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prepare dataset readme

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.gitignore vendored Executable file
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dataset/README_MINIGPTv2_FINETUNE.md Normal file
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## 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
### COCO captions
### RefCOCO, RefCOCO+, RefCOCOg
### Visual genome
### textcaps
### LLaVA
### gqa
### OKVQA
### AOK-VQA
### OCR-VQA
### filtered Flickr-30k
### Multi-task conversation
### Unnatural instruction
### Pre-training datasets download:
We use the filtered synthetic captions prepared by BLIP. For more details about the dataset, please refer to [BLIP](https://github.com/salesforce/BLIP).
It requires ~2.3T to store LAION and CC3M+CC12M+SBU datasets
Image source | Filtered synthetic caption by ViT-L
--- | :---:
CC3M+CC12M+SBU | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/ccs_synthetic_filtered_large.json">Download</a>
LAION115M | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/laion_synthetic_filtered_large.json">Download</a>
This will download two json files
```
ccs_synthetic_filtered_large.json
laion_synthetic_filtered_large.json
```
## prepare the data step-by-step
### setup the dataset folder and move the annotation file to the data storage folder
```
export MINIGPT4_DATASET=/YOUR/PATH/FOR/LARGE/DATASET/
mkdir ${MINIGPT4_DATASET}/cc_sbu
mkdir ${MINIGPT4_DATASET}/laion
mv ccs_synthetic_filtered_large.json ${MINIGPT4_DATASET}/cc_sbu
mv laion_synthetic_filtered_large.json ${MINIGPT4_DATASET}/laion
```
### Convert the scripts to data storate folder
```
cp convert_cc_sbu.py ${MINIGPT4_DATASET}/cc_sbu
cp download_cc_sbu.sh ${MINIGPT4_DATASET}/cc_sbu
cp convert_laion.py ${MINIGPT4_DATASET}/laion
cp download_laion.sh ${MINIGPT4_DATASET}/laion
```
### Convert the laion and cc_sbu annotation file format to be img2dataset format
```
cd ${MINIGPT4_DATASET}/cc_sbu
python convert_cc_sbu.py
cd ${MINIGPT4_DATASET}/laion
python convert_laion.py
```
### Download the datasets with img2dataset
```
cd ${MINIGPT4_DATASET}/cc_sbu
sh download_cc_sbu.sh
cd ${MINIGPT4_DATASET}/laion
sh download_laion.sh
```
The final dataset structure
```
.
├── ${MINIGPT4_DATASET}
│ ├── cc_sbu
│ ├── convert_cc_sbu.py
│ ├── download_cc_sbu.sh
│ ├── ccs_synthetic_filtered_large.json
│ ├── ccs_synthetic_filtered_large.tsv
│ └── cc_sbu_dataset
│ ├── 00000.tar
│ ├── 00000.parquet
│ ...
│ ├── laion
│ ├── convert_laion.py
│ ├── download_laion.sh
│ ├── laion_synthetic_filtered_large.json
│ ├── laion_synthetic_filtered_large.tsv
│ └── laion_dataset
│ ├── 00000.tar
│ ├── 00000.parquet
│ ...
...
```
## Set up the dataset configuration files
Then, set up the LAION dataset loading path in
[here](../minigpt4/configs/datasets/laion/defaults.yaml#L5) at Line 5 as
${MINIGPT4_DATASET}/laion/laion_dataset/{00000..10488}.tar
and the Conceptual Captoin and SBU datasets loading path in
[here](../minigpt4/configs/datasets/cc_sbu/defaults.yaml#L5) at Line 5 as
${MINIGPT4_DATASET}/cc_sbu/cc_sbu_dataset/{00000..01255}.tar

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train_configs/minigpt_v2_finetune.yaml
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@ -29,244 +29,244 @@ datasets:
name: "blip_caption"
sample_ratio: 40
# llava_conversation: # 77k
# batch_size: 2
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 10
llava_conversation: # 77k
batch_size: 2
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 10
# unnatural_instruction:
# batch_size: 1
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 15
unnatural_instruction:
batch_size: 1
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 15
# refvg:
# batch_size: 6
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 40
refvg:
batch_size: 6
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 40
# llava_detail: #23K
# batch_size: 4
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 20
llava_detail: #23K
batch_size: 4
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 20
# llava_reason: # 77k
# batch_size: 4
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 80
llava_reason: # 77k
batch_size: 4
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 80
# flickr_grounded_caption:
# batch_size: 2
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 80
flickr_grounded_caption:
batch_size: 2
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 80
# flickr_CaptionToPhrase:
# batch_size: 2
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 80
flickr_CaptionToPhrase:
batch_size: 2
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 80
# flickr_ObjectToPhrase:
# batch_size: 2
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 80
flickr_ObjectToPhrase:
batch_size: 2
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 80
# coco_caption:
# batch_size: 6
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 10
coco_caption:
batch_size: 6
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 10
# textcaps_caption: #
# batch_size: 6
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 10
textcaps_caption: #
batch_size: 6
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 10
# refcoco: # 142k
# batch_size: 6
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 15
refcoco: # 142k
batch_size: 6
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 15
# refcocop:
# batch_size: 6
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 15
refcocop:
batch_size: 6
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 15
# refcocog:
# batch_size: 6
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 15
refcocog:
batch_size: 6
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 15
# invrefcoco:
# batch_size: 6
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 10
invrefcoco:
batch_size: 6
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 10
# invrefcocop:
# batch_size: 6
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 10
invrefcocop:
batch_size: 6
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 10
# invrefcocog:
# batch_size: 6
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 10
invrefcocog:
batch_size: 6
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 10
# coco_vqa: # 82K
# batch_size: 6
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 15
coco_vqa: # 82K
batch_size: 6
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 15
# ok_vqa: # 9k
# batch_size: 6
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 8
ok_vqa: # 9k
batch_size: 6
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 8
# aok_vqa: # 17k
# batch_size: 6
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 12
aok_vqa: # 17k
batch_size: 6
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 12
# gqa: # 82K
# batch_size: 6
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 40
gqa: # 82K
batch_size: 6
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 40
# ocrvqa: # 800K
# batch_size: 6
# vis_processor:
# train:
# name: "blip2_image_train"
# image_size: 448
# text_processor:
# train:
# name: "blip_caption"
# sample_ratio: 30
ocrvqa: # 800K
batch_size: 6
vis_processor:
train:
name: "blip2_image_train"
image_size: 448
text_processor:
train:
name: "blip_caption"
sample_ratio: 30
run:
@ -285,7 +285,7 @@ run:
iters_per_epoch: 1000
seed: 42
output_dir: "/ibex/project/c2090/minigpt4_ckpt/448_conversation_correct_best_v7_ablation1_v5_v6_again_system_prompt"
output_dir: "/ibex/project/c2090/minigpt4_ckpt/448_finetune_test_online"
amp: True
resume_ckpt_path: null