# InternVL Stage-2 Pre-training & Retrieval Fine-tuning This folder contains the implementation of the InternVL 1.0 for stage2 pre-training and retrieval fine-tuning, which corresponds to Section 4.3 of our [InternVL 1.0 paper](https://arxiv.org/pdf/2312.14238). ![image](https://github.com/user-attachments/assets/239f38b2-8867-4539-9dd8-c1a1eaa40aef) ## 🛠️ Installation Follow the [installation guide](../INSTALLATION.md) to perform installations. ## 📦 Data Preparation Three datasets need to be prepared: COCO Caption, Flickr30K, and NoCaps.
COCO Caption ```bash mkdir -p data/coco && cd data/coco # download coco images wget http://images.cocodataset.org/zips/train2014.zip && unzip train2014.zip wget http://images.cocodataset.org/zips/val2014.zip && unzip val2014.zip wget http://images.cocodataset.org/zips/test2015.zip && unzip test2015.zip mkdir -p annotations && cd annotations/ # download converted annotation files wget https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json wget https://github.com/OpenGVLab/InternVL/releases/download/data/coco_karpathy_test.json wget https://github.com/OpenGVLab/InternVL/releases/download/data/coco_karpathy_test_gt.json cd ../../../ ```
Flickr30K ```bash mkdir -p data/flickr30k && cd data/flickr30k # download images from https://bryanplummer.com/Flickr30kEntities/ # karpathy split annotations can be downloaded from the following link: # https://github.com/mehdidc/retrieval_annotations/releases/download/1.0.0/flickr30k_test_karpathy.txt # this file is provided by the clip-benchmark repository. # We convert this txt file to json format, download the converted file: wget https://github.com/OpenGVLab/InternVL/releases/download/data/flickr30k_cn_test.txt wget https://github.com/OpenGVLab/InternVL/releases/download/data/flickr30k_cn_train.txt wget https://github.com/OpenGVLab/InternVL/releases/download/data/flickr30k_test_karpathy.json wget https://github.com/mehdidc/retrieval_annotations/releases/download/1.0.0/flickr30k_test_karpathy.txt wget https://github.com/mehdidc/retrieval_annotations/releases/download/1.0.0/flickr30k_train_karpathy.txt wget https://github.com/mehdidc/retrieval_annotations/releases/download/1.0.0/flickr30k_val_karpathy.txt cd ../.. ```
NoCaps ```bash mkdir -p data/nocaps && cd data/nocaps # download images from https://nocaps.org/download # original annotations can be downloaded from https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json wget https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json cd ../.. ```
After the download is complete, the directory structure is: ```shell data ├── coco │   ├── annotations │   │   ├── coco_karpathy_train.json │   ├── test2017 │   ├── train2014 │   ├── train2017 │   ├── val2014 │   └── val2017 ├── flickr30k │   ├── flickr30k_cn_test.txt │   ├── flickr30k_cn_train.txt │   ├── flickr30k_test_karpathy.json │   ├── flickr30k_test_karpathy.txt │   ├── flickr30k_train_karpathy.txt │   ├── flickr30k_val_karpathy.txt │   └── Images └── nocaps ├── images └── nocaps_val_4500_captions.json ``` ## 📦 Model Preparation | model name | type | download | size | | ------------------ | ----------- | ----------------------------------------------------------------- | :-----: | | InternVL-14B-224px | huggingface | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-14B-224px) | 27.7 GB | Please download the above model weights and place them in the `pretrained/` folder. ```sh cd pretrained/ # pip install -U huggingface_hub huggingface-cli download --resume-download --local-dir-use-symlinks False OpenGVLab/InternVL-14B-224px --local-dir InternVL-14B-224px ``` The directory structure is: ```sh pretrained └── InternVL-14B-224px/ ``` ## 🔥 Generative Pre-training There are currently no plans to release this part of the code. ## 📊 Evaluation ### Zero-Shot Image Captioning | model | dataset | BLEU4 | METEOR | CIDEr | | ---------- | ----------------------- | ----- | ------ | ----- | | InternVL-G | COCO Karpathy test | 37.1 | 30.1 | 128.2 | | InternVL-G | Flickr30K Karpathy test | 27.0 | 25.3 | 79.2 | | InternVL-G | NoCaps val | 44.3 | 30.1 | 113.7 |
