Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats
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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,)
本文介绍了已发表论文《Goat-CNN：一种用于山羊姿态无关体况评分（Body Condition Score，简称BCS）估计的轻量级卷积神经网络（Lightweight Convolutional Neural Network）》中所使用的数据集。 本研究采集的全部视频均存储于"bcs"文件夹内。每个视频文件的命名格式均包含其相关细节：首位数字代表采集序列序号，第二位数字为耳标号，末尾数字则为体况评分值。 例如，文件名"1_158734_2.50"代表耳标号为"158734"的受试动物的第1次采样样本，其体况评分为"2.50"。 此外，本仓库附带两份Python脚本：其一为"Video2Frame.py"，可实现将视频分割为独立图像帧的功能；其二为"Frames2npy.py"，可将上述图像帧转换为两份适配NumPy的".npy"格式文件，分别存储图像数据（"X_train_bcs300.npy"）与对应的标签数据（"Y_train_bcs300.npy"）。 为便于快速开展实验，本仓库同时提供了所需的".npy"格式文件。 若需在Python环境中加载此类文件，可使用以下代码片段： 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,)
