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Fix typos (#107)

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Signed-off-by: micaela <micaelaverucchi@gmail.com>
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micaela
2020-09-11 09:13:59 +02:00
parent 38106a9495
commit fa2b3d26cb
19 changed files with 50 additions and 50 deletions
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README.md
+7 -7
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@@ -15,7 +15,7 @@ M. Verucchi, G. Brilli, D. Sapienza, M. Verasani, M. Arena, F. Gatti, A. Capoton
```
## FPS Results
Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimesion as the input size, on
Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimension as the input size, on
* RTX 2080Ti (CUDA 10.2, TensorRT 7.0.0, Cudnn 7.6.5);
* Xavier AGX, Jetpack 4.3 (CUDA 10.0, CUDNN 7.6.3, tensorrt 6.0.1 );
* Tx2, Jetpack 4.2 (CUDA 10.0, CUDNN 7.3.1, tensorrt 5.0.6 );
@@ -169,7 +169,7 @@ tkDNN implement and easy parser for darknet cfg files, a network can be converte
tk::dnn::Network *net = tk::dnn::darknetParser("yolov4.cfg", "yolov4/layers", "coco.names");
net->print();
```
All models from darknet are now parsed directly from cfg, you still need to export the weights with the descripted tools in the previus section.
All models from darknet are now parsed directly from cfg, you still need to export the weights with the described tools in the previous section.
<details>
<summary>Supported layers</summary>
convolutional
@@ -203,7 +203,7 @@ cmake .. -DDEBUG=True
make
```
Once you have succesfully created your rt file, run the demo:
Once you have successfully created your rt file, run the demo:
```
./demo yolo4_fp32.rt ../demo/yolo_test.mp4 y
```
@@ -247,7 +247,7 @@ You should provide image_list.txt and label_list.txt, using training images. How
```
bash scripts/download_validation.sh COCO
```
to automatically download COCO2017 validation (inside demo folder) and create those needed file. Use BDD insted of COCO to download BDD validation.
to automatically download COCO2017 validation (inside demo folder) and create those needed file. Use BDD instead of COCO to download BDD validation.
Then a complete example using yolo3 and COCO dataset would be:
```
@@ -269,8 +269,8 @@ N.B.
export TKDNN_BATCHSIZE=2
# build tensorRT files
```
This will create a TensorRT file with the desidered **max** batch size.
The test will still run with a batch of 1, but the created tensorRT can manage the desidered batch size.
This will create a TensorRT file with the desired **max** batch size.
The test will still run with a batch of 1, but the created tensorRT can manage the desired batch size.
### Test batch Inference
This will test the network with random input and check if the output of each batch is the same.
@@ -316,7 +316,7 @@ cd build
./map_demo dla34_cnet_FP32.rt c ../demo/COCO_val2017/all_labels.txt ../demo/config.yaml
```
This demo also creates a json file named ```net_name_COCO_res.json``` containing all the detections computed. The detections are in COCO format, the correct format to subit the results to [CodaLab COCO detection challenge](https://competitions.codalab.org/competitions/20794#participate).
This demo also creates a json file named ```net_name_COCO_res.json``` containing all the detections computed. The detections are in COCO format, the correct format to submit the results to [CodaLab COCO detection challenge](https://competitions.codalab.org/competitions/20794#participate).
## Existing tests and supported networks