Merge branch 'main' of ssh://192.168.0.63:2290/raphael.maenle/hailo_inference into main

README.md

@@ -18,7 +18,7 @@ For deployment
 - Ubuntu 18.04 or 20.4
 
 
-For taining
+For training
 - GPU enabled device (recommended)
 - [hailo.ai](www.hailo.ai) developer account
 - [docker](https://docs.docker.com/engine/install/ubuntu/)
@@ -58,6 +58,7 @@ Here you'll train, quantize, compile (on a gpu if possible) and infer (on the ha
         explains well on how to create that. 
    - There's a minimal example dataset in this repository under `/dataset` 
    - To mount this, use eg.: `docker run -it --gpus all -ipc=host -v /path/to/dataset/:/dataset yolov5:v0`
+   - `python train.py --img 640 --batch 16 --epochs 3 --data /dataset/dataset/dataset.yaml --weights yolov5m.pt --cfg models/yolov5m.yaml`
    - For training, make sure you target the correct `--model` and use the correct `--weights` (which are now conveniently already in the hailo docker)
    - once you've saved the best.pb onnx file, you can exit this docker container
 - once you are done with the steps 'training and exporting to ONNX', move on to the next step.
@@ -89,7 +90,7 @@ Further Notes and Examples:
 # Other Networks
 
 - For yolov5m, Hailo provides a configuration yaml which defines the quantization levels for the various networks. If you have a custom network you will have to define your custom yaml file. I liked using [netron](https://github.com/lutzroeder/Netron) to visualize the network for that. 
-- Anything the hailo\_model\_zoo provides a configuration file for (under `hailo_odel_zoo/cfg/networks/`) can be easily trained and deployed using the process
+- Anything the hailo\_model\_zoo provides a configuration file for (under `hailo_model_zoo/cfg/networks/`) can be easily trained and deployed using the process
   described above.
 - Also check out the [Hailo Tappas](https://hailo.ai/developer-zone/tappas-apps-toolkit/) which supply a variety of pre-trained _.hef_ files, which you can run
   without having to compile anything.