[InternVL-G] COCO Karpathy test ```bash sh evaluate.sh pretrained/InternVL-14B-224px caption-coco ``` Expected results: ``` ['coco', 'English caption:', 10.5974, dict_items([('Bleu_1', 0.7876323287981284), ('Bleu_2', 0.6353512494727918), ('Bleu_3', 0.49108984183589743), ('Bleu_4', 0.37062736733849205), ('METEOR', 0.30106315496945923), ('ROUGE_L', 0.5898249189475652), ('CIDEr', 1.281844384075423)])] ```
[InternVL-G] Flickr30K Karpathy test ``` sh evaluate.sh pretrained/InternVL-14B-224px caption-flickr30k ``` Expected results: ```bash ['flickr30k', 'English caption:', 10.666, dict_items([('Bleu_1', 0.7182900534357628), ('Bleu_2', 0.5353390037921949), ('Bleu_3', 0.3834462132295285), ('Bleu_4', 0.2702131471765472), ('METEOR', 0.25263515267930103), ('ROUGE_L', 0.5305876871149064), ('CIDEr', 0.7919734768328237)])] ```
[InternVL-G] NoCaps val ```bash sh evaluate.sh pretrained/InternVL-14B-224px caption-nocaps ``` Expected results: ``` ['nocaps', 'English caption:', 10.463111111111111, dict_items([('Bleu_1', 0.8518290482155187), ('Bleu_2', 0.7165227921485106), ('Bleu_3', 0.5733723839888316), ('Bleu_4', 0.44268902150723105), ('METEOR', 0.30078174807736896), ('ROUGE_L', 0.6070208063052156), ('CIDEr', 1.1371742045267772)])] ```
### Fine-tuned Image-Text Retrieval #### Flickr30K fine-tuned model: [InternVL-14B-Flickr30K-FT-364px](https://huggingface.co/OpenGVLab/InternVL-14B-Flickr30K-FT-364px)
model Flickr30K avg
image-to-text text-to-image
R@1 R@5 R@10 R@1 R@5 R@10
InternVL-C-FT 97.2 100.0 100.0 88.5 98.4 99.2 97.2
InternVL-G-FT 97.9 100.0 100.0 89.6 98.6 99.2 97.6
[InternVL-C-FT] Flickr30K ```bash cd ../clip_benchmark/ CUDA_VISIBLE_DEVICES=0 python3 clip_benchmark/cli.py eval --model_type internvl --language "en" --task "zeroshot_retrieval" \ --dataset "flickr30k" --dataset_root ./data/flickr30k --model internvl_c_retrieval_hf \ --pretrained ./work_dirs/internvl_stage2_finetune_flickr_364_bs1024_ep10/ --output result_ft.json ``` Expected results: ``` {"dataset": "flickr30k", "model": "internvl_c_retrieval_hf", "pretrained": "./work_dirs/internvl_stage2_finetune_flickr_364_bs1024_ep10", "task": "zeroshot_retrieval", "metrics": {"image_retrieval_recall@1": 0.8853999972343445, "text_retrieval_recall@1": 0.972000002861023, "image_retrieval_recall@5": 0.9836000204086304, "text_retrieval_recall@5": 1.0, "image_retrieval_recall@10": 0.9923999905586243, "text_retrieval_recall@10": 1.0}, "language": "en"} ```
[InternVL-G-FT] Flickr30K ```bash cd ../clip_benchmark/ CUDA_VISIBLE_DEVICES=0 python3 clip_benchmark/cli.py eval --model_type internvl --language "en" --task "zeroshot_retrieval" \ --dataset "flickr30k" --dataset_root ./data/flickr30k --model internvl_g_retrieval_hf \ --pretrained ./work_dirs/internvl_stage2_finetune_flickr_364_bs1024_ep10/ --output result_ft.json ``` Expected results: ``` {"dataset": "flickr30k", "model": "internvl_g_retrieval_hf", "pretrained": "./work_dirs/internvl_stage2_finetune_flickr_364_bs1024_ep10", "task": "zeroshot_retrieval", "metrics": {"image_retrieval_recall@1": 0.895799994468689, "text_retrieval_recall@1": 0.9789999723434448, "image_retrieval_recall@5": 0.9861999750137329, "text_retrieval_recall@5": 1.0, "image_retrieval_recall@10": 0.9922000169754028, "text_retrieval_recall@10": 1.0}, "language": "en"} ```
#### Flickr30K-CN fine-tuned model: [InternVL-14B-FlickrCN-FT-364px](https://huggingface.co/OpenGVLab/InternVL-14B-FlickrCN-FT-364px)
model Flickr30K-CN avg
image-to-text text-to-image
R@1 R@5 R@10 R@1 R@5 R@10
InternVL-C-FT 96.5 99.9 100.0 85.2 97.0 98.5 96.2
