Update README.md

README.md

@@ -19,8 +19,9 @@ tags:
 
 [![github](https://img.shields.io/badge/GitHub-TinyLLaVA-blue)](https://github.com/DLCV-BUAA/TinyLLaVABench) [![arXiv](https://img.shields.io/badge/Arxiv-2402.14289-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2402.14289) [![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](https://github.com/PKU-YuanGroup/MoE-LLaVA/blob/main/LICENSE) 
 
-
 ## 🎉 News
+* **[2024.03.10]**  base recipe out!
+* **[2024.03.10]**  Finetune scripts out!
 * **[2024.02.25]**  Update evaluation scripts and docs!
 * **[2024.02.25]**  Data descriptions out. Release TinyLLaVA-1.5B and TinyLLaVA-2.0B!
 * **[2024.02.24]**  Example code on inference and model loading added!
@@ -31,7 +32,8 @@ tags:
 
 ## ⌛ TODO
 - [ ] Add support for Ollama and llama.cpp.
-- [ ] Developers' guide / How to build demo locally.
+- [x] Developers' guide / How to build demo locally.
+- [x] Training and custom finetuning docs.
 - [x] Model Zoo descriptions.
 - [x] Examples and inference.
 - [x] Release code for training.
@@ -44,22 +46,17 @@ tags:
 
 - Our best model, TinyLLaVA-3.1B, achieves better overall performance against existing 7B models such as LLaVA-1.5 and Qwen-VL.
 
-## 🐳 Model Zoo
-### Legacy Model
-- [tiny-llava-hf](https://huggingface.co/bczhou/tiny-llava-v1-hf)
-
-### Pretrained Models
-- [TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B)
-- [TinyLLaVA-2.0B](https://huggingface.co/bczhou/TinyLLaVA-2.0B)
-- [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B)
-
-### Model Details
-| Name          | LLM               | Checkpoint                                     | LLaVA-Bench-Wild | MME      | MMBench | MM-Vet | SQA-image | VQA-v2 | GQA   | TextVQA |
-|---------------|-------------------|------------------------------------------------|------------------|----------|---------|--------|-----------|--------|-------|---------|
-| TinyLLaVA-3.1B | Phi-2             | [TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B) | 75.8             | 1464.9   | 66.9    | 32.0   | 69.1      | 79.9   | 62.0  | 59.1    |
-| TinyLLaVA-2.0B | StableLM-2-1.6B   | [TinyLLaVA-2.0B](https://huggingface.co/bczhou/TinyLLaVA-2.0B) | 66.4             | 1433.8     | 63.3    | 32.6   | 64.7      | 78.9   | 61.9  | 56.4    |
-| TinyLLaVA-1.5B | TinyLlama         | [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B) | 60.8             | 1276.5     | 55.2     | 25.8   | 60.3      | 76.9   | 60.3  | 51.7    |
+## Contents
 
+- [Install](#x1f527-requirements-and-installation)
+- [Model Zoo](#x1f433-model-zoo)
+- [Demo](#Demo)
+- [Quick Start](#x1f527-quick-start)
+- [Run Inference](#x1f527-run-inference)
+- [Evaluation](#evaluation)
+- [Data](#data-preparation)
+- [Train](#train)
+- [Custom Finetune](#custom-finetune)
 
 
 ## 🔧 Requirements and Installation
@@ -85,7 +82,7 @@ pip install -e .
 pip install -e ".[train]"
 pip install flash-attn --no-build-isolation
 ```
-### Upgrade to latest code base
+### Upgrade to the latest code base
 
