MiniGPT-4/dataset/README_MINIGPTv2_FINETUNE.md

## Download the COCO captions, RefCOCO, RefCOCO+. RefCOCOg, visual genome, textcaps, LLaVA, gqa, AOK-VQA, OK-VQA, OCR-VQA, filtered Flickr-30k, multi-task conversation, and Unnatural instruction datasets


Download the dataset

Image source | Download path
--- | :---:
COCO 2014 images | <a href="http://images.cocodataset.org/zips/train2014.zip">images</a> <a href="https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json"> captions</a>
Visual Genome |  <a href="https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip">images part1</a> <a href="https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip">images part2</a>
TextCaps | <a href="https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip">images</a> <a href="https://dl.fbaipublicfiles.com/textvqa/data/textcaps/TextCaps_0.1_train.json"> annotations</a> 
RefCOCO | <a href="https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco.zip"> annotations </a>
RefCOCO+ | <a href="https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco+.zip"> annotations </a>
RefCOCOg | <a href="https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcocog.zip"> annotations </a>
LLaVA | <a href="https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/resolve/main/complex_reasoning_77k.json"> Compelex reasoning </a> <a href="https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/resolve/main/detail_23k.json"> Detailed description </a>  <a href="https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/resolve/main/conversation_58k.json"> Conversation </a> 
OKVQA | <a href="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json"> annotations </a>
AOK-VQA | <a href="https://prior-datasets.s3.us-east-2.amazonaws.com/aokvqa/aokvqa_v1p0.tar.gz"> annotations </a>
OCR-VQA | <a href="https://drive.google.com/drive/folders/1_GYPY5UkUy7HIcR0zq3ZCFgeZN7BAfm_?usp=sharing"> annotations </a>
Filtered Flickr-30k | images:  annotations: <a href="https://drive.google.com/drive/folders/19c_ggBI77AvdtYlPbuI0ZpnPz73T5teX?usp=sharing"> annotations </a>
Multi-task conversation ｜ <a href="https://drive.google.com/file/d/11HHqB2c29hbSk-WLxdta-nG8UCUrcCN1/view?usp=sharing"> annotations </a> 
Filtered unnatural instruction ｜<a href="https://drive.google.com/file/d/1lXNnBcb5WU-sc8Fe2T2N8J0NRw4sBLev/view?usp=sharing"> annotations </a>


.
├── ${MINIGPTv2_DATASET}
│   ├── coco_captions
│       ├── coco_images
|       ├── annotations
|            ├── coco_karpathy_train.json


### COCO captions



Download the COCO 2014 images
- [train2014](http://images.cocodataset.org/zips/train2014.zip)




### Visual genome

- [part1](https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip), [part2](https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip)

### TextCaps

- [TextCaps_0.1_train](https://dl.fbaipublicfiles.com/textvqa/data/textcaps/TextCaps_0.1_train.json)
- [Images](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip)

### RefCOCO, RefCOCO+, RefCOCOg
Make sure you have the COCO 2014 images first. 

Then,
download RefCOCO, RefCOCO+, and RefCOCOg annotation files in the following links.

- https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco.zip
- https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco+.zip
- https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcocog.zip

Unzip these files to the location you like. It should have the structure like the following

```
Location_you_like
├── refcoco
│   ├── instances.json
│   ├── refs(google).p
│   └── refs(unc).p
├── refcoco+
│   ├── instances.json
│   └── refs(unc).p
└── refcocog
    ├── instances.json
    ├── refs(google).p
    └── refs(umd).p
```

Set **image_path** in all the following dataset configuration files to the COCO 2014 image folder.
Similarly, set **ann_path** in all the following configs to the above folder (Location_you_like) that contains refcoco, refcoco+, and refcocog.

- [minigpt4/configs/datasets/coco_bbox/refcoco.yaml](../minigpt4/configs/datasets/coco_bbox/refcoco.yaml)
- [minigpt4/configs/datasets/coco_bbox/refcocog.yaml](../minigpt4/configs/datasets/coco_bbox/refcocog.yaml) 
- [minigpt4/configs/datasets/coco_bbox/refcocop.yaml](../minigpt4/configs/datasets/coco_bbox/refcocop.yaml)
- [minigpt4/configs/datasets/coco_bbox/invrefcoco.yaml](../minigpt4/configs/datasets/coco_bbox/invrefcoco.yaml)
- [minigpt4/configs/datasets/coco_bbox/invrefcocog.yaml](../minigpt4/configs/datasets/coco_bbox/invrefcocog.yaml) 
- [minigpt4/configs/datasets/coco_bbox/invrefcocop.yaml](../minigpt4/configs/datasets/coco_bbox/invrefcocop.yaml)

### LLaVA
Makesure you have the COCO 2014 images first. 

Download Llava annotation files in the following link to the place you like.

- https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/resolve/main/conversation_58k.json
- https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/resolve/main/detail_23k.json
- https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/resolve/main/complex_reasoning_77k.json

Set **image_path** in all the following dataset configuration files to the COCO 2014 image folder.
Similarly, set **ann_path** to the location of the previous downloaded conversation_58k.json, 
detail_23k.json, and complex_reasoning_77k.json in conversation.yaml, detail.yaml, and reason.yaml, respectively.


- [minigpt4/configs/datasets/llava/conversation.yaml](../minigpt4/configs/datasets/llava/conversation.yaml)
- [minigpt4/configs/datasets/llava/detail.yaml](../minigpt4/configs/datasets/llava/detail.yaml) 
- [minigpt4/configs/datasets/llava/reason.yaml](../minigpt4/configs/datasets/llava/reason.yaml)





### OKVQA

- [OK-VQA Input Questions](https://okvqa.allenai.org/static/data/OpenEnded_mscoco_train2014_questions.json.zip)
- [OK-VQA Annotations](https://okvqa.allenai.org/static/data/mscoco_train2014_annotations.json.zip)

- [okvqa_train](https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json)
- Images are from COCO

### AOK-VQA

```
export AOKVQA_DIR=YOUR_DATASET_PATH
mkdir -p ${AOKVQA_DIR}
curl -fsSL https://prior-datasets.s3.us-east-2.amazonaws.com/aokvqa/aokvqa_v1p0.tar.gz | tar xvz -C ${AOKVQA_DIR}
```

### OCR-VQA
- [download script](https://drive.google.com/drive/folders/1_GYPY5UkUy7HIcR0zq3ZCFgeZN7BAfm_?usp=sharing), **we save all files as `.jpg`**

### filtered Flickr-30k


### Multi-task conversation

### Unnatural instruction