The YOLOv8s model is loaded from a TorchScript file.
Conversion: The model is converted to TFLite format without quantization and saved.

python3 convert_to_tflite.py

import mtk_converter
import numpy as np

def data_gen():
    for i in range(100):
        yield [np.random.randn(1, 3, 640, 640).astype(np.float32)]

converter = mtk_converter.PyTorchConverter.from_script_module_file(
    'yolov8s.torchscript',  [[1, 3, 640, 640]],
)
converter.input_value_ranges = [(-1.0, 1.0)]
converter.calibration_data_gen = data_gen
_ = converter.convert_to_tflite(output_file='yolov8s.tflite')

Convert to DLA format:

Setting Environment Variables:
export LD_LIBRARY_PATH=/path/to/neuropilot-sdk-basic-<version>/neuron_sdk/host/lib
TFLite Model convert to DLA format:

Use the NeuroPilot Converter Tool to convert your FP32 TFLite model into the DLA format. The following example shows how to convert an FP32 TFLite model to DLA format using the specified architecture (mdla3.0) and enabling relaxed FP32 operations:
/path/to/neuropilot-sdk-basic-<version>/neuron_sdk/host/bin/ncc-tflite --arch=mdla3.0 --relax-fp32 yolov8s.tflite

Model Information

Note

The models and benchmark data mentioned below have been processed using the mtk_converter.

General Information

The information in the table below is sourced from the Detection section of the ultralytics repository, which can be found at ultralytics repository.

Property	Value
Category	Detection
Input Size	640x640
FLOPs (B)	28.6
#Params (M)	11.2
Training Framework	PyTorch
Inference Framework	TFLite

Pre-converted Model

Deployable Model

Model Type	Download Link	Supported Backend
Quant8 Model package	Download Quant8	CPU,GPU,ARMNN,Neuron Stable Delegage,NeuronSDK
Float32 Model package	Download Fp32	CPU,GPU,ARMNN,Neuron Stable Delegage,NeuronSDK

Model Properties

YOLOv8s-quant8

Inputs

Property	Value
Name	input.49
Tensor	int8[1,3,640,640]
Identifier	35
Quantization	Linear
Quantization Range	-1.0039 ≤ 0.0078 * q ≤ 0.9961

Outputs

Property	Value
Name	80
Tensor	int8[1,84,8400]
Identifier	378
Quantization	Linear
Quantization Range	-10.1582 ≤ 2.5395 * (q + 124) ≤ 637.4246
Name	77
Tensor	int8[1,144,80,80]
Identifier	37
Quantization	Linear
Quantization Range	-18.2789 ≤ 0.1115 * (q - 36) ≤ 10.1426
Name	78
Tensor	int8[1,144,40,40]
Identifier	270
Quantization	Linear
Quantization Range	-17.3353 ≤ 0.1008 * (q - 44) ≤ 8.3653
Name	79
Tensor	int8[1,144,20,20]
Identifier	155
Quantization	Linear
Quantization Range	-23.8304 ≤ 0.1288 * (q - 57) ≤ 9.0169

YOLOv8s-fp32

Inputs

Property	Value
Name	input.49
Tensor	float32[1,3,640,640]
Identifier	145

Outputs

Property	Value
Name	80
Tensor	float32[1,84,8400]
Identifier	78
Name	77
Tensor	float32[1,144,80,80]
Identifier	235
Name	78
Tensor	float32[1,144,40,40]
Identifier	73
Name	79
Tensor	float32[1,144,20,20]
Identifier	343

Benchmark Results

Note

The benchmark results shown below were measured with performance mode enabled. These numbers are for reference only, as actual performance may vary depending on the hardware and platform used.

Please note the following limitations:

The G350 does not support Neuron Stable Delegate (APU) and APU (MDLA) because the hardware does not yet support these features.
Running models on the G350 using ARMNN inference may result in a crash due to the model size being too large for the platform to handle.

