Caffe_v1.1.2
- Features
-
INT8 inference
Inference speed improved with upgraded MKL-DNN library.
In-place concat for latency improvement with batch size 1. Scale unify for concat for better performance. Support added in calibration tool as well -
FP32 inference
Performance improved on detectionOutput layer with ~3X
Add MKL-DNN 3D convolution support -
Multi-node training
SSD-VGG16 multi-node training is supported -
New models
Support training of R-FCN object detection model
Support training of Yolo-V2 object detection model
Support inference of SSD-MobileNet object detection model
Added the SSD-VGG16 multi-node model that converges to SOTA -
Build improvement
Fixed compiler warnings using GCC7+ version -
Misc
MKLML upgraded to mklml_lnx_2019.0.20180710
MKL-DNN upgraded to v0.16+ (4e333787e0d66a1dca1218e99a891d493dbc8ef1)
- Known issues
- INT8 inference accuracy drop for convolutions with output channel 16-individable
- FP32 training cannot reach SOTA accuracy with Winograd convolution