Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats

Here we introduce the dataset utilized in our published paper entitled "Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats".

Contained within the "bcs" folder are all the videos collected for this study. Each video file is named with a format denoting its respective details. The first number signifies the sequence of collection, the second denotes the ear tag, and the final figure represents the body condition score (BCS) value.

For example: "1_158734_2.50" indicates the first sampling of an animal with the ear tag "158734" and a BCS value of "2.50".

Additionally, we provide two Python scripts in this repository. The first script, "Video2Frame.py", facilitates the splitting of videos into individual frames. The second script, "Frames2npy.py", converts these frames into two numpy-friendly files with the extension ".npy". These files contain both the images ("X_train_bcs300.npy") and their corresponding labels ("Y_train_bcs300.npy").

Furthermore, for the convenience of swift experimentation, we have included the desired .npy files within the repository.

To load these files into your Python environment, you can use the following code snippet:

th4figs = '/content/drive/MyDrive/compag_2023/'

path4images = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/X_train_bcs300.npy"

Xtrain = np.load(path4images)

path4labels = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/Y_train_bcs300.npy"

Ytrain = np.load(path4labels).astype(float)

print("X train : ", Xtrain.shape)

print("Y train : ", Ytrain.shape)

X train : (5332, 300, 300, 3)
Y train : (5332,)