YOLOv8s-quant8

Run model (.tflite) 10 times	CPU (Thread:8)	GPU	ARMNN(GpuAcc)	ARMNN(CpuAcc)	Neuron Stable Delegate	NeuronSDK
G350	1030.54 ms (Thread:4)	1306.24 ms	752.383 ms	730.644 ms	N/A	N/A
G510	360.921 ms	437.340 ms	232.064 ms	178.786 ms	29.564 ms	25.51 ms
G700	169.113 ms	301.178 ms	162.038 ms	160.934 ms	27.336 ms	17.01 ms
G1200	170.408 ms	207.360 ms	104.634 ms	88.477 ms	29.590 ms	28.04 ms

YOLOv8s-fp32

Run model (.tflite) 10 times	CPU (Thread:8)	GPU	ARMNN(GpuAcc)	ARMNN(CpuAcc)	Neuron Stable Delegate	NeuronSDK
G350	2191.58 ms (Thread:4)	1273.35 ms	1145.45 ms	N/A	N/A	N/A
G510	832.005 ms	410.635 ms	373.830 ms	428.679 ms	68.295 ms	70.95 ms
G700	465.654 ms	284.420 ms	258.681 ms	361.914 ms	49.723 ms	50.04 ms
G1200	415.078 ms	190.626 ms	164.658 ms	209.437 ms	55.250 ms	55.84 ms

Run Benchmark Tools

This section will guide you on how to execute the benchmark tool with different delegates and hardware configurations.

First, push your TFLite model to the target device:

adb push <your_tflite_model> /usr/share/label_image/

Make sure to replace <your_tflite_model> with the actual path of your TFLite model.

Next, open an ADB shell to the target device:

adb shell

After this, you can execute the following commands directly from the shell.

Execute on CPU (8 threads)

To execute the benchmark on the CPU using 8 threads, run the following command:

benchmark_model --graph=/usr/share/label_image/<your_tflite_model> --num_threads=8 --num_runs=10

Execute on GPU, with GPU delegate

To execute the benchmark on the GPU using the TensorFlow Lite GPU delegate, run the following command:

benchmark_model --graph=/usr/share/label_image/<your_tflite_model> --use_gpu=1 --allow_fp16=0 --gpu_precision_loss_allowed=0 --num_runs=10

Execute on GPU, with Arm NN delegate

To execute the benchmark on the GPU using the Arm NN delegate, use the following command:

benchmark_model --graph=/usr/share/label_image/<your_tflite_model> --external_delegate_path=/usr/lib/libarmnnDelegate.so.29 --external_delegate_options="backends:GpuAcc" --num_runs=10

Execute on CPU, with Arm NN delegate

To run the benchmark on the CPU using the Arm NN delegate, use the following command:

benchmark_model --graph=/usr/share/label_image/<your_tflite_model> --external_delegate_path=/usr/lib/libarmnnDelegate.so.29 --external_delegate_options="backends:CpuAcc" --num_runs=10

Execute on APU, with Neuron Delegate

For executing on the APU using the Neuron delegate, run the following command:

benchmark_model --stable_delegate_settings_file=/usr/share/label_image/stable_delegate_settings.json --use_nnapi=false --use_xnnpack=false --use_gpu=false --min_secs=20 --graph=/usr/share/label_image/<your_tflite_model>

Note

If you are using the G350 platform, please make the following adjustments:

For CPU-based benchmarks, change the –num_threads parameter to 4:

benchmark_model --graph=/usr/share/label_image/<your_tflite_model> --num_threads=4 --use_xnnpack=0 --num_runs=10

For all benchmarks (CPU, GPU, Arm NN), add the parameter –use_xnnpack=0 to disable the XNNPACK delegate

Neuron SDK

Follow these steps to benchmark your TensorFlow Lite model using the Neuron SDK with MDLA 3.0:

Transfer the Model to the Device:

Use adb to push your TFLite model to the device:
```
adb push <your_tflite_model> /user/share/benchmark_dla/
```
Access the Device Shell:

Connect to your device’s shell:
```
adb shell
```
Navigate to the Benchmark Directory:

Change to the directory where the model is stored:
```
cd /user/share/benchmark_dla/
```

Run the Benchmark:

Execute the benchmarking script with the following command:

python3 benchmark.py --file <your_tflite_model> --target mdla3.0 --profile --options='--relax-fp32'

Description:

The benchmark.py script runs a performance evaluation on your model using MDLA 3.0.
The –file parameter specifies the path to your TFLite model.
The –target mdla3.0 option sets the target hardware to MDLA 3.0.
The –profile flag enables profiling to provide detailed performance metrics.
The –options=’–relax-fp32’ option allows relaxation of floating-point precision to improve compatibility with MDLA.