@@ -18,6 +18,32 @@ The resulting self-configuring method, nnDetection, adapts itself without any ma
We demonstrate the effectiveness of nnDetection on two public benchmarks, ADAM and LUNA16, and propose 10 further public data sets for a comprehensive evaluation of medical object detection methods.
# Installation
## Docker Installation
The easiest way to get started with nnDetection is the provided is to build a Docker Container with the provided Dockerfile.
Please install docker and [nvidia-docker2](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) before continuing.
All projects which are based on nnDetection assume that the base image was built with the following tagging scheme `nnDetection:[version]`.
To build a container (nnDetection Version 0.1) run the following command from the base directory:
(`--build-arg env_det_num_threads=6` and `--build-arg env_det_verbose=1` are optional and are used to overwrite the provided default parameters)
The docker container expects data and models in its own `/opt/data` and `/opt/models` directories respectively.
The directories need to be mounted via docker `-v`. For simplicity and speed, the ENV variables `det_data` and `det_models` can be set in the host system to point to the desired directories. To run:
```bash
docker run --gpus all -v${det_data}:/opt/data -v${det_models}:/opt/models -it--shm-size=24gb nndetection:0.1 /bin/bash
```
Warning:
When running a training inside the container it is necessary to [increase the shared memory](https://stackoverflow.com/questions/30210362/how-to-increase-the-size-of-the-dev-shm-in-docker-container)(via --shm-size).
## Source Installation
1. Install CUDA (>10.1) and cudnn (make sure to select [compatible versions](https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html)!)
2.[Optional] Depending on your GPU you might need to set `TORCH_CUDA_ARCH_LIST`, check [compute capabilities](https://developer.nvidia.com/cuda-gpus) here.
3. Install [torch](https://pytorch.org/)(make sure to match the pytorch and CUDA versions!) (requires pytorch >1.7+)
...
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@@ -52,34 +78,6 @@ To test the whole installation please run the Toy Dataset example.
To get the best possible performance we recommend using CUDA 11.0+ with cuDNN 8.1.X+ and a (!)locally compiled version(!) of Pytorch 1.7+
</details>
<detailsclose>
<summary>Docker Container</summary>
<br>
The provided Dockerfile can be used to setup quick development environments or deploy nnDetection.
Please install docker and [nvidia-docker2](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) before continuing.
All projects which are based on nnDetection assume that the base image was built with the following tagging scheme `nnDetection:[version]`.
To build a container (nnDetection Version 0.1) run the following command from the base directory:
(`--build-arg env_det_num_threads=6` and `--build-arg env_det_verbose=1` are optional and are used to overwrite the provided default parameters)
The docker container expects data and models in its own `/opt/data` and `/opt/models` directories respectively.
The directories need to be mounted via docker `-v`. For simplicity and speed, the ENV variables `det_data` and `det_models` can be set in the host system to point to the desired directories. To run:
```bash
docker run --gpus all -v${det_data}:/opt/data -v${det_models}:/opt/models -it--shm-size=24gb nndetection:0.1 /bin/bash
```
Warning:
When running a training inside the container it is necessary to [increase the shared memory](https://stackoverflow.com/questions/30210362/how-to-increase-the-size-of-the-dev-shm-in-docker-container)(via --shm-size).
@@ -134,11 +132,12 @@ After running the generation script follow the `Planning`, `Training` and `Infer
</div>
## Adding New Datasets
nnDetection relie on a standardized input format which is very similar to the [nnU-Net](https://github.com/MIC-DKFZ/nnUNet) format and allows easy integration of new datasets.
The format is explained below.
nnDetection relies on a standardized input format which is very similar to the [nnU-Net](https://github.com/MIC-DKFZ/nnUNet) format and allows easy integration of new datasets.
More details about the format can be found below.
### Folders
All datasets should reside inside `Task[Number]_[Name]` folder inside the specified detection data folder (et the path to this folder with the `det_data` environment flag).
To avoid conflicts with our provided pretrained models we recommend to use task numbers starting from 100.
An overview is provided below ([Name] symbolise folder, `-` symbolise files, indents refer to substructures)
After running all fold it is time to collect the models and creat a unified inference plan.
After running all folds it is time to collect the models and creat a unified inference plan.
The following command will copy all the models and predictions per fold and by adding the `sweep` options the empiricaly hyperparameter optimization across all fold can be started.
This will generate a unified plan for all models which will be used during inference.
