Merge branch 'main' of https://github.com/junchen14/MiniGPT-4_finetune into main

dataset/Evaluation.md

@@ -0,0 +1 @@
+

dataset/README_MINIGPTv2_FINETUNE.md

@@ -1,33 +1,17 @@
 ## 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
 
-After downloading all of them, organize the data as follows in `./playground/data`,
-
-```
-├── coco
-│   └── train2017
-├── gqa
-│   └── images
-├── ocr_vqa
-│   └── images
-├── textvqa
-│   └── train_images
-└── vg
-    ├── VG_100K
-    └── VG_100K_2
-```
 
 ### COCO captions
 - [train2017](http://images.cocodataset.org/zips/train2017.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
 
 ### RefCOCO, RefCOCO+, RefCOCOg
-Makesure you have the COCO 2014 images first. 
+Make sure you have the COCO 2014 images first. 
 
 Then,
 download RefCOCO, RefCOCO+, and RefCOCOg annotation files in the following links.
@@ -88,16 +72,6 @@ detail_23k.json, and complex_reasoning_77k.json in conversation.yaml, detail.yam
 - [minigpt4/configs/datasets/llava/reason.yaml](../minigpt4/configs/datasets/llava/reason.yaml)
 
 
-### TextVQA
-- [train_val_images](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip)
-### GQA
-- [images](https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip)
-- [Annotations](https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/testdev_balanced_questions.json)
-
-
-
-### GQA
-
 ### OKVQA
 
 ### AOK-VQA