diff --git a/docs/en/benchmark.md b/docs/en/benchmark.md
index 0ac0164b9..d677a4976 100644
--- a/docs/en/benchmark.md
+++ b/docs/en/benchmark.md
@@ -179,29 +179,29 @@ Users can directly test the speed through [how_to_measure_performance_of_models.
| YOLOv3 |
COCO |
- 1x3x800x1344 |
- 94.08 |
- 10.63 |
- 24.90 |
- 40.17 |
- 24.87 |
- 40.21 |
- 47.64 |
- 20.99 |
+ 1x3x320x320 |
+ 14.76 |
+ 67.76 |
+ 24.92 |
+ 40.13 |
+ 24.92 |
+ 40.13 |
+ 18.07 |
+ 55.35 |
$MMDET_DIR/configs/yolo/yolov3_d53_320_273e_coco.py |
| SSD-Lite |
COCO |
- 1x3x800x1344 |
- 14.91 |
- 67.06 |
- 8.92 |
- 112.13 |
- 8.65 |
- 115.63 |
- 30.13 |
- 33.19 |
+ 1x3x320x320 |
+ 8.84 |
+ 113.12 |
+ 9.21 |
+ 108.56 |
+ 8.04 |
+ 124.38 |
+ 19.72 |
+ 50.71 |
$MMDET_DIR/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py |
diff --git a/docs/zh_cn/benchmark.md b/docs/zh_cn/benchmark.md
index dfbdf7a72..db8dd3aa8 100644
--- a/docs/zh_cn/benchmark.md
+++ b/docs/zh_cn/benchmark.md
@@ -34,6 +34,7 @@ GPU: TensorRT, PPLNN
| MMCls |
TensorRT |
PPLNN |
+ NCNN |
|
@@ -44,8 +45,10 @@ GPU: TensorRT, PPLNN
Input |
fp32 |
fp16 |
- in8 |
+ int8 |
fp16 |
+ SnapDragon888-fp32 |
+ Adreno660-fp32 |
model config file |
@@ -57,6 +60,10 @@ GPU: TensorRT, PPLNN
| FPS |
latency (ms) |
FPS |
+ latency (ms) |
+ FPS |
+ latency (ms) |
+ FPS |
| ResNet |
@@ -70,6 +77,10 @@ GPU: TensorRT, PPLNN
829.66 |
1.30 |
768.28 |
+ 33.91 |
+ 29.49 |
+ 25.93 |
+ 38.57 |
$MMCLS_DIR/configs/resnet/resnet50_b32x8_imagenet.py |
@@ -84,6 +95,10 @@ GPU: TensorRT, PPLNN
| 727.42 |
1.36 |
737.67 |
+ 133.44 |
+ 7.49 |
+ 69.38 |
+ 14.41 |
$MMCLS_DIR/configs/resnext/resnext50_32x4d_b32x8_imagenet.py |
@@ -98,6 +113,10 @@ GPU: TensorRT, PPLNN
| 662.90 |
1.91 |
524.07 |
+ 107.84 |
+ 9.27 |
+ 80.85 |
+ 12.37 |
$MMCLS_DIR/configs/seresnet/seresnet50_b32x8_imagenet.py |
@@ -112,6 +131,10 @@ GPU: TensorRT, PPLNN
| 883.47 |
4.69 |
213.33 |
+ 9.55 |
+ 104.71 |
+ 10.66 |
+ 93.81 |
$MMCLS_DIR/configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py |
@@ -139,7 +162,7 @@ GPU: TensorRT, PPLNN
Input |
fp32 |
fp16 |
- in8 |
+ int8 |
fp16 |
model config file |
@@ -156,29 +179,29 @@ GPU: TensorRT, PPLNN
| YOLOv3 |
COCO |
- 1x3x800x1344 |
- 94.08 |
- 10.63 |
- 24.90 |
- 40.17 |
- 24.87 |
- 40.21 |
- 47.64 |
- 20.99 |
+ 1x3x320x320 |
+ 14.76 |
+ 67.76 |
+ 24.92 |
+ 40.13 |
+ 24.92 |
+ 40.13 |
+ 18.07 |
+ 55.35 |
$MMDET_DIR/configs/yolo/yolov3_d53_320_273e_coco.py |
| SSD-Lite |
COCO |
- 1x3x800x1344 |
- 14.91 |
- 67.06 |
- 8.92 |
- 112.13 |
- 8.65 |
- 115.63 |
- 30.13 |
- 33.19 |
+ 1x3x320x320 |
+ 8.84 |
+ 113.12 |
+ 9.21 |
+ 108.56 |
+ 8.04 |
+ 124.38 |
+ 19.72 |
+ 50.71 |
$MMDET_DIR/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py |
@@ -253,6 +276,51 @@ GPU: TensorRT, PPLNN
+
+
+
+ | MMDet |
+ NCNN |
+ |
+
+
