> ## Documentation Index
> Fetch the complete documentation index at: https://docs.biom.science/llms.txt
> Use this file to discover all available pages before exploring further.

# DeepLabCut — Pose Estimation

> Markerless animal pose estimation and tracking.

# DeepLabCut

DeepLabCut provides markerless pose estimation for tracking animal body parts in videos and images. Relay supports both inference with pre-trained models and training custom models.

## Input formats

`.mp4`, `.avi`, `.mov`, `.mkv`, `.tiff`, `.png`, `.jpg`

## Pre-trained models

| Model             | Description                                               |
| ----------------- | --------------------------------------------------------- |
| **full\_mouse**   | Full-body mouse tracking (side view)                      |
| **full\_rat**     | Full-body rat tracking                                    |
| **top\_mouse**    | Top-down mouse tracking                                   |
| **side\_mouse**   | Side-view mouse tracking                                  |
| **primate\_face** | Primate facial landmark tracking                          |
| **horse**         | Horse body tracking                                       |
| **custom**        | Your own trained model (upload a DLC project or snapshot) |

## Parameters

| Parameter                       | Range  | Default | Description                                      |
| ------------------------------- | ------ | ------- | ------------------------------------------------ |
| **Confidence cutoff** (pcutoff) | 0–1    | 0.6     | Minimum confidence to show a keypoint            |
| **Batch size**                  | 1–64   | 8       | Frames processed per batch                       |
| **Num animals**                 | 1–20   | 1       | Number of animals to track                       |
| **Identity tracking**           | Toggle | Off     | Track individual identity across frames          |
| **Overlay video**               | Toggle | On      | Generate video with skeleton overlay             |
| **CSV export**                  | Toggle | On      | Export keypoint coordinates as CSV               |
| **Dynamic cropping**            | Toggle | Off     | Crop around detected animals for better accuracy |

## Outputs

| Output          | Format | Description                          |
| --------------- | ------ | ------------------------------------ |
| Keypoint tracks | H5     | Full keypoint coordinates per frame  |
| Coordinates     | CSV    | Tabular keypoint data                |
| Skeleton        | JSON   | Skeleton connectivity definition     |
| Overlay video   | MP4    | Video with skeleton visualization    |
| QC metrics      | JSON   | Quality metrics and confidence stats |

## Presets

| Preset                 | Description                                      |
| ---------------------- | ------------------------------------------------ |
| **Quick Analysis**     | Fast inference with default settings             |
| **High Quality**       | Higher confidence threshold, all outputs enabled |
| **Multi-Animal Setup** | Multi-animal mode with identity tracking         |

## Compute requirements

| Resource | Requirement                                      |
| -------- | ------------------------------------------------ |
| GPU      | T4 minimum (4–6+ GB VRAM)                        |
| Duration | \~300 seconds baseline, scales with video length |

## Training custom models

You can train custom DeepLabCut models on your own labeled data:

### Base architectures

| Architecture        | Description                                  |
| ------------------- | -------------------------------------------- |
| ResNet-50           | Standard, good balance of speed and accuracy |
| ResNet-101          | Higher accuracy, slower                      |
| EfficientNet-B0     | Efficient, lower memory                      |
| MobileNet-V2        | Fast, mobile-optimized                       |
| HRNet-W32           | High-resolution, best accuracy               |
| DLCRNet-MS5         | DLC-specific architecture                    |
| SuperAnimal-Mouse   | Pre-trained on diverse mouse data            |
| SuperAnimal-Primate | Pre-trained on diverse primate data          |

### Training parameters

| Parameter         | Range                          | Default | Description                        |
| ----------------- | ------------------------------ | ------- | ---------------------------------- |
| Max iterations    | 10k–1M                         | 200k    | Training iterations                |
| Batch size        | 1–32                           | 8       | Training batch size                |
| Augmentation      | default/tensorpack/imgaug/none | default | Data augmentation strategy         |
| Transfer learning | Toggle                         | On      | Fine-tune from pre-trained weights |

### Training presets

| Preset           | Iterations | Architecture | Use case                    |
| ---------------- | ---------- | ------------ | --------------------------- |
| **Quick**        | 50k        | MobileNet-V2 | Rapid prototyping           |
| **Standard**     | 200k       | ResNet-50    | General purpose             |
| **High Quality** | 500k       | HRNet-W32    | Publication-quality results |

### Training outputs

* Trained model (ZIP)
* Checkpoint files (.pb)
* Loss curves (JSON + PNG)
* Evaluation metrics (JSON)