mirror of https://github.com/YifanXu74/MQ-Det.git
74 lines
2.9 KiB
Markdown
74 lines
2.9 KiB
Markdown
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We provide guidance for preparing the data used by MQ-DET. Note that not all data are needed for a specific experiments. Please check the `` Required Data`` fields in [README](README.md) to download necessary data. All data should by placed under the ``DATASET`` folder.
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The data should be organized in the following format:
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```
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DATASET/
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coco/
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annotations/
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lvis_od_train.json
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lvis_od_val.json
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lvis_v1_minival_inserted_image_name.json
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train2017/
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val2017/
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test2017/
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Objects365/
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images/
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zhiyuan_objv2_train.json
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odinw/
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AerialMaritimeDrone/
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...
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WildfireSmoke/
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```
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#### ``Objects365``
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We found that the Objects365 v1 is unavailable now. Please try to download v2 as follows.
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Download the [Objects365](https://www.objects365.org/overview.html) dataset from [YOLOv5](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml).
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You can also use custom datasets for modulated pre-training as long as they are in COCO format.
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#### ``LVIS``
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LVIS use the same images as COCO. Thus prepare the COCO images and annoations first and place them at ``DATASET/coco/``.
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**All processed LVIS annotation files can be downloaded through:**
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|train|minival|val 1.0|
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|-----|-------|-------|
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|[link](https://drive.google.com/file/d/1UpLRWfvXnGrRrhniKuiX_E1bkT90yZVE/view?usp=sharing)|[link](https://drive.google.com/file/d/1lLN9wole5yAsatFpYLnlnFEgcbDLXTfH/view?usp=sharing)|[link](https://drive.google.com/file/d/1BxlNOXEkcwsY2w2QuKdA2bdrrKCGv08J/view?usp=sharing)|
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And place them at ``DATASET/coco/annotations/``.
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**If you want to process by yourself rather than using the pre-processed files**, please follow the [instruction in GLIP](https://github.com/microsoft/GLIP/blob/main/DATA.md), summarized as following.
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Download the following annotation files:
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```
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wget https://penzhanwu2bbs.blob.core.windows.net/data/GLIPv1_Open/coco/annotations/lvis_v1_minival_inserted_image_name.json -O DATASET/coco/annotations/lvis_v1_minival_inserted_image_name.json
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wget https://penzhanwu2bbs.blob.core.windows.net/data/GLIPv1_Open/coco/annotations/lvis_od_val.json -O coco/annotations/lvis_od_val.json"
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```
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Also download the training set for extracting vision queries:
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```
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wget https://s3-us-west-2.amazonaws.com/dl.fbaipublicfiles.com/LVIS/lvis_v1_train.json.zip -O coco/annotations/lvis_v1_train.json.zip
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```
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Unpack the .zip file to ``coco/annotations/lvis_v1_train.json``, and convert it to coco format:
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```
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python utils/add_file_name.py
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```
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#### ``Object Detection in the Wild (ODinW)``
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**Download ODinW**
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```
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python odinw/download_datasets.py
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```
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``configs/odinw_35`` contain all the meta information of the datasets. ``configs/odinw_13`` are the datasets used by GLIP. Each dataset follows the coco detection format.
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Please refer to [GLIP](https://github.com/microsoft/GLIP/tree/main) for more details.
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