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.

Uebergabe.md

@@ -0,0 +1,41 @@
+# Übergabe Hailo
+
+* Raphael Maehnle
+* Thomas Hamböck
+
+## Agenda
+
+1. CVAT: Login, Data Import, Labeling, Data export
+   1. Create a new Project; Important: Labels eintragen, möglichst in konsistenter Reihenfolge (zu anderen Projekten)
+      1. Früher gab es nur Tasks => da war Labels konsistent halten aufwändiger; Anm.: Projects funktionieren zZ noch nicht stabil
+      2. Anm.: Früher gab es ein Speicherproblem; Bilder blieben im RAM => häufigeres Seite neu laden sinnvoll
+   2. Jobs: Aufteilung von Bildern auf annotierende Leute (User Assignment)
+   3. Bilder annotieren: Tastenkürzel ("n" > neue Bounding Box, ...): https://openvinotoolkit.github.io/cvat/docs/manual/advanced/shortcuts/
+   4. "..." > export as dataset > Yolo1.1
+2. hailo setup for development (training)
+   1. prerequisite: nvidia-docker
+   2. clone hailo model zoo: https://github.com/hailo-ai/hailo_model_zoo
+   3. download & run docker container "Hailo Software Suite (for Ubuntu 20.04)"
+      1. note: volume mount is missing in the setup guide => will be required for importing data
+3. Retraining Yolov5m
+   1. Tutorial for yolov5 training
+      https://github.com/hailo-ai/hailo_model_zoo/blob/master/docs/RETRAIN_ON_CUSTOM_DATASET.md
+   2. dataset meta setup:
+      http://192.168.0.63:8930/raphael.maenle/hailo_inference/-/blob/main/dataset/dataset/dataset.yaml
+      labels aus CVAT nochmal in die liste übertragen, nc == len(names)
+   3. from readme:  `docker run -it --gpus all -ipc=host -v /path/to/dataset/:/dataset yolov5:v0`
+      based on https://github.com/hailo-ai/hailo_model_zoo/blob/master/training/yolov5/Dockerfile
+   4. https://github.com/hailo-ai/hailo_model_zoo/blob/master/docs/RETRAIN_ON_CUSTOM_DATASET.md?plain=1#L183
+      epochs 3 ist etwas wenig; ~100 wäre besser
+      `--data` => auf dataset
+   5. Export to ONNX: https://github.com/hailo-ai/hailo_model_zoo/blob/master/docs/RETRAIN_ON_CUSTOM_DATASET.md?plain=1#L192
+      For visualisation: ONNX viewer, e.g.: https://github.com/lutzroeder/Netron
+4. Yolov5 hailo quantisation + compile
+   1. `hailo_sw_suite_docker_run.sh` => add `-v` as `DOCKER_ARGS` for volume mount in script
+   2. network config: `yolov5m.yaml`
+   3. do `compile` => includes `quantize`
+      `.hef` => network bitstream
+5. hailo setup for deployment (inference)
+   1. `HailoRT` => setup für runtime
+6. Inference
+