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https://github.com/lewes6369/TensorRT-Yolov3
TensorRT for Yolov3
https://github.com/lewes6369/TensorRT-Yolov3
caffe tensorrt yolov3
Last synced: 3 months ago
JSON representation
TensorRT for Yolov3
- Host: GitHub
- URL: https://github.com/lewes6369/TensorRT-Yolov3
- Owner: lewes6369
- License: mit
- Created: 2018-12-04T15:32:28.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-09-26T19:26:41.000Z (over 2 years ago)
- Last Synced: 2024-08-02T01:19:49.948Z (6 months ago)
- Topics: caffe, tensorrt, yolov3
- Language: C++
- Size: 72.3 KB
- Stars: 489
- Watchers: 19
- Forks: 165
- Open Issues: 42
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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- awesome-yolo-object-detection - lewes6369/TensorRT-Yolov3 - Yolov3?style=social"/> : TensorRT for Yolov3. (Lighter and Deployment Frameworks)
- awesome-yolo-object-detection - lewes6369/TensorRT-Yolov3 - Yolov3?style=social"/> : TensorRT for Yolov3. (Lighter and Deployment Frameworks)
- awesome-cuda-triton-hpc - lewes6369/TensorRT-Yolov3 - Yolov3?style=social"/> : TensorRT for Yolov3. (Applications)
- awesome-cuda-triton-hpc - lewes6369/TensorRT-Yolov3 - Yolov3?style=social"/> : TensorRT for Yolov3. (Applications)
README
# TRTForYolov3
## Desc
tensorRT for Yolov3
### Test Enviroments
Ubuntu 16.04
TensorRT 5.0.2.6/4.0.1.6
CUDA 9.2
### Models
Download the caffe model converted by official model:
+ Baidu Cloud [here](https://pan.baidu.com/s/1VBqEmUPN33XrAol3ScrVQA) pwd: gbue
+ Google Drive [here](https://drive.google.com/open?id=18OxNcRrDrCUmoAMgngJlhEglQ1Hqk_NJ)
If run model trained by yourself, comment the "upsample_param" blocks, and modify the prototxt the last layer as:
```
layer {
#the bottoms are the yolo input layers
bottom: "layer82-conv"
bottom: "layer94-conv"
bottom: "layer106-conv"
top: "yolo-det"
name: "yolo-det"
type: "Yolo"
}
```
It also needs to change the yolo configs in "YoloConfigs.h" if different kernels.
### Run Sample
```bash
#build source code
git submodule update --init --recursive
mkdir build
cd build && cmake .. && make && make install
cd ..
#for yolov3-608
./install/runYolov3 --caffemodel=./yolov3_608.caffemodel --prototxt=./yolov3_608.prototxt --input=./test.jpg --W=608 --H=608 --class=80
#for fp16
./install/runYolov3 --caffemodel=./yolov3_608.caffemodel --prototxt=./yolov3_608.prototxt --input=./test.jpg --W=608 --H=608 --class=80 --mode=fp16
#for int8 with calibration datasets
./install/runYolov3 --caffemodel=./yolov3_608.caffemodel --prototxt=./yolov3_608.prototxt --input=./test.jpg --W=608 --H=608 --class=80 --mode=int8 --calib=./calib_sample.txt
#for yolov3-416 (need to modify include/YoloConfigs for YoloKernel)
./install/runYolov3 --caffemodel=./yolov3_416.caffemodel --prototxt=./yolov3_416.prototxt --input=./test.jpg --W=416 --H=416 --class=80
```
### Performance
Model | GPU | Mode | Inference Time
-- | -- | -- | --
Yolov3-416 | GTX 1060 | Caffe | 54.593ms
Yolov3-416 | GTX 1060 | float32 | 23.817ms
Yolov3-416 | GTX 1060 | int8 | 11.921ms
Yolov3-608 | GTX 1060 | Caffe | 88.489ms
Yolov3-608 | GTX 1060 | float32 | 43.965ms
Yolov3-608 | GTX 1060 | int8 | 21.638ms
Yolov3-608 | GTX 1080 Ti | float32 | 19.353ms
Yolov3-608 | GTX 1080 Ti | int8 | 9.727ms
Yolov3-416 | GTX 1080 Ti | float32 | 9.677ms
Yolov3-416 | GTX 1080 Ti | int8 | 6.129ms | li
### Eval Result
run above models with appending ```--evallist=labels.txt```
int8 calibration data made from 200 pics selected in val2014 (see scripts dir)
Model | GPU | Mode | dataset | MAP(0.50) | MAP(0.75)
-- | -- | -- | -- | -- | --
Yolov3-416 | GTX 1060 | Caffe(fp32) | COCO val2014 | 50.33 | 33.00
Yolov3-416 | GTX 1060 | float32 | COCO val2014 | 50.27 | 32.98
Yolov3-416 | GTX 1060 | int8 | COCO val2014 | 44.15 | 30.24
Yolov3-608 | GTX 1060 | Caffe(fp32) | COCO val2014 | 52.89 | 35.31
Yolov3-608 | GTX 1060 | float32 | COCO val2014 | 52.84 | 35.26
Yolov3-608 | GTX 1060 | int8 | COCO val2014 | 48.55 | 35.53 | li
Notice:
+ caffe implementation is little different in yolo layer and nms, and it should be the similar result compared to tensorRT fp32.
### Details About Wrapper
see link [TensorRTWrapper](https://github.com/lewes6369/tensorRTWrapper)