Prepare datasets for Detic

The basic training of our model uses LVIS (which uses COCO images) and ImageNet-21K. Some models are trained on Conceptual Caption (CC3M). Optionally, we use Objects365 and OpenImages (Challenge 2019 version) for cross-dataset evaluation. Before starting processing, please download the (selected) datasets from the official websites and place or sim-link them under $Detic_ROOT/datasets/.

$Detic_ROOT/datasets/
    metadata/
    lvis/
    coco/
    imagenet/
    cc3m/
    objects365/
    oid/

metadata/ is our preprocessed meta-data (included in the repo). See the below section for details. Please follow the following instruction to pre-process individual datasets.

COCO and LVIS

First, download COCO and LVIS data place them in the following way:

lvis/
    lvis_v1_train.json
    lvis_v1_val.json
coco/
    train2017/
    val2017/
    annotations/
        captions_train2017.json
        instances_train2017.json 
        instances_val2017.json

Next, prepare the open-vocabulary LVIS training set using

python tools/remove_lvis_rare.py --ann datasets/lvis/lvis_v1_train.json

This will generate datasets/lvis/lvis_v1_train_norare.json.

ImageNet-21K

The ImageNet-21K folder should look like:

imagenet/
    ImageNet-21K/
        n01593028.tar
        n01593282.tar
        ...

We first unzip the overlapping classes of LVIS (we will directly work with the .tar file for the rest classes) and convert them into LVIS annotation format.

mkdir imagenet/annotations
python tools/unzip_imagenet_lvis.py --dst_path datasets/imagenet/ImageNet-LVIS
python tools/create_imagenetlvis_json.py --imagenet_path datasets/imagenet/ImageNet-LVIS --out_path datasets/imagenet/annotations/imagenet_lvis_image_info.json

This creates datasets/imagenet/annotations/imagenet_lvis_image_info.json.

[Optional] To train with all the 21K classes, run

python tools/get_imagenet_21k_full_tar_json.py
python tools/create_lvis_21k.py

This creates datasets/imagenet/annotations/imagenet-21k_image_info_lvis-21k.json and datasets/lvis/lvis_v1_train_lvis-21k.json (combined LVIS and ImageNet-21K classes in categories).

[Optional] To train on combined LVIS and COCO, run

python tools/merge_lvis_coco.py

This creates datasets/lvis/lvis_v1_train+coco_mask.json

Conceptual Caption

Download the dataset from this page and place them as:

cc3m/
    GCC-training.tsv

Run the following command to download the images and convert the annotations to LVIS format (Note: download images takes long).

python tools/download_cc.py --ann datasets/cc3m/GCC-training.tsv --save_image_path datasets/cc3m/training/ --out_path datasets/cc3m/train_image_info.json
python tools/get_cc_tags.py

This creates datasets/cc3m/train_image_info_tags.json.

Objects365

Download Objects365 (v2) from the website. We only need the validation set in this project:

objects365/
    annotations/
        zhiyuan_objv2_val.json
    val/
        images/
            v1/
                patch0/
                ...
                patch15/
            v2/
                patch16/
                ...
                patch49/

The original annotation has typos in the class names, we first fix them for our following use of language embeddings.

python tools/fix_o365_names.py --ann datasets/objects365/annotations/zhiyuan_objv2_val.json

This creates datasets/objects365/zhiyuan_objv2_val_fixname.json.

To train on Objects365, download the training images and use the command above. We note some images in the training annotation do not exist. We use the following command to filter the missing images.

python tools/fix_0365_path.py

This creates datasets/objects365/zhiyuan_objv2_train_fixname_fixmiss.json.

OpenImages

We followed the instructions in UniDet to convert the metadata for OpenImages.

The converted folder should look like

oid/
    annotations/
        oid_challenge_2019_train_bbox.json
        oid_challenge_2019_val_expanded.json
    images/
        0/
        1/
        2/
        ...

Open-vocabulary COCO

We first follow OVR-CNN to create the open-vocabulary COCO split. The converted files should be like

coco/
    zero-shot/
        instances_train2017_seen_2.json
        instances_val2017_all_2.json

We further pre-process the annotation format for easier evaluation:

python tools/get_coco_zeroshot_oriorder.py --data_path datasets/coco/zero-shot/instances_train2017_seen_2.json
python tools/get_coco_zeroshot_oriorder.py --data_path datasets/coco/zero-shot/instances_val2017_all_2.json

Next, we preprocess the COCO caption data:

python tools/get_cc_tags.py --cc_ann datasets/coco/annotations/captions_train2017.json --out_path datasets/coco/captions_train2017_tags_allcaps.json --allcaps --convert_caption

This creates datasets/coco/captions_train2017_tags_allcaps.json.

Metadata

metadata/
    lvis_v1_train_cat_info.json
    coco_clip_a+cname.npy
    lvis_v1_clip_a+cname.npy
    o365_clip_a+cnamefix.npy
    oid_clip_a+cname.npy
    imagenet_lvis_wnid.txt
    Objects365_names_fix.csv

lvis_v1_train_cat_info.json is used by the Federated loss. This is created by

python tools/get_lvis_cat_info.py --ann datasets/lvis/lvis_v1_train.json

*_clip_a+cname.npy is the pre-computed CLIP embeddings for each datasets. They are created by (taking LVIS as an example)

python tools/dump_clip_features.py --ann datasets/lvis/lvis_v1_val.json --out_path metadata/lvis_v1_clip_a+cname.npy

Note we do not include the 21K class embeddings due to the large file size. To create it, run

python tools/dump_clip_features.py --ann datasets/lvis/lvis_v1_val_lvis-21k.json --out_path datasets/metadata/lvis-21k_clip_a+cname.npy

imagenet_lvis_wnid.txt is the list of matched classes between ImageNet-21K and LVIS.

Objects365_names_fix.csv is our manual fix of the Objects365 names.