Official Implementation of Vision-based Behavioral Recognition of Novelty Preference in Pigs
Arxiv Paper: https://arxiv.org/abs/2106.12181 (CVPR2021 CV4Animals Workshop Poster)
Download the dataset and the annotations from this drive link and place under the data folder.
Use the script extract_frames.py to extract and downsample the annotated frames from the dataset.
Use statistic.ipynb to truncate clips into a fixed length of either 30 or 60 frames.
Create a directory checkpoints and place this TSM checkpoint in the checkpoints folder. Run annotate.py using the following sample command:
python3 annotate.py -v data/videos/1815_C2_624_4wk.mp4 -c checkpoints/tsm.best.pth.tar -m data/pncl-maskfilter.png -j data/
Run python3 train.py to train the model.
To use pretrained model, download the cnn-pig.pth and rnn-pig.pth from this drive and place in the models/LRCN/checkpoints folder
Run python3 annotate-folder.py to annotate the video dataset
Download the C3D sports-1m weights using
cd models/C3D
wget https://github.com/adamcasson/c3d/releases/download/v0.1/sports1M_weights_tf.h5 -o c3d_sports1m.h5
Precompute C3D features for the dataset using the script extract.py.
Run python3 train.py to train the model.
Run python3 annotate-folder.py to annotate the video dataset.
Follow the procedure specified here to generate the dataset. Run the following command to train the model:
python3 main.py pig RGB \
-p 2 --arch resnet18 --num_segments 8 --gd 20 --lr 0.02 \
--wd 1e-4 --lr_steps 12 25 --epochs 35 --batch-size 64 -j 16 --dropout 0.5 \
--consensus_type=avg --eval-freq=1 --shift --shift_div=8 --shift_place=blockres --npb
Run python3 annotate.py to annotate the video dataset.