InternVL-G-FT 96.9 99.9 100.0 85.9 97.1 98.7 96.4
[InternVL-C-FT] Flickr30K-CN ```bash cd ../clip_benchmark/ CUDA_VISIBLE_DEVICES=0 python3 clip_benchmark/cli.py eval --model_type internvl --language "cn" --task "zeroshot_retrieval" \ --dataset "flickr30k" --dataset_root ./data/flickr30k --model internvl_c_retrieval_hf \ --pretrained ./work_dirs/internvl_stage2_finetune_flickrcn_364_bs1024_ep10/ --output result_ft.json ``` Expected results: ``` {"dataset": "flickr30k", "model": "internvl_c_retrieval_hf", "pretrained": "./work_dirs/internvl_stage2_finetune_flickrcn_364_bs1024_ep10", "task": "zeroshot_retrieval", "metrics": {"image_retrieval_recall@1": 0.8521999716758728, "text_retrieval_recall@1": 0.9649999737739563, "image_retrieval_recall@5": 0.9697999954223633, "text_retrieval_recall@5": 0.9990000128746033, "image_retrieval_recall@10": 0.9854000210762024, "text_retrieval_recall@10": 1.0}, "language": "cn"} ```
[InternVL-G-FT] Flickr30K-CN ```bash cd ../clip_benchmark/ CUDA_VISIBLE_DEVICES=0 python3 clip_benchmark/cli.py eval --model_type internvl --language "cn" --task "zeroshot_retrieval" \ --dataset "flickr30k" --dataset_root ./data/flickr30k --model internvl_g_retrieval_hf \ --pretrained ./work_dirs/internvl_stage2_finetune_flickrcn_364_bs1024_ep10/ --output result_ft.json ``` Expected results: ``` {"dataset": "flickr30k", "model": "internvl_g_retrieval_hf", "pretrained": "./work_dirs/internvl_stage2_finetune_flickrcn_364_bs1024_ep10", "task": "zeroshot_retrieval", "metrics": {"image_retrieval_recall@1": 0.8587999939918518, "text_retrieval_recall@1": 0.968999981880188, "image_retrieval_recall@5": 0.9714000225067139, "text_retrieval_recall@5": 0.9990000128746033, "image_retrieval_recall@10": 0.9865999817848206, "text_retrieval_recall@10": 1.0}, "language": "cn"} ```
## 🔥 Retrieval Fine-tuning (Fully) > Note: In our experiments, full parameter fine-tuning achieves the best results on image-text retrieval tasks in Flickr30K and COCO. By following the experimental hyperparameters in this section, you can reproduce the model performance reported in the [Evaluation section](#evaluation). To fine-tune InternVL on Flickr30K with 32 GPUs and slurm system, run: ```bash PARTITION='your partition' GPUS=32 sh shell/finetune/internvl_stage2_finetune_flickr_364_bs1024_ep10.sh ``` To fine-tune InternVL on Flickr30K-CN with 32 GPUs and slurm system, run: ```shell PARTITION='your partition' GPUS=32 sh shell/finetune/internvl_stage2_finetune_flickrcn_364_bs1024_ep10.sh ``` To fine-tune InternVL on COCO with 32 GPUs and slurm system, run: ```shell PARTITION='your partition' GPUS=32 sh shell/finetune/internvl_stage2_finetune_coco_364_bs1024_ep5.sh ``` The hyperparameters used here are: | config | Flickr30K | Flickr30K-CN | COCO | | --------------------------- | ----------------------------------- | ----------------------------------- | ----------------------------------- | | learning rate | 1e-6 | 1e-6 | 1e-6 | | layer-wise lr
decay rate | InternViT-6B (0.9),
QLLaMA (0.9) | InternViT-6B (0.9),
QLLaMA (0.9) | InternViT-6B (0.9),
QLLaMA (0.9) | | optimizer | AdamW | AdamW | AdamW | | weight decay | 0.05 | 0.05 | 0.05 | | input resolution | 364x364 | 364x364 | 364x364 | | total batch size | 1024 | 1024 | 1024 | | warm-up iterations | 100 | 100 | 100 | | training epochs | 10 | 10 | 5 | | drop path rate | 0.3 | 0.3 | 0.3 | | numerical precision | zero1 + bf16 | zero1 + bf16 | zero1 + bf16 | | trainable / total params | 14B / 14B | 14B / 14B | 14B / 14B | | GPUs for training | 32×A100 (80G) | 32×A100 (80G) | 32×A100 (80G) | | Required GPU memory | 80G | 80G | 80G | ## 🔥 Retrieval Fine-tuning (Head) > Note: This section demonstrates how to perform a cost-effective fine-tuning of our model. The hyperparameters shown here are not optimized for any specific task. For practical applications, further adjustments to the