 ```Shell
 git pull
@@ -95,6 +92,41 @@ pip install -e .
 # pip install flash-attn --no-build-isolation --no-cache-dir
 ```
 
+## 🐳 Model Zoo
+### Legacy Model
+- [tiny-llava-hf](https://huggingface.co/bczhou/tiny-llava-v1-hf)
+
+### Pretrained Models
+- [TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B)
+- [TinyLLaVA-2.0B](https://huggingface.co/bczhou/TinyLLaVA-2.0B)
+- [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B)
+
+### Model Details
+| Name          | LLM               | Checkpoint                                     | LLaVA-Bench-Wild | MME      | MMBench | MM-Vet | SQA-image | VQA-v2 | GQA   | TextVQA |
+|---------------|-------------------|------------------------------------------------|------------------|----------|---------|--------|-----------|--------|-------|---------|
+| TinyLLaVA-3.1B | Phi-2             | [TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B) | 75.8             | 1464.9   | 66.9    | 32.0   | 69.1      | 79.9   | 62.0  | 59.1    |
+| TinyLLaVA-2.0B | StableLM-2-1.6B   | [TinyLLaVA-2.0B](https://huggingface.co/bczhou/TinyLLaVA-2.0B) | 66.4             | 1433.8     | 63.3    | 32.6   | 64.7      | 78.9   | 61.9  | 56.4    |
+| TinyLLaVA-1.5B | TinyLlama         | [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B) | 60.8             | 1276.5     | 55.2     | 25.8   | 60.3      | 76.9   | 60.3  | 51.7    |
+
+
+## Demo
+
+### Gradio Web Demo
+
+Launch a local web demo by running:
+```shell
+python tinyllava/serve/app.py --model-path bczhou/TinyLLaVA-3.1B --model-name TinyLLaVA-3.1B
+```
+
+### CLI Inference
+
+We also support running inference with CLI. To use our model, run:
+```shell
+python -m tinyllava.serve.cli \
+    --model-path bczhou/TinyLLaVA-3.1B \
+    --image-file "./tinyllava/serve/examples/extreme_ironing.jpg" 
+```
+
 
 ## 🔧 Quick Start
 
@@ -151,7 +183,7 @@ eval_model(args)
 ### Important
 We use different `conv_mode` for different models. Replace the `conv_mode` in `args` according to this table:
 | model          	| conv_mode 	|
-|-------------------|---------------|
+|----------------	|-----------	|
 | TinyLLaVA-3.1B 	| phi       	|
 | TinyLLaVA-2.0B 	| phi       	|
 | TinyLLaVA-1.5B 	| v1        	|
@@ -161,6 +193,115 @@ To ensure the reproducibility, we evaluate the models with greedy decoding.
 
 See [Evaluation.md](https://github.com/DLCV-BUAA/TinyLLaVABench/blob/main/docs/Evaluation.md)
 
+## Data Preparation
+
+In our paper, we used two different datasets: the [LLaVA dataset](https://github.com/haotian-liu/LLaVA?tab=readme-ov-file#pretrain-feature-alignment) and the [ShareGPT4V dataset](https://github.com/InternLM/InternLM-XComposer/blob/main/projects/ShareGPT4V/docs/Data.md), and compared their differences. In this section, we provide information on data preparation.
+
+### Pretraining Images
+* LLaVA: The pretraining images of LLaVA is from the 558K subset of the LAION-CC-SBU dataset.
+* ShareGPT4V: The pretraining images of ShareGPT4V is a mixture of 558K LAION-CC-SBU subset, SAM dataset, and COCO dataset.
+
+### Pretraining Annotations
+* LLaVA: The pretraining annotations of LLaVA are [here](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain).
+* ShareGPT4V: The pretraining annotations of ShareGPT4V are [here](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/share-captioner_coco_lcs_sam_1246k_1107.json).
+
+
+### SFT Images & Annotations
+The majority of the two SFT datasets are the same, with the exception that the 23K detailed description data in LLaVA-1.5-SFT being replaced with detailed captions randomly sampled from the [100K ShareGPT4V data](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/sharegpt4v_instruct_gpt4-vision_cap100k.json).
+
+### Download data
+
+1. Download relevant images
+
+- LAION-CC-SBU-558K: [images.zip](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain/blob/main/images.zip)
+- COCO: This dataset is from the [COCO2017 challenge](https://cocodataset.org/). Download: [train2017](http://images.cocodataset.org/zips/train2017.zip)
