Welcome to our module!
Aja-pose
AJA-pose helps you train, validate and test your animal pose estimation model.
Check out how we have done it in Google Colab.
We have evaluated our model (PCK0.05) and the mean accuracy for the 23 keypoints for our model is 93.073%<br>
We recommend using at least Nvidia V100 GPU for faster inferencing but T4 GPUs will still work.
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[](https://colab.research.google.com/drive/1N3v7Y-PN9uvw5V5PUbAYqh9vGkLfm_Km?usp=sharing)
Getting Started
bash
git clone https://github.com/Antony-gitau/AJA-pose.git
cd AJA-pose
pip install -e .
Getting our model and the dataset.
We have made our model public and can be downloaded here
python
import urllib.request
Get the dataset
url = "https://storage.googleapis.com/figures-gp/animal-kingdom/dataset.zip"
destination = "dataset.zip"
urllib.request.urlretrieve(url, destination)
Unzip the file
!unzip dataset.zip
The model file
url = "https://storage.googleapis.com/figures-gp/animal-kingdom/all_animals_no_pretrain_106.pth"
destination = "all_animals_no_pretrain_60.pth"
urllib.request.urlretrieve(url, destination)
Test our model<br>
We require the path to the test images directory and the test.json file in MPII format
python
from aja_pose import Model
path to the images directory and annotation in mpii json format
images_directory = '' Path to the images directory
mpii_json = '' Path to the test.json file
model_file = 'all_animals_no_pretrain_60.pth' Path to the model file
Initialize the class
model = Model()
Test the model on Protocol 1
model.test(images_directory, protocol='P1', model=model_file)
Test the model on Protocol 2
model.test(images_directory, protocol='P2', model=model_file)
Test the model on birds class Protocol 3
model.test(images_directory, protocol='P3', model=model_file, animal_class='bird')
Test the model on reptiles class Protocol 3
model.test(images_directory, protocol='P3', model=model_file, animal_class='reptile')
Test the model on mammals class Protocol 3
model.test(images_directory, protocol='P3', model=model_file, animal_class='mammal')
Test the model on fish class Protocol 3
model.test(images_directory, protocol='P3', model=model_file, animal_class='fish')
Test the model on amphibian class Protocol 3
model.test(images_directory, protocol='P3', model=model_file, animal_class='amphibian')
You can also start to train your model or pretrain on top of ours
python
train a VHR model
train_json = '' labels for the train set (train.json)
valid_json = '' Labels for the validation set (test.json)
model_file = '' A pytorch model file to pretrain on.
model.train(images_directory, train_json, valid_json, pretrained=model_file)
Train a model on a particular class e.g (Ampibian)
model.train(images_directory, protocol='P3', animal_class='amphibian', model=model_file)
Results
A sanity check on our model.
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Ground Truth
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Predictions
Performance
The performance of our model on the different animal classes is as shown below.
| Animal Class | Samples | Head | Shoulder | Elbow | Wrist | Hip | Knee | Ankle | Mouth | Tail | Mean |
|---------------|---------|-------|----------|-------|-------|-----|------|-------|-------|------|------|
| Birds | 1705 | 95.756 | 93.637 | 89.774 | 88.179 | 98.975 | 97.582 | 94.326 | 98.447 | 95.112 | 95.164 |
| Reptiles | 1209 | 91.538 | 85.291 | 84.662 | 85.587 | 90.457 | 88.097 | 85.239 | 96.723 | 83.925 | 89.553 |
| Mammals | 1496 | 90.641 | 89.269 | 88.509 | 89.927 | 90.263 | 88.655 | 89.535 | 93.622 | 82.161 | 90.038 |
| Fish | 918 | 96.468 | 96.249 | 98.643 | 96.058 | 98.403 | 96.743 | 95.775 | 97.564 | 98.256 | 96.467 |
| Amphibian | 1279 | 98.128 | 94.342 | 97.948 | 98.508 | 95.491 | 94.957 | 94.319 | 98.702 | 99.568 | 95.493 |
The model performance on Protocol 1 and Protocol 2 is as shown below.
| Protocol | Samples | Head | Shoulder | Elbow | Wrist | Hip | Knee | Ankle | Mouth | Tail | Mean |
|----------|---------|------|----------|-------|-------|-----|------|-------|-------|------|------|
| P1 | 6620 | 94.230 | 91.054 | 90.806 | 90.920 | 94.414 | 93.233 | 92.094 | 96.867 | 92.346 | 93.073 |
| P2 | 2883 | 88.683 | 75.815 | 80.223 | 81.136 | 85.568 | 83.840 | 82.028 | 94.799 | 72.506 | 83.711 |