Download WTBD/generate_split.py from ObjEarth/ObjEarth-Data: direct link, hf CLI and curl.
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https://huggingface.co/datasets/ObjEarth/ObjEarth-Data/resolve/refs%2Fpr%2F2/WTBD/generate_split.py
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hf download hf://datasets/ObjEarth/ObjEarth-Data@refs/pr/2/WTBD/generate_split.py
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curl -L -o generate_split.py https://huggingface.co/datasets/ObjEarth/ObjEarth-Data/resolve/refs%2Fpr%2F2/WTBD/generate_split.py
1.54 kB
| import random | |
| import os | |
| def generate_split_file(total_images=1065, seed=42): | |
| # 1. 准备文件名列表 (0.jpg 到 1064.jpg) | |
| filenames = [f"{i}.jpg" for i in range(total_images)] | |
| # 2. 设置随机种子以保证可复现性 (Reviewer 重点关注点) | |
| random.seed(seed) | |
| # 3. 打乱列表 | |
| shuffled_files = filenames.copy() | |
| random.shuffle(shuffled_files) | |
| # 4. 计算划分数量 (70% / 15% / 15%) | |
| n_train = int(total_images * 0.70) | |
| n_val = int(total_images * 0.15) | |
| # 剩余归为测试集 | |
| train_files = set(shuffled_files[:n_train]) | |
| val_files = set(shuffled_files[n_train:n_train + n_val]) | |
| test_files = set(shuffled_files[n_train + n_val:]) | |
| # 5. 写入文件 | |
| # 为了方便用户查阅,我们按文件名顺序(0->1064)写入,并在后面标注它属于哪个集 | |
| output_filename = "train_val_test_split.txt" | |
| with open(output_filename, "w") as f: | |
| # 写入表头 | |
| f.write("ImageID,Subset\n") | |
| for i in range(total_images): | |
| fname = f"{i}.jpg" | |
| if fname in train_files: | |
| subset = "train" | |
| elif fname in val_files: | |
| subset = "val" | |
| else: | |
| subset = "test" | |
| f.write(f"{fname},{subset}\n") | |
| print(f"Successfully generated {output_filename}") | |
| print(f"Stats: Train={len(train_files)}, Val={len(val_files)}, Test={len(test_files)}") | |
| if __name__ == "__main__": | |
| generate_split_file() |