---
deeplabcut:
last_content_updated: '2025-02-28'
last_metadata_updated: '2026-03-06'
ignore: false
---
(convert-maDLC)=
# How to convert a pre-2.2 project for use with DeepLabCut 2.2 or later
If you have a pre-2.2 project (`labeled-data`) with a **single animal** that you want to use with a multianimal project
in DLC 2.2 or later, i.e. use your older data to now train the new multi-task deep neural network, here is what you
need to do.
(1) We recommend you make a back-up of your project folder.
(2) Open your `config.yaml` file (in any text editor, or python IDE such as PyCharm, Spyder, VScode, atom, etc).
- After `task, scorer, date, project_path` please add the following (i.e. in the image above, you would start adding
below line 6) Note, the ordering isn't important but useful to keep consistent with the template:
```python
multianimalproject: true
individuals:
uniquebodyparts: []
multianimalbodyparts:
identity: false/true
```
- Now, please name the animal you have a new name under individuals, i.e.:
```python
individuals:
- mouse1
```
- `"uniquebodyparts: []` can stay blank, unless you have other items labeled you want to estimate (consider these as
similar to bodyparts in pre-2.2); i.e. corners of a box, etc. All unique bodyparts should not be connected to the
multianimal bodyparts in the skeleton you will eventually make. See "advanced option" below.
- Please move your "bodyparts:" to "multianimalbodyparts:" (bodypart names must stay the same!) These are the parts
that will always be interconnected fully!
```python
multianimalbodyparts:
- snout
- leftear
- rightear
- tailbase
```
then you can set `bodyparts: MULTI!`
(3) Save the config.yaml (be sure to double check for spacing or typos first!) and then run:
```python
deeplabcut.convert2_maDLC(path_config_file, userfeedback=True)
```
Now you will see that your data within `labeled-data` are converted to a new format, and the single animal format was
saved for you under a new file named `CollectedData_ ...singleanimal.h5` and `.csv` as a back-up!
(4) We strongly recommend to first run check_labels and verify that the conversion was as expected before creating a
multianimal training dataset. For instance, you can load this project `config.yaml` in the Project Manager GUI and
check labels then create a multi-animal training set with
```python
deeplabcut.create_multianimaltraining_dataset(path_config_file)
```
to begin training.
**Advanced option:** You can also assign former `bodyparts` to either `uniquebodyparts` or `multianimalbodyparts`
(you can even leave some unassigned, which means they will be dropped in the conversion).
Example: Imagine you had a project with the moon and a rocket with two parts labeled:
`bodyparts: [moon, rocket_tip,rocket_bottom]`
Now you want to use this former project (labeled-data) and work on a new dataset (videos) with one moon but multiple
(3) rockets. Then convert it as follows:
```
individuals: [rocket1, rocket2, rocket3]
uniquebodyparts: [moon]
multianimalbodyparts: [rocket_tip,rocket_bottom]
skeleton: [[[rocket_tip,rocket_bottom]]]
```
In the unusual case, that your data also has multiple moons (e.g. is now carried out around Jupiter), but one rocket:
```
individuals: [Io, Europa, Ganymede, Callisto]
uniquebodyparts: [rocket_tip,rocket_bottom]
multianimalbodyparts: [moon]
```
Note you can use the single object tracker for this situation. What if you have multiple moons and rockets?