YOLOv5s Models
Overview
YOLOv5s is a variant of the YOLO (You Only Look Once) family of object detection models, specifically designed to be a smaller and faster version suitable for real-time object detection tasks. Developed by Ultralytics, YOLOv5 is the latest iteration in the YOLO series, offering improved speed and accuracy compared to previous versions.
Getting Started & How it work ?
To see detailed steps on how to use the model converter you can go to MTK Converter ,it will direct you to the appropriate page
Model Details
General Information
Property |
Value |
|---|---|
Category |
Detection |
Input Size |
640x640 |
FLOPs@640 (B) |
16.5 |
#Params (M) |
7.2 |
Training Framework |
PyTorch |
Inference Framework |
TFLite |
Quant8 Model package |
|
Float32 Model package |
Model Properties
Quant8
Format: TensorFlow Lite v3
Description: Exported by NeuroPilot converter v7.14.1+release
Inputs
Property |
Value |
Name |
x.1 |
Tensor |
int8[1,3,640,640] |
Identifier |
67 |
Quantization |
Linear |
Quantization Range |
0.0039 * (q + 128) ≤ 0.9993 |
Outputs
Property |
Value |
Name |
77 |
Tensor |
int8[1,255,80,80] |
Identifier |
315 |
Quantization |
Linear |
Quantization Range |
-19.3298 ≤ 0.0966 * (q - 72) ≤ 5.3157 |
Name |
78 |
Tensor |
int8[1,255,40,40] |
Identifier |
279 |
Quantization |
Linear |
Quantization Range |
-15.8150 ≤ 0.0841 * (q - 60) ≤ 5.6362 |
Name |
79 |
Tensor |
int8[1,255,20,20] |
Identifier |
15 |
Quantization |
Linear |
Quantization Range |
-15.7213 ≤ 0.0845 * (q - 58) ≤ 5.8321 |
Fp32
Format: TensorFlow Lite v3
Description: Exported by NeuroPilot converter v7.14.1+release
Inputs
Property |
Value |
Name |
x.1 |
Tensor |
float32[1,3,640,640] |
Identifier |
315 |
Outputs
Property |
Value |
Name |
77 |
Tensor |
float32[1,255,80,80] |
Identifier |
304 |
Name |
78 |
Tensor |
float32[1,255,40,40] |
Identifier |
272 |
Name |
79 |
Tensor |
float32[1,255,20,20] |
Identifier |
230 |
Performance Benchmarks
YOLOv5s-quant8
Run model (.tflite) 10 times |
CPU (Thread:8) |
GPU |
ARMNN(GpuAcc) |
ARMNN(CpuAcc) |
Neuron Stable Delegate(APU) |
APU(MDLA) |
APU(VPU) |
G350 |
669.998 ms (Thread:4) |
984.989 ms |
492.372 ms |
456.609 ms |
N/A |
N/A |
70487.4 ms |
G510 |
336.39 ms |
358.188 ms |
161.230 ms |
116.290 ms |
17.894 ms |
17.47 ms |
N/A |
G700 |
115.887 ms |
225.351 ms |
113.794 ms |
104.801 ms |
10.899 ms |
10.04 ms |
N/A |
G1200 |
116.143 ms |
150.983 ms |
72.639 ms |
58.181 ms |
19.238 ms |
19.05 ms |
N/A |
YOLOv5s-fp32
Run model (.tflite) 10 times |
CPU (Thread:8) |
GPU |
ARMNN(GpuAcc) |
ARMNN(CpuAcc) |
Neuron Stable Delegate(APU) |
APU(MDLA) |
APU(VPU) |
G350 |
1379.79 ms (Thread:4) |
935.716 ms |
957.083 ms |
N/A |
N/A |
N/A |
4775.23 ms |
G510 |
548.035 ms |
304.006 ms |
302.887 ms |
326.755 ms |
43.684 ms |
46.41 ms |
N/A |
G700 |
299.257 ms |
209.685 ms |
207.253 ms |
278.701 ms |
31.853 ms |
32.04 ms |
N/A |
G1200 |
272.845 ms |
136.244 ms |
133.026 ms |
158.299 ms |
36.771 ms |
36.66 ms |
N/A |
Widespread: CPU only, light workload.
Performance: CPU and GPU, medium workload.
Ultimate: CPU, GPU, and APUs, heavy workload.
Resources
To preview related documentation about YOLOv5, please visit the GitHub repository.