The model is updated to v2.

The source code was re-written. The computational cost is doubled. But the speed is almost the same with the previous one because int8 convolutional operation is carried out by AVX2. But NEON support is not finished.

CMakeLists.txt

@@ -4,7 +4,6 @@ project(facedetection)
 
 cmake_minimum_required(VERSION 2.8)
 
-option(ENABLE_INT8 "use int8" OFF)
 option(ENABLE_AVX2 "use avx2" OFF)
 option(ENABLE_NEON "whether use neon, if use arm please set it on" OFF)
 option(DEMO "build the demo" OFF)
@@ -20,11 +19,6 @@ SET(fdt_lib_shared ${fdt_lib_name}_shared)
 FILE(GLOB_RECURSE fdt_source_files ${fdt_src_dir}/*.cpp)
 LIST(SORT         fdt_source_files)
 
-if(ENABLE_INT8)
-	message("using int8")
-	add_definitions(-D_ENABLE_INT8)
-endif()
-
 if(ENABLE_AVX2)
 	message("using avx2")
 	add_definitions(-D_ENABLE_AVX2)

ChangeLog

@@ -1,3 +1,8 @@
+2019-09-14
+---------------------
+* Update the model to v2. The computational cost is doubled. But the speed is almost the same with the previous one because int8 convolutional operation is carried out by AVX2.
+* NEON support is not finished.
+
 2019-03-13
 ---------------------
 * Release the source code and the model files. Removed the binary libary. 

README.md

@@ -60,23 +60,25 @@ make
 
 ## CNN-based Face Detection on Windows
 
+
 | Method             |Time          | FPS         |Time          | FPS         |
 |--------------------|--------------|-------------|--------------|-------------|
 |                    |  X64         |X64          |  X64         |X64          |
 |                    |Single-thread |Single-thread|Multi-thread  |Multi-thread |
 |OpenCV Haar+AdaBoost (640x480)|   --         | --          | 12.33ms      |   81.1      |
-|cnn (CPU, 640x480)  |  64.21ms     | 15.57       | 15.59ms      |   64.16     |
-|cnn (CPU, 320x240)  |  15.23ms     | 65.68       |  3.99ms      |  250.40     |
-|cnn (CPU, 160x120)  |   3.47ms     | 288.08      |  0.95ms      | 1052.20     |
-|cnn (CPU, 128x96)   |   2.35ms     | 425.95      |  0.64ms      | 1562.10     |
+|cnn (CPU, 640x480)  |  64.55ms     | 15.49       | 15.78ms      |  63.36      |
+|cnn (CPU, 320x240)  |  15.48ms     | 64.60       |  3.92ms      |  255.01     |
+|cnn (CPU, 160x120)  |   3.86ms     | 259.01      |  1.07ms      |  938.71     |
+|cnn (CPU, 128x96)   |   2.46ms     | 406.33      |  0.68ms      | 1479.79     |
 
 * OpenCV Haar+AdaBoost runs with minimal face size 48x48
 * Face detection only, and no landmark detection included
-* Minimal face size ~12x12
+* Minimal face size ~10x10
 * Intel(R) Core(TM) i7-7700 CPU @ 3.6GHz
 
 ## CNN-based Face Detection on ARM Linux (Raspberry Pi 3 B+)
 
+(* to be updated)
 | Method             |Time          | FPS         |Time          | FPS         |
 |--------------------|--------------|-------------|--------------|-------------|
 |                    |Single-thread |Single-thread|Multi-thread  |Multi-thread |
@@ -86,7 +88,7 @@ make
 |cnn (CPU, 128x96)   |   17.78ms    | 56.24       |    6.12ms    | 163.50      |
 
 * Face detection only, and no landmark detection included.
-* Minimal face size ~12x12
+* Minimal face size ~10x10
 * Raspberry Pi 3 B+, Broadcom BCM2837B0, Cortex-A53 (ARMv8) 64-bit SoC @ 1.4GHz