| Data name | Json file | Json_Size | Images_Size |
|---|---|---|---|
| VersaD | list_pretrain.json | 1.02 GB | 140 GB |
| VHM_SFT | list_sft.json | 1.5 GB | 124 GB |
| VHM_Eval | - | - | 7.35 GB |
This dataset is curated from crowdai, cvact, cvusa, fmow, loveda, millionAID, etc, resulting in total 140M high-quality image-text pairs with the help of powerful Gemini-Vision. The dataset is used for the pretraining of VHM.
This dataset contains VersaD-Instruct，HnstD and VariousRS-Instruct sub datasets. The images in these datasets come from public datasets such as BANDON,DOIR,DOTA,FBP,METER-ML,MSAR,Mts-WH,NWPU-RESISC45,RSITMD,RSVQA,UCM,crowdAI,deepglobe,fair1M and fmow, the instruction portion is based on their original labels.
This dataset is a collection of all evaluation data in the paper, including Table 5-9 and Table 11. The specific relationship between tasks and corresponding files is as follows:
| Task name | Question type | Json file |
|---|---|---|
| Honst Tasks | ||
| Honst-presence | open-end | presence_mo_dota.json |
| Honst-object absolute position | multi-choice | abspos_dota-test_mc.json |
| Honst-object absolute position-false | multi-choice | abspos_false_dota-test_adversarial_mc.json abspos_false_dota-test_popular_mc.json abspos_false_dota-test_random_mc.json |
| Honst-object relative position | multi-choice | relpos_dota-test_mc.json |
| Honst-object relative position-false | multi-choice | relpos_false_dota-test_adversarial_mc.json relpos_false_dota-test_popula_mc.json relpos_false_dota-test_random_mc.json |
| Honst-clolor | open-end | color_dota-test_fair1m-val_open.json |
| Honst-color-false | open-end | color_false_dota-test_random_open.json color_false_dota-test_popular_open.json color_false_dota-test_adversarial_open.json |
| Honst-clolor-pan false | open-end | color_false_pan_dota-test.json |
| VariousRS Tasks | ||
| Scene Classification | open-end | cls_WHU_RS19.json cls_SIRI_WHU.json cls_NWPU_RESISC45.json cls_METER_ML.json cls_AID.json |
| Building footprint vectorization | open-end | bfv_crowdai_val.json |
| Counting | open-end | counting_dota-test_open.json |
| Image Resolution | open-end | gsd_dota_fbp.json |
| Image Modality | multi-choice | imgType_mcq.json |
| Multi-label Classification | open-end | mlc_fbp_test.json mlc_gid_test.json |
| Geometric Measurement | open-end | obj_meas_dota_test.json |
| RSVQA-HR* | open-end | RSVQA_HR-comp_RSVQA.json RSVQA_HR-presence_RSVQA.json |
| RSVQA-LR* | open-end | RSVQA_LR-presence_RSVQA.json RSVQA_LR-presence_RSVQA.json RSVQA_LR-rural_urban_RSVQA.json |
| Visual Grounding | open-end | VG_DOIR_RSVG_test.json |
* means that the dataset is a randomly sampled subset; you need to download the entire dataset yourself.
- Please download the VersaD dataset.
- Prepare the datasets according to the file structure shown below, where
pretrain_basedenotes the root directory of the entire pretrain dataset.
{pretrain_base}/
# image dirs
crowdai/
image0.jpg
image1.jpg
...
imagexx.jpg
cvusa/
...
# json files
list_pretrain.json
- Please download the VHM_SFT dataset.
- Prepare the datasets according to the file structure shown below, where
sft_basedenotes the root directory of the entire SFT dataset.
{sft_base}/
# image dirs
BANDON/
image0.jpg
image1.jpg
...
imagexx.jpg
DOTA-train/
...
# json files
list_sft.json
Important notice: For the convenience, we provide a zip file for web data. These images must be used for academic purpose.