--- title: coco-multilabel canonical_url: "https://www.modelscope.cn/datasets/swift/coco-multilabel" md_url: "https://www.modelscope.cn/datasets/swift/coco-multilabel.md" repository: swift/coco-multilabel last_updated: 2026-04-04 license: "Apache License 2.0" storage_size: "19 GB" downloads: 9878 stars: 4 --- # coco-multilabel > coco-multilabel - swift 在 ModelScope 开源的数据集。数据集地址：https://modelscope.cn/datasets/swift/coco-multilabel 数据集下载： swift/coco-multilabel 是 ModelScope 魔搭社区上的数据集，存储大小 19 GB，采用 Apache License 2.0 许可。 - **Repository**: swift/coco-multilabel - **License**: Apache License 2.0 - **Storage size**: 19 GB - **Downloads**: 9878 - **Stars**: 4 - **Last updated**: 2026-04-04 Source: https://www.modelscope.cn/datasets/swift/coco-multilabel --- ## 数据集 数据集地址：https://modelscope.cn/datasets/swift/coco-multilabel 数据集下载： ```python from modelscope import MsDataset dataset = MsDataset.load('swift/coco-multilabel', split='train') test_dataset = MsDataset.load('swift/coco-multilabel', split='test') print(dataset) print(test_dataset) """ Dataset({ features: ['images', 'labels'], num_rows: 117218 }) Dataset({ features: ['images', 'labels'], num_rows: 5000 }) """ print(dataset[0].keys(), dataset[0]['labels']) print(test_dataset[0].keys(), test_dataset[0]['labels']) """ dict_keys(['images', 'labels']) [49, 76] dict_keys(['images', 'labels']) None """ # 查看图片 from PIL import Image import io image_bytes = dataset[0]['images'][0]['bytes'] image = Image.open(io.BytesIO(image_bytes)) image.save('image.png') ``` 该数据集包含 117,218 条训练样本 和 5,000 条测试样本。在训练集中，每条样本均包含真实标签 labels，代表图像中包含哪些东西（例如：person, cat, train等）。测试集中则不包含 labels 标签，模型需要根据测试集中提供 "images" 字段，推断出labels是什么，本质是多标签分类问题。你可以使用生成式任务/多标签任务来解决这个问题。 标签的对应关系请查看`labels.txt`文件。 ## Baseline 以下介绍使用ms-swift大模型训练框架，对Qwen3.5-4B使用该数据集进行LoRA微调的baseline - ms-swift github：https://github.com/modelscope/ms-swift - Qwen3.5: https://modelscope.cn/models/Qwen/Qwen3.5-4B - 以下提供的baseline所需显存资源12GiB，可在魔搭免费算力A10上运行 - 该baseline的F1为: 0.8925 环境准备： ```shell pip install "ms-swift==4.0.*" "transformers==5.2.*" "peft==0.18.*" -U ``` 单卡训练： ```python # GPU Memory: 12GB import os from typing import Any, Dict from swift import SftArguments, sft_main from swift.dataset import DatasetMeta, ResponsePreprocessor, SubsetDataset, register_dataset os.environ['CUDA_VISIBLE_DEVICES'] = '0' os.environ['MAX_PIXELS'] = str(1024 * 32 * 32) labels = [ 'airplane', 'apple', 'backpack', 'banana', 'baseball bat', 'baseball glove', 'bear', 'bed', 'bench', 'bicycle', 'bird', 'boat', 'book', 'bottle', 'bowl', 'broccoli', 'bus', 'cake', 'car', 'carrot', 'cat', 'cell phone', 'chair', 'clock', 'couch', 'cow', 'cup', 'dining table', 'dog', 'donut', 'elephant', 'fire hydrant', 'fork', 'frisbee', 'giraffe', 'hair drier', 'handbag', 'horse', 'hot dog', 'keyboard', 'kite', 'knife', 'laptop', 'microwave', 'motorcycle', 'mouse', 'orange', 'oven', 'parking meter', 'person', 'pizza', 'potted plant', 'refrigerator', 'remote', 'sandwich', 'scissors', 'sheep', 'sink', 'skateboard', 'skis', 'snowboard', 'spoon', 'sports ball', 'stop sign', 'suitcase', 'surfboard', 'teddy bear', 'tennis racket', 'tie', 'toaster', 'toilet', 'toothbrush', 'traffic light', 'train', 'truck', 'tv', 'umbrella', 'vase', 'wine glass', 'zebra' ] labels_str = ', '.join(labels) class CustomPreprocessor(ResponsePreprocessor): def preprocess(self, row: Dict[str, Any]) -> Dict[str, Any]: row['query'] = f'Multi-label classification with the following categories: {labels_str}' return super().preprocess(row) register_dataset( DatasetMeta( ms_dataset_id='swift/coco-multilabel', preprocess_func=CustomPreprocessor(columns={'labels': 'label'}), subsets=[SubsetDataset('train', split=['train']), SubsetDataset('test', split=['test'])])) if __name__ == '__main__': sft_main( SftArguments( model='Qwen/Qwen3.5-4B', tuner_type='lora', # 节约时间，只选择20000条数据集 dataset=['swift/coco-multilabel:train#20000'], load_from_cache_file=True, add_non_thinking_prefix=True, loss_scale='ignore_empty_think', split_dataset_ratio=0.01, torch_dtype='bfloat16', num_train_epochs=1, per_device_train_batch_size=4, per_device_eval_batch_size=4, learning_rate=1e-4, lora_rank=8, lora_alpha=32, target_modules=['all-linear'], freeze_vit=True, gradient_accumulation_steps=4, eval_steps=100, save_steps=100, save_total_limit=2, logging_steps=5, max_length=2048, output_dir='output', warmup_ratio=0.05, dataset_num_proc=4, dataloader_num_workers=4, num_labels=80, task_type='seq_cls', problem_type='multi_label_classification', )) ``` ## 提交结果 我们提供了推理脚本, 最终需要将以下推理脚本产生的`infer_result`目录中的jsonl文件进行提交 (由于比赛界面只允许传递json后缀的文件, 请重命名为`result.json`, 不需要改内容). ```python # GPU Memory: 12GB import os from typing import Any, Dict from swift import InferArguments, infer_main from swift.dataset import DatasetMeta, ResponsePreprocessor, SubsetDataset, register_dataset os.environ['CUDA_VISIBLE_DEVICES'] = '0' os.environ['MAX_PIXELS'] = str(1024 * 32 * 32) labels = [ 'airplane', 'apple', 'backpack', 'banana', 'baseball bat', 'baseball glove', 'bear', 'bed', 'bench', 'bicycle', 'bird', 'boat', 'book', 'bottle', 'bowl', 'broccoli', 'bus', 'cake', 'car', 'carrot', 'cat', 'cell phone', 'chair', 'clock', 'couch', 'cow', 'cup', 'dining table', 'dog', 'donut', 'elephant', 'fire hydrant', 'fork', 'frisbee', 'giraffe', 'hair drier', 'handbag', 'horse', 'hot dog', 'keyboard', 'kite', 'knife', 'laptop', 'microwave', 'motorcycle', 'mouse', 'orange', 'oven', 'parking meter', 'person', 'pizza', 'potted plant', 'refrigerator', 'remote', 'sandwich', 'scissors', 'sheep', 'sink', 'skateboard', 'skis', 'snowboard', 'spoon', 'sports ball', 'stop sign', 'suitcase', 'surfboard', 'teddy bear', 'tennis racket', 'tie', 'toaster', 'toilet', 'toothbrush', 'traffic light', 'train', 'truck', 'tv', 'umbrella', 'vase', 'wine glass', 'zebra' ] labels_str = ', '.join(labels) class CustomPreprocessor(ResponsePreprocessor): def preprocess(self, row: Dict[str, Any]) -> Dict[str, Any]: row['query'] = f'Multi-label classification with the following categories: {labels_str}' return super().preprocess(row) register_dataset( DatasetMeta( ms_dataset_id='swift/coco-multilabel', preprocess_func=CustomPreprocessor(columns={'labels': 'label'}), subsets=[SubsetDataset('train', split=['train']), SubsetDataset('test', split=['test'])])) if __name__ == '__main__': ckpt_dir = 'output/vx-xxx/checkpoint-xxx' # last_checkpoint result = infer_main(InferArguments( adapters=[ckpt_dir], temperature=0, val_dataset=["swift/coco-multilabel:test"], max_batch_size=16, infer_backend='transformers')) # 结果会保存在`{ckpt_dir}/infer_result/xxx-xxx.jsonl`中, 提交该文件即可. # (由于比赛界面只能传递json后缀的文件, 请重命名为`result.json`, 不需要改内容). ``` 提交的jsonl文件格式如下，顺序与`test_dataset`顺序一致，共5000条， ``` {"response": [7, 22, 24, 64]} {"response": [...]} {"response": [...]} ``` 打分脚本： 最终结果以F1为评测标准进行排序。 ```python from swift.utils import read_from_jsonl from datasets import load_dataset labels = load_dataset('parquet', data_files='test_labels.parquet', split='train')['labels'] results = read_from_jsonl('result.jsonl') tp, fp, fn = 0, 0, 0 for i, (res, label) in enumerate(zip(results, labels)): y_pred = set(res['response']) y_true = set(label) intersection = y_pred & y_true tp += len(intersection) fp += len(y_pred - y_true) fn += len(y_true - y_pred) precision = tp / (tp + fp) recall = tp / (tp + fn) f1 = 2 * precision * recall / (precision + recall) print(f'Precision: {precision:.4f}') print(f'Recall: {recall:.4f}') print(f'F1 Score: {f1:.4f}') ```