AutoMM 检测 - 目标检测数据格式¶

本节介绍 AutoMM 检测支持的两种主要数据格式：COCO 格式和 DataFrame 格式。

COCO 格式¶

有关 COCO 数据集格式的详细介绍，请参见将数据转换为 COCO 格式一节。实质上，您需要一个包含数据集数据信息的 .json 文件。例如，您可以按以下格式准备数据

data = {
    # list of dictionaries containing all the category information
    "categories": [
        {"supercategory": "none", "id": 1, "name": "person"},
        {"supercategory": "none", "id": 2, "name": "bicycle"},
        {"supercategory": "none", "id": 3, "name": "car"},
        {"supercategory": "none", "id": 4, "name": "motorcycle"},
        # ...
    ],

    # list of dictionaries containing image info
    "images": [
        {
            "file_name": "<imagename0>.<ext>",
            "height": 427,
            "width": 640,
            "id": 1
        },
        {
            "file_name": "<imagename2>.<ext>",
            "height": 427,
            "width": 640,
            "id": 2
        },
        # ...
    ],
    # list of dictionaries containing bounding box annotation info
    "annotations": [
        {
            'area': 33453,  # area of the bounding box
            'iscrowd': 0,  # if the bounding box contains multiple objects, usually this is 0 since we are dealing with single box -> single object 
            'bbox': [181, 133, 177, 189],  # the [x, y, width, height] format annotation of bounding box
            'category_id': 8,  # the "id" field of the corresponding category, not the "name" field
            'ignore': 0,  # set to 1 to ignore this annotation
            'segmentation': [],  # always empty since this tutorial is not for segmentation
            'image_id': 1617,  # the "id" field of the corresponding image
            'id': 1  # the "id" of this particular annotation
        },
        {
            'area': 25740, 
            'iscrowd': 0,
            'bbox': [192, 100, 156, 165],
            'category_id': 9,
            'ignore': 0,
            'segmentation': [],
            'image_id': 1617,
            'id': 2
        },
        # ...
    ],
    
    "type": "instances"
}

`pd.DataFrame` 格式¶

AutoMM 检测也支持 pd.DataFrame 格式。您的 pd.DataFrame 应包含 3 列。

image: 图像文件的路径
rois: 包含边界框标注 [x1, y1, x2, y2, class_label] 的数组列表
label: rois 列的副本

示例如下

                                               image  \
/home/ubuntu/autogluon-dev/docs/tutorials/mult...   
/home/ubuntu/autogluon-dev/docs/tutorials/mult...   
/home/ubuntu/autogluon-dev/docs/tutorials/mult...   
/home/ubuntu/autogluon-dev/docs/tutorials/mult...   
/home/ubuntu/autogluon-dev/docs/tutorials/mult...   

                                                rois  \
[[352.0, 138.0, 374.0, 373.0, 7], [105.0, 1.0,...   
[[40.0, 71.0, 331.0, 332.0, 7], [33.0, 42.0, 3...   
[[52.0, 22.0, 306.0, 326.0, 8], [26.0, 108.0, ...   
[[114.0, 154.0, 367.0, 346.0, 7], [292.0, 49.0...   
[[279.0, 225.0, 374.0, 338.0, 3], [245.0, 230....   

                                               label  
[[352.0, 138.0, 374.0, 373.0, 7], [105.0, 1.0,...  
[[40.0, 71.0, 331.0, 332.0, 7], [33.0, 42.0, 3...  
[[52.0, 22.0, 306.0, 326.0, 8], [26.0, 108.0, ...  
[[114.0, 154.0, 367.0, 346.0, 7], [292.0, 49.0...  
[[279.0, 225.0, 374.0, 338.0, 3], [245.0, 230....  

使用数据格式训练和评估模型¶

下载数据¶

我们已在云端准备了样本数据集。让我们下载它

import os

from autogluon.core.utils.loaders import load_zip

zip_file = "https://automl-mm-bench.s3.amazonaws.com/object_detection_dataset/tiny_motorbike_coco.zip"
download_dir = "./tiny_motorbike_coco"

load_zip.unzip(zip_file, unzip_dir=download_dir)
data_dir = os.path.join(download_dir, "tiny_motorbike")
train_path = os.path.join(data_dir, "Annotations", "trainval_cocoformat.json")
test_path = os.path.join(data_dir, "Annotations", "test_cocoformat.json")

Downloading ./tiny_motorbike_coco/file.zip from https://automl-mm-bench.s3.amazonaws.com/object_detection_dataset/tiny_motorbike_coco.zip...

