Our new model ZOO works with DepthAI V3. Find out more in our documentation.
Model Details
Model Description
The DLC SuperAnimal keypoints model is intended to be used for pose estimation of quadruped (animals that have 4 legs) images taken from side view. It has 39 keypoints. Model can estimate the pose of more than 45 different species: from mice, rats, horses, dogs, and cats, to elephants and gazelles. Input image should be already cropped so it only contains the desired animal
Developed by: Mathis Lab
Shared by:
Model type: Computer Vision
License:
Resources for more information:
Training Details
Training Data
Quadruped-80K dataset which is built from over 85,000 images sourced from diverse laboratory settings and in-the-wild data. For more information check the .
Testing Details
Metrics
Evaluation results are taken from .
Dataset
mAP
RMSE
AP-10K
80.11
11.30
AnimalPose
87.03
4.64
Horse-10
95.17
1.15
Technical Specifications
Input/Output Details
Input:
Name: input
Info: NCHW BGR un-normalized image
Output:
Name: heatmaps
Info: Heatmaps for all 39 keypoints. Additional postprocessing is required (see the parser node).
Model Architecture
Please consult for more information on model architecture.
* Benchmarked with , using 2 threads (and the DSP runtime in balanced mode for RVC4).
* Parameters and FLOPs are obtained from the package.
Quantization
RVC4 version of the model was quantized using a custom dataset.
This was created by taking 70-image subset of dataset.
Utilization
Models converted for RVC Platforms can be used for inference on OAK devices.
DepthAI pipelines are used to define the information flow linking the device, inference model, and the output parser (as defined in model head(s)).
Below, we present the most crucial utilization steps for the particular model.
Please consult the docs for more information.
SuperAnimalParser that outputs message (at most 39 detected keypoints with scores).
Get parsed output(s):
while pipeline.isRuning():
parser_output: Keypoints = parser_output_queue.get()
Example
You can quickly run the model using our example.
The example demonstrates how to build a 2-stage DepthAI pipeline for detecting animals and estimating their poses. The pipeline consists of object detector and pose estimation model. The example works on both RVC2 and RVC4. For realtime application you will need to use OAK4 cameras.
It automatically downloads the model, creates a DepthAI pipeline, runs the inference, and displays the results using our DepthAI visualizer tool.
