We provide two scripts for reproduction about (1) CoMP-MM-1B w/ SigLIP and Qwen2.5:
bash scripts/comp/comp_1b_siglip.shand (2) CoMP-MM-1B w/ DINOv2 and Qwen2.5:
bash scripts/comp/comp_1b_dinov2.shAlternatively, you can use our pre-trained CoMP-SigLIP (obtained by bash scripts/comp/comp_1b_siglip.sh ) to replace original SigLIP for more powerful and efficient training:
bash scripts/comp/comp_1b_comp_siglip.shdatasets:
- json_path: data/LLaVA-Pretrain/blip_laion_cc_sbu_558k_mixin.json
sampling_strategy: allWe use BLIP558K (same as previous llava 1.5 series), and we modify the image path prefix to a mixin for compatibility with other datasets.
datasets:
- json_path: data/LLaVA-OneVision-Mid-Data/synthdog_en_processed.json
sampling_strategy: all
- json_path: data/LLaVA-OneVision-Mid-Data/synthdog_zh_processed.json
sampling_strategy: all
- json_path: data/LLaVA-OneVision-Mid-Data/ureader_tr_processed.json
sampling_strategy: all
- json_path: data/cc3m_llava_recap/cc3m_llava_recap_2m_subset.json
sampling_strategy: all
- json_path: data/LLaVA-ReCap-118K/LLaVA-ReCap-118K_all.json
sampling_strategy: all
- json_path: data/LLaVA-ReCap-558K/LLaVA-ReCap-558K_all.json
sampling_strategy: all
- json_path: data/DenseFusion/DenseFusion-1M.json
sampling_strategy: all
# Due to the Alignment Loss, we removed all text-only training data.
# - json_path: data/LLaVA-OneVision-Mid-Data/evol_instruct/evol_instruct_processed.json
# sampling_strategy: allWe use LLaVA-Mid-Stage data, including LLaVA-OneVision-Mid-Data and LLaVA-Recap-Data. To support high resolution inputs, we replace 1M data in CC3M with Densefusion-1M.
datasets:
# Due to the Alignment Loss, we removed all text-only training data.
- json_path: data/LLaVA-NeXT-Data/llava_next_748k_mixin.json
sampling_strategy: all
- json_path: data/llava_ov_si_only_image.json
sampling_strategy: allWe use LLaVA-OV-SI data, including LLaVA-NeXT-Data and images from LLaVA-OneVision-Data.