  0%|          | 0.00/21.8M [00:00<?, ?iB/s]
 48%|████▊     | 10.5M/21.8M [00:00<00:00, 105MiB/s]
 96%|█████████▌| 21.0M/21.8M [00:00<00:00, 44.1MiB/s]
100%|██████████| 21.8M/21.8M [00:00<00:00, 45.4MiB/s]

我们提供了实用的工具函数，用于将 COCO 格式转换为 pd.DataFrame 格式，反之亦然。

从 COCO 格式到 `pd.DataFrame`¶

现在我们首先介绍如何从 COCO 转换为 pd.DataFrame

from autogluon.multimodal.utils.object_detection import from_coco
train_df = from_coco(train_path)
print(train_df)

loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
                                                 image  \
  ./tiny_motorbike_coco/tiny_motorbike/Annotatio...   
  ./tiny_motorbike_coco/tiny_motorbike/Annotatio...   
  ./tiny_motorbike_coco/tiny_motorbike/Annotatio...   
  ./tiny_motorbike_coco/tiny_motorbike/Annotatio...   
  ./tiny_motorbike_coco/tiny_motorbike/Annotatio...   
..                                                 ...   
./tiny_motorbike_coco/tiny_motorbike/Annotatio...   
./tiny_motorbike_coco/tiny_motorbike/Annotatio...   
./tiny_motorbike_coco/tiny_motorbike/Annotatio...   
./tiny_motorbike_coco/tiny_motorbike/Annotatio...   
./tiny_motorbike_coco/tiny_motorbike/Annotatio...   

                                                  rois  \
  [[352.0, 138.0, 499.0, 374.0, 7], [105.0, 1.0,...   
  [[40.0, 71.0, 332.0, 472.0, 7], [33.0, 42.0, 3...   
  [[52.0, 22.0, 307.0, 327.0, 8], [26.0, 108.0, ...   
  [[114.0, 154.0, 368.0, 347.0, 7], [292.0, 49.0...   
                  [[120.0, 333.0, 309.0, 463.0, 7]]   
..                                                 ...   
[[1.0, 187.0, 496.0, 332.0, 7], [124.0, 15.0, ...   
[[205.0, 24.0, 379.0, 286.0, 8], [118.0, 82.0,...   
[[192.0, 48.0, 320.0, 365.0, 8], [39.0, 123.0,...   
[[137.0, 55.0, 319.0, 339.0, 7], [242.0, 1.0, ...   
[[87.0, 120.0, 328.0, 290.0, 7], [145.0, 38.0,...   

                                                 label  
  [[352.0, 138.0, 499.0, 374.0, 7], [105.0, 1.0,...  
  [[40.0, 71.0, 332.0, 472.0, 7], [33.0, 42.0, 3...  
  [[52.0, 22.0, 307.0, 327.0, 8], [26.0, 108.0, ...  
  [[114.0, 154.0, 368.0, 347.0, 7], [292.0, 49.0...  
                  [[120.0, 333.0, 309.0, 463.0, 7]]  
..                                                 ...  
[[1.0, 187.0, 496.0, 332.0, 7], [124.0, 15.0, ...  
[[205.0, 24.0, 379.0, 286.0, 8], [118.0, 82.0,...  
[[192.0, 48.0, 320.0, 365.0, 8], [39.0, 123.0,...  
[[137.0, 55.0, 319.0, 339.0, 7], [242.0, 1.0, ...  
[[87.0, 120.0, 328.0, 290.0, 7], [145.0, 38.0,...  

[170 rows x 3 columns]

从 `pd.DataFrame` 到 COCO 格式¶

from autogluon.multimodal.utils.object_detection import object_detection_df_to_coco

train_coco = object_detection_df_to_coco(train_df)
print(train_coco)

