PlantNetLibre-300K/README.md

# PlantNet-300K Forklet
This is a forklet of the PlantNet-300K training repository.

Related repository:

* https://spacecruft.org/deepcrayon/PlantNetLibre


# Install
Install thusly.

Using Debian Stable (12/Bookworm).


## Dependencies
Debian package dependencies.

```
sudo apt install build-essential libssl-dev zlib1g-dev libbz2-dev \
  libreadline-dev libsqlite3-dev curl libncursesw5-dev xz-utils \
  tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev \
  python3-virtualenv python3-pip cmake-curses-gui cmake \
  libudev-dev libsystemd-dev
```

## Python
The Python setup is a bit involved, but very flexible.
This setup will allow for any particular version of Python to be
installed and used.
This will also set up a virtual environment for `pip` installs.


Firstly, setup `pyenv` to install and use any version of Python
```
curl https://pyenv.run | bash
```

Add these lines to the `~/.bashrc`:

```
export PYENV_ROOT="$HOME/.pyenv"
command -v pyenv >/dev/null || export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)"
```

Then log out and log back in. The following command should work:

```
pyenv versions
```

Now install various version(s) of Python:

```
pyenv install 3.11
pyenv install 3.10
```


## PlantNetLibre-300K
Clone the repo and set up Python environment.
Flavor Python version to taste.

```
git clone https://spacecruft.org/deepcrayon/PlantNetLibre-300K
cd PlantNetLibre-300K/
pyenv local 3.11
virtualenv -p python3.11.4 env
source env/bin/activate
python --version
```

Install PyTorch and dependencies.
This is for NVidia CUDA.
Install `lit` first or install fails (just repeat install to fix).

```
pip install --upgrade pip
pip install lit
pip install torch torchvision torchaudio \
  --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
```

## Images
Set up data archive of photo images from PlantNet.
Archive is 31 gigabytes and contains the main
`images` `test`, `train`, and `val` directories.


Download images from upstream, or this `rsync`, *por ahora*.

```
sudo mkdir /srv/ml
sudo chown jebba:jebba /srv/ml
mkdir -p /srv/ml/plantnet/files/

rsync -utla rsync://66.135.9.139/plantnet/files/ \
  /srv/ml/plantnet/files/
```


## `nvtop`
It may be handy to have `top` for NVidia GPUs.
Works with A16 and A40, partially works with A100.

In new directory:

```
git clone https://github.com/Syllo/nvtop
cd nvtop/
mkdir build
cd build
cmake ..
ccmake ..
make -j4
# Run:
./src/nvtop
```

In the `ccmake` menu, disable all except NVidia.


# Run
Run thusly.


## Train Model
Train a model:

```
./train.sh [model name]
```

Example:

```
./train.sh resnet50
```


## Load Model
This repo is for training models, not using them.
But there is a script that can load each model to verify it at least
can load or not.

Edit source to test each model individually. Then run:

```
./load-model.py
```


# Docs
See the Libre Office spreadsheet `docs/train-plantnet.ods` for
info about each initial test run.


# Development
The `black` formatting application is used.
To install:

```
pip install -r requirements-dev.txt
```


# Upstream
Upstream source repository, BSD 2-Clause License.

* https://github.com/plantnet/PlantNet-300K

See `README-upstream.md` for upstream README.


# Copyright
Unofficial project, not related to PlantNet.
Upstream sources under their respective copyrights.

Source code license: BSD 2-Clause License.

Documentation license: Creative Commons CC by SA 4.0 International.

Data license: Creative Commons CC by SA 4.0 International.

*Copyright © 2023, Jeff Moe.*