diff --git a/README.md b/README.md index b826dbd..565fb5a 100644 --- a/README.md +++ b/README.md @@ -33,51 +33,63 @@ you would like to deploy. You'll need a way to train your network (yielding a .pb file from it) and the hailo software suite to generate the Hailo File from that. - - always download the newest version of the [hailo software suite](https://hailo.ai/developer-zone/sw-downloads) from the [hailo.ai software download zone](https://hailo.ai/developer-zone/sw-downloads/) +- always download the newest version of the [hailo software suite](https://hailo.ai/developer-zone/sw-downloads) from the [hailo.ai software download zone](https://hailo.ai/developer-zone/sw-downloads/) which are getting monthly updates at the time of writing. This of course means that these notes are rapidly aging into inaccuracy. - - clone the [hailo model zoo](https://github.com/hailo-ai/hailo_model_zoo), which has some convenient yolov5 +- clone the [hailo model zoo](https://github.com/hailo-ai/hailo_model_zoo), which has some convenient yolov5 Docker containers. - -### Setup - - - unzip the `hailo software suite` and run inside `./hailo_sw_suite_docker_run.sh`. If this is your fist time running, the container will set up. +- unzip the `hailo software suite` and run inside `./hailo_sw_suite_docker_run.sh`. If this is your fist time running, the container will set up. Otherwise, you'll have to either `--resume` or `--override` ## For your Device with a Hailo Chip attached - - downloads the newest hailo rt from the [hailo.ai software download zone](https://hailo.ai/developer-zone/sw-downloads/) - - extract the file and run the installer. Once you're done, reboot. - - Source the virtual environment under `/path/to/hailo_rt/Installer/hailo_platform_venv/bin/activate` - - test virtual environment by running `hailo` +- downloads the newest hailo rt from the [hailo.ai software download zone](https://hailo.ai/developer-zone/sw-downloads/) +- extract the file and run the installer. Once you're done, reboot. +- Source the virtual environment under `/path/to/hailo_rt/Installer/hailo_platform_venv/bin/activate` +- test virtual environment by running `hailo` # Setup Example for a custom Yolov5m +Here you'll train, quantize, compile (on a gpu if possible) and infer (on the hailo chip) ## Train your own Yolov5m - - For yolov5 we'll be using hailo Docker containers, which are based on the [ultralytics yolov5 containers](https://github.com/ultralytics/yolov5) - - hailo model zoo now has a [guide how to train yolov5 for hailo](https://github.com/hailo-ai/hailo_model_zoo/blob/master/docs/RETRAIN_ON_CUSTOM_DATASET.md) follow that one. Some notes: - - You'll need to create your own dataset structure for the training process. [This guide](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) +- For yolov5 we'll be using hailo Docker containers, which are based on the [ultralytics yolov5 containers](https://github.com/ultralytics/yolov5) +- hailo model zoo now has a [guide how to train yolov5 for hailo](https://github.com/hailo-ai/hailo_model_zoo/blob/master/docs/RETRAIN_ON_CUSTOM_DATASET.md) follow that one. Some notes: + - You'll need to create your own dataset structure for the training process. [This guide](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 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` - - 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. + - 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` + - 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. ## Create Hailo representation (hef) - - in `hailo_sw_suite_docker_run.sh` add a volume so you can access your best.pb onnx file, for example +- in `hailo_sw_suite_docker_run.sh` add a volume so you can access your best.pb onnx file, for example in the `DOCKER_ARGS` you could add -v /home/user/files:/files - - run `./hailo_sw_suite_docker_run.sh --resume` to get into the docker container - - follow the rest of the tutorial from [the hailo model zoo](https://github.com/hailo-ai/hailo_model_zoo/blob/master/docs/RETRAIN_ON_CUSTOM_DATASET.md) - - Note, that 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. +- run `./hailo_sw_suite_docker_run.sh --resume` to get into the docker container +- `cd hailo_model_zoo` and continue the tutorial. + +Further Notes and Examples: +- Another example to compile the model is to run: `python hailo_model_zoo/main.py compile yolov5m --ckpt /files/my_dataset/best.onnx --calib-path /files/watt_dataset/dataset/dataset/images/ --yaml ./hailo_model_zoo/cfg/networks/yolov5m.yaml` +- note that here you're quantizing and compiling to HEF file. If you just want to quantize into a _har_ file, run `python hailo_model_zoo/main.py quantize yolov5m --ckpt /files/my_dataset/best.onnx --calib-path /files/my_dataset/dataset/dataset/images/ --yaml ./hailo_model_zoo/cfg/networks/yolov5m.yaml`, which yields the _.har_ representation of the file + - You have to provide a list of images for quantization (--calib-path) + - also provide a quantization scheme (--yaml), where hailo\_model\_zoo provides a variety +- **There's no real reason to not use _compile_ directly from what I know.** +- Note that the file locations in these examples are different to Hailos. These worked, while Hailo's didn't. +- follow the getting started guide from [hailo model zoo / GETTING STARTED](https://github.com/hailo-ai/hailo_model_zoo/blob/master/docs/GETTING_STARTED.md), specifically the + _optimizing_ and _compile_ steps if you want more information on quantization parameters and how to go on from there. ## run inference +- this git repository includes a _inference.py_ file, which which loads a specified _.hef_ file. Change that to your _.hef_ file location. +- If you have anything other than yolov5m, you will have to change the pre/post-processing steps. Otherwise it won't work. Check out the Hailo Handler Class and go from there. # Other Networks -- what other networks can I deploy on Hailo? +- 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 + 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.