{'images': [{'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000051.jpg', 'height': 500, 'width': 375, 'id': 0}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000072.jpg', 'height': 333, 'width': 500, 'id': 1}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000163.jpg', 'height': 500, 'width': 432, 'id': 2}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000164.jpg', 'height': 500, 'width': 375, 'id': 3}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000219.jpg', 'height': 333, 'width': 500, 'id': 4}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000302.jpg', 'height': 500, 'width': 375, 'id': 5}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000374.jpg', 'height': 500, 'width': 375, 'id': 6}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000419.jpg', 'height': 500, 'width': 375, 'id': 7}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000427.jpg', 'height': 500, 'width': 334, 'id': 8}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000463.jpg', 'height': 500, 'width': 175, 'id': 9}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000499.jpg', 'height': 500, 'width': 375, 'id': 10}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000525.jpg', 'height': 500, 'width': 331, 'id': 11}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000579.jpg', 'height': 500, 'width': 332, 'id': 12}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000583.jpg', 'height': 500, 'width': 276, 'id': 13}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000685.jpg', 'height': 500, 'width': 375, 'id': 14}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000686.jpg', 'height': 500, 'width': 375, 'id': 15}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000733.jpg', 'height': 450, 'width': 435, 'id': 16}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000770.jpg', 'height': 500, 'width': 375, 'id': 17}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000822.jpg', 'height': 500, 'width': 374, 'id': 18}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000851.jpg', 'height': 500, 'width': 375, 'id': 19}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000892.jpg', 'height': 500, 'width': 375, 'id': 20}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/000987.jpg', 'height': 500, 'width': 447, 'id': 21}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/001008.jpg', 'height': 500, 'width': 375, 'id': 22}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/001017.jpg', 'height': 333, 'width': 500, 'id': 23}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/001068.jpg', 'height': 500, 'width': 332, 'id': 24}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/001171.jpg', 'height': 500, 'width': 331, 'id': 25}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/001199.jpg', 'height': 500, 'width': 375, 'id': 26}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/001201.jpg', 'height': 500, 'width': 333, 'id': 27}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/001266.jpg', 'height': 500, 'width': 332, 'id': 28}, {'file_name': './tiny_motorbike_coco/tiny_motorbike/Annotations/../JPEGImages/001286.jpg', 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'category_id': 3, 'ignore': 0, 'segmentation': [], 'image_id': 168, 'id': 688}, {'area': 40970.0, 'iscrowd': 0, 'bbox': [87.0, 120.0, 241.0, 170.0], 'category_id': 7, 'ignore': 0, 'segmentation': [], 'image_id': 169, 'id': 689}, {'area': 39909.0, 'iscrowd': 0, 'bbox': [145.0, 38.0, 159.0, 251.0], 'category_id': 8, 'ignore': 0, 'segmentation': [], 'image_id': 169, 'id': 690}], 'categories': [{'supercategory': 'none', 'id': 0, 'name': 7}, {'supercategory': 'none', 'id': 1, 'name': 8}, {'supercategory': 'none', 'id': 2, 'name': 3}, {'supercategory': 'none', 'id': 3, 'name': 5}, {'supercategory': 'none', 'id': 4, 'name': 0}, {'supercategory': 'none', 'id': 5, 'name': 2}, {'supercategory': 'none', 'id': 6, 'name': 9}, {'supercategory': 'none', 'id': 7, 'name': 6}, {'supercategory': 'none', 'id': 8, 'name': 1}, {'supercategory': 'none', 'id': 9, 'name': 4}]}

您可以通过在调用 object_detection_df_to_coco 时指定 save_path，将字典 train_coco 保存到 .json 文件中。

train_coco = object_detection_df_to_coco(train_df, save_path="./df_converted_to_coco.json")

下次通过调用 from_coco 从 .json 文件加载时，请确保提供正确的 root，以便 <root>/<file_name> 是有效的图像路径。（注意：file_name 位于本教程开头定义的 data 中 "images" 子字段下。）例如

train_df_from_saved_coco = from_coco("./df_converted_to_coco.json", root="./")

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[6], line 1
----> 1 train_df_from_saved_coco = from_coco("./df_converted_to_coco.json", root="./")

TypeError: from_coco() got an unexpected keyword argument 'root'

使用 `pd.DataFrame` 格式进行训练¶

首先，让我们导入 MultiModalPredictor

from autogluon.multimodal import MultiModalPredictor

请确保已安装 mmcv 和 mmdet

!mim install mmcv
!pip install "mmdet==3.1.0"

同样，我们遵循 AutoMM 检测 - 在小型 COCO 格式数据集上的快速入门中的模型设置。

checkpoint_name = "yolov3_mobilenetv2_320_300e_coco"
num_gpus = -1  # use all GPUs

import uuid

model_path = f"./tmp/{uuid.uuid4().hex}-df_train_temp_save"
predictor_df = MultiModalPredictor(
    hyperparameters={
        "model.mmdet_image.checkpoint_name": checkpoint_name,
        "env.num_gpus": num_gpus,
    },
    problem_type="object_detection",
    sample_data_path=train_df,  # we specify train_df here as the sample_data_path in order to get the num_classes
    path=model_path,
)

predictor_df.fit(
    train_df,
    hyperparameters={
        "optim.lr": 2e-4, # we use two stage and detection head has 100x lr
        "optim.max_epochs": 30,
        "env.per_gpu_batch_size": 32,  # decrease it when model is large
    },
)

使用 `pd.DataFrame` 格式进行评估¶

我们遵循 :ref:sec_automm_detection_quick_start_coco 中的评估设置。建议您查阅了解更多详情。

要使用 pd.DataFrame 格式评估模型，请运行以下代码。

test_df = from_coco(test_path)
predictor_df.evaluate(test_df)

其他示例¶

您可以访问 AutoMM 示例，探索 AutoMM 的其他示例。

自定义¶

要了解如何自定义 AutoMM，请参阅自定义 AutoMM。

引用¶

@misc{redmon2018yolov3,
    title={YOLOv3: An Incremental Improvement},
    author={Joseph Redmon and Ali Farhadi},
    year={2018},
    eprint={1804.02767},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

AutoMM 检测 - 目标检测数据格式¶

COCO 格式¶

pd.DataFrame 格式¶

使用数据格式训练和评估模型¶

下载数据¶

从 COCO 格式到 pd.DataFrame¶

从 pd.DataFrame 到 COCO 格式¶

使用 pd.DataFrame 格式进行训练¶

使用 pd.DataFrame 格式进行评估¶