+- WebData: This dataset is curated by the [ShareGPT4V project](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V). Download: [images](https://drive.google.com/drive/folders/1tCUQ-sq6vdshZVkF0ZeF3K4eztkXJgax?usp=sharing). Only for academic usage.
+- SAM: This dataset is collected by [Meta](https://ai.meta.com/datasets/segment-anything-downloads/). Download: [images](https://ai.meta.com/datasets/segment-anything-downloads/). We only use 000000~000050.tar for now. If you just want to use ShareGPT4V for SFT, you can quickly download 9K images from [here](https://drive.google.com/file/d/1dKumdOKSXtV7lIXdrG7jsIK_z2vZv2gs/view?usp=drive_link).
+- GQA: [GQA project page](https://cs.stanford.edu/people/dorarad/gqa/about.html). Download: [images](https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip)
+- OCR-VQA: [OCR-VQA project page](https://ocr-vqa.github.io/). Download: [download script](https://drive.google.com/drive/folders/1_GYPY5UkUy7HIcR0zq3ZCFgeZN7BAfm_?usp=sharing). We save all files as `.jpg`
+- TextVQA: [TextVQA project page](https://textvqa.org/). Download: [trainvalimages](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip)
+- VisualGenome: [VisualGenome project page](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html). Download: [part1](https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip), [part2](https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip)
+
+
+2. Download relevant annotations
+
+- LLaVA's pretraining annotations: [blip_laion_cc_sbu_558k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain)
+- LLaVA's SFT annotations: [llava_v1_5_mix665k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json)
+- ShareGPT4V's pretraining annotations: [share-captioner_coco_lcs_sam_1246k_1107.json](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/share-captioner_coco_lcs_sam_1246k_1107.json)
+- ShareGPT4V's SFT annotations: [sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json)
+
+
+### Organize Data
+
+Organize the image files and annotation files as follows in `path/to/your/data`:
+
+```none
+data
+├── llava
+│   ├── llava_pretrain
+│   │   ├── images
+│   │   ├── blip_laion_cc_sbu_558k.json
+├── coco
+│   ├── train2017
+├── sam
+│   ├── images
+├── gqa
+│   ├── images
+├── ocr_vqa
+│   ├── images
+├── textvqa
+│   ├── train_images
+├── vg
+│   ├── VG_100K
+│   ├── VG_100K_2
+├── share_textvqa
+│   ├── images
+├── web-celebrity
+│   ├── images
+├── web-landmark
+│   ├── images
+├── wikiart
+│   ├── images
+├── text_files
+│   ├── llava_v1_5_mix665k.json
+│   ├── share-captioner_coco_lcs_sam_1246k_1107.json
+│   ├── sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json
+```
+
+## Train
+
+**This section we describe the base recipe.**
+### Hyperparameters
+Both hyperparameters used in pretraining and finetuning are provided below.
+
+1. Pretraining
+
+| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
+|----------------| ---: | ---: | ---: |-----------:| ---: |
+| TinyLLaVA-3.1B | 256 | 1e-3 | 1 |       3072 | 0 |
+
+2. Finetuning
+
+| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
+|----------------| ---: | ---: | ---: |-----------:| ---: |
+| TinyLLaVA-3.1B | 128 | 2e-5 | 1 |       3072 | 0 |
+
+### Pretrain
+
+**Replace paths to your paths**
+
+Training script with DeepSpeed ZeRO-2: [`pretrain.sh`](https://github.com/DLCV-BUAA/TinyLLaVABench/blob/main/scripts/tiny_llava/pretrain.sh).
+
+### Finetune
+
+**Replace paths to your paths**
+
+Training script with DeepSpeed ZeRO-3: [`finetune.sh`](https://github.com/DLCV-BUAA/TinyLLaVABench/blob/main/scripts/tiny_llava/finetune.sh).
+
+## Custom-Finetune
+
+Check out our custom finetune using LoRA [here](https://github.com/DLCV-BUAA/TinyLLaVABench/blob/dev/docs/CUTOM_FINETUNE.md).
+
 
 ## ✏ Citation
 
@@ -176,3 +317,8 @@ If you find our paper and code useful in your research, please consider giving a
       primaryClass={cs.LG}
 }
 ```
+
+
+## ❤️ Community efforts
+* Our codebase is built upon the [LLaVA](https://github.com/haotian-liu/LLaVA) project. Great work!
+* Our project uses data from the [ShareGPT4V](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V) project. Great work!