+
+
+ | Model |
+ Dataset |
+ Input |
+ SnapDragon888-fp32 |
+ Adreno660-fp32 |
+ model config file |
+
+
+ | latency (ms) |
+ FPS |
+ latency (ms) |
+ FPS |
+
+
+ | MobileNetv2-YOLOv3 |
+ COCO |
+ 1x3x320x320 |
+ 48.57 |
+ 20.59 |
+ 66.55 |
+ 15.03 |
+ $MMDET_DIR/configs/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco.py |
+
+
+ | SSD-Lite |
+ COCO |
+ 1x3x320x320 |
+ 44.91 |
+ 22.27 |
+ 66.19 |
+ 15.11 |
+ $MMDET_DIR/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py |
+
+
+
@@ -274,7 +342,7 @@ GPU: TensorRT, PPLNN
Input |
fp32 |
fp16 |
- in8 |
+ int8 |
fp16 |
model config file |
@@ -328,6 +396,7 @@ GPU: TensorRT, PPLNN
MMOCR |
TensorRT |
PPLNN |
+ NCNN |
|
@@ -338,8 +407,10 @@ GPU: TensorRT, PPLNN
Input |
fp32 |
fp16 |
- in8 |
+ int8 |
fp16 |
+ SnapDragon888-fp32 |
+ Adreno660-fp32 |
model config file |
@@ -351,105 +422,46 @@ GPU: TensorRT, PPLNN
| FPS |
latency (ms) |
FPS |
+ latency (ms) |
+ FPS |
+ latency (ms) |
+ FPS |
-
- | YOLOv3 |
- COCO |
- 1x3x800x1344 |
- 94.08 |
- 10.63 |
- 24.90 |
- 40.17 |
- 24.87 |
- 40.21 |
- 47.64 |
- 20.99 |
- $MMDET_DIR/configs/yolo/yolov3_d53_320_273e_coco.py |
-
-
- | SSD-Lite |
- COCO |
- 1x3x800x1344 |
- 14.91 |
- 67.06 |
- 8.92 |
- 112.13 |
- 8.65 |
- 115.63 |
- 30.13 |
- 33.19 |
- $MMDET_DIR/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py |
-
-
- | RetinaNet |
- COCO |
- 1x3x800x1344 |
- 97.09 |
- 10.30 |
- 25.79 |
- 38.78 |
- 16.88 |
- 59.23 |
- 38.34 |
- 26.08 |
- $MMDET_DIR/configs/retinanet/retinanet_r50_fpn_1x_coco.py |
-
-
- | FCOS |
- COCO |
- 1x3x800x1344 |
- 84.06 |
- 11.90 |
- 23.15 |
- 43.20 |
- 17.68 |
- 56.57 |
+
+ | DBNet |
+ ICDAR2015 |
+ 1x3x640x640 |
+ 10.70 |
+ 93.43 |
+ 5.62 |
+ 177.78 |
+ 5.00 |
+ 199.85 |
+ 34.84 |
+ 28.70 |
- |
- |
- $MMDET_DIR/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py |
-
-
- | FSAF |
- COCO |
- 1x3x800x1344 |
- 82.96 |
- 12.05 |
- 21.02 |
- 47.58 |
- 13.50 |
- 74.08 |
- 30.41 |
- 32.89 |
- $MMDET_DIR/configs/fsaf/fsaf_r50_fpn_1x_coco.py |
-
-
- | Faster-RCNN |
- COCO |
- 1x3x800x1344 |
- 88.08 |
- 11.35 |
- 26.52 |
- 37.70 |
- 19.14 |
- 52.23 |
- 65.40 |
- 15.29 |
- $MMDET_DIR/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py |
-
-
- | Mask-RCNN |
- COCO |
- 1x3x800x1344 |
- 320.86 |
- 3.12 |
- 241.32 |
- 4.14 |
- |
- |
- 86.80 |
- 11.52 |
- $MMDET_DIR/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py |
+ $MMOCR_DIR/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py |
+
+ | CRNN |
+ IIIT5K |
+ 1x1x32x32 |
+ 1.93 |
+ 518.28 |
+ 1.40 |
+ 713.88 |
+ 1.36 |
+ 736.79 |
+ - |
+ - |
+ 10.57 |
+ 94.64 |
+ 20.00 |
+ 50.00 |
+ $MMOCR_DIR/configs/textrecog/crnn/crnn_academic_dataset.py |
@@ -474,7 +486,7 @@ GPU: TensorRT, PPLNN
Input |
fp32 |
fp16 |
- in8 |
+ int8 |
fp16 |
model config file |