hyperparameters may be necessary to achieve optimal performance. To fine-tune the head of InternVL on Flickr30K with 4 GPUs, run: ```bash GPUS=4 BATCH_SIZE=32 sh shell/head_finetune/internvl_stage2_finetune_flickr_224_bs1024_ep10_head_4gpu.sh ``` To fine-tune the head of InternVL on Flickr30K-CN with 4 GPUs, run: ```shell GPUS=4 BATCH_SIZE=32 sh shell/head_finetune/internvl_stage2_finetune_flickrcn_224_bs1024_ep10_head_4gpu.sh ``` To fine-tune the head of InternVL on COCO with 4 GPUs, run: ```shell GPUS=4 BATCH_SIZE=32 shell/head_finetune/internvl_stage2_finetune_coco_224_bs1024_ep5_head_4gpu.sh ``` The hyperparameters used here are: | config | Flickr30K | Flickr30K-CN | COCO | | ------------------------ | ------------- | ------------- | ------------- | | learning rate | 1e-6 | 1e-6 | 1e-6 | | optimizer | AdamW | AdamW | AdamW | | weight decay | 0.05 | 0.05 | 0.05 | | input resolution | 224x224 | 224x224 | 224x224 | | total batch size | 4x32 | 4x32 | 4x32 | | warm-up iterations | 100 | 100 | 100 | | training epochs | 10 | 10 | 5 | | drop path rate | 0.0 | 0.0 | 0.3 | | numerical precision | zero3 + bf16 | zero3 + bf16 | zero1 + bf16 | | trainable / total params | 0.2B / 14B | 0.2B / 14B | 0.2B / 14B | | GPUs for training | 4×GPU (>=32G) | 4×GPU (>=32G) | 4×GPU (>=32G) | | Required GPU memory | 24G | 24G | 24G | ## 🔥 Retrieval Fine-tuning (LoRA) > Note: This section demonstrates how to perform a cost-effective fine-tuning of our model. The hyperparameters shown here are not optimized for any specific task. For practical applications, further adjustments to the hyperparameters may be necessary to achieve optimal performance. To fine-tune InternVL using LoRA on Flickr30K with 4 GPUs, run: ```bash GPUS=4 BATCH_SIZE=32 sh shell/lora_finetune/internvl_stage2_finetune_flickr_224_bs1024_ep10_lora16_4gpu.sh ``` To fine-tune InternVL using LoRA on Flickr30K-CN with 4 GPUs, run: ```shell GPUS=4 BATCH_SIZE=32 sh shell/lora_finetune/internvl_stage2_finetune_flickrcn_224_bs1024_ep10_lora16_4gpu.sh ``` To fine-tune InternVL using LoRA on COCO with 4 GPUs, run: ```shell GPUS=4 BATCH_SIZE=32 shell/lora_finetune/internvl_stage2_finetune_coco_224_bs1024_ep5_lora16_4gpu.sh ``` The hyperparameters used here are: | config | Flickr30K | Flickr30K-CN | COCO | | ------------------------ | ------------- | ------------- | ------------- | | learning rate | 1e-6 | 1e-6 | 1e-6 | | optimizer | AdamW | AdamW | AdamW | | lora rank | 16 | 16 | 16 | | weight decay | 0.05 | 0.05 | 0.05 | | input resolution | 224x224 | 224x224 | 224x224 | | total batch size | 4x32 | 4x32 | 4x32 | | warm-up iterations | 100 | 100 | 100 | | training epochs | 10 | 10 | 5 | | drop path rate | 0.0 | 0.0 | 0.3 | | numerical precision | zero3 + bf16 | zero3 + bf16 | zero1 + bf16 | | trainable / total params | 0.3B / 14B | 0.3B / 14B | 0.3B / 14B | | GPUs for training | 4×GPU (>=40G) | 4×GPU (>=40G) | 4×GPU (>=40G) | | Required GPU memory | 37G | 37G | 37G | ## Fine-Tuning a Custom Dataset 1. **Organize Your Data**: Format your dataset similar to COCO or Flickr30K. 2. **Update Meta Information**: Add your dataset's meta information to the `ds_collections` dictionary in `internvl_g/internvl/train/internvl_stage2_finetune.py`. For example: ```python ds_collections = { 'my_dataset_flickr_format': { 'root': './data/my_dataset/images/', 'annotation': './data/my_dataset/annotations.txt', }, 'my_dataset_coco_format': { 'root': './data/my_dataset/', 'annotation': './data/my_dataset/annotations.json', }, } ``` 3. **Name Your Dataset**: - Include `flickr_format` or `coco_format` in your dataset's `dataset_name`. This will allow the script to reuse the Flickr30K or COCO dataloader accordingly. By following these steps, you can easily fine-tune the InternVL model on your custom dataset using the existing COCO or Flickr30K data loading mechanisms.