--group_by_modality_length True: this should only be used when your instruction tuning dataset contains both language (e.g. ShareGPT) and multimodal (e.g. LLaVA-Instruct). It makes the training sampler only sample a single modality (either image or language) during training, which we obser...
Please download the 558K subset of the LAION-CC-SBU dataset with BLIP captions we use in the paper here. Pretrain takes around 5.5 hours for LLaVA-v1.5-13B on 8x A100 (80G), due to the increased resolution to 336px. It takes around 3.5 hours for LLaVA-v1.5-7B. Training script wi...
We extract noun-phrases usingSpacyfor each caption over the whole CC3M dataset, and count the frequency of each unique noun-phrase. We skip noun-phrases whose frequency is smaller than 3, as they are usually rare combinations concept and attributes that has already been covered by other captio...
If we can predict them well, we have essentially solved precision health. Now, of course, as you can guess, this is not so easy, right? So, a patient journey is not just a snapshot, but actually a longitudinal time series. More annoyingly, most...
--group_by_modality_length True: this should only be used when your instruction tuning dataset contains both language (e.g. ShareGPT) and multimodal (e.g. LLaVA-Instruct). It makes the training sampler only sample a single modality (either image or language) during training, which we obser...
Pretraining Data: Download the 558K subset of the LAION-CC-SBU dataset with BLIP captions we use in the paper here. And put the data into ./playground/data. Fine-tuning Data: Please download all images and the instruction-tuning annotations llava-uhd-v2-sft-data.json in LLaVA-UHD-v2-...
Please download the 558K subset of the LAION-CC-SBU dataset with BLIP captions we use in the paperhere. Pretrain takes around 5.5 hours for LLaVA-v1.5-13B on 8x A100 (80G), due to the increased resolution to 336px. It takes around 3.5 hours for LLaVA-v1.5-7B. ...
broad-coverage biomedical figure-caption dataset extracted from PubMed Central, use GPT-4 to self-instruct open-ended instruction-following data from the captions, and then fine-tune a large general-domain vision-language model using a novel curriculum ...
Captions:从不同角度描述视觉场景 Boxes:定位场景中的对象并编码其概念和空间位置 生成的指令数据可以分成三类 Conversation:让 GPT 模仿人和智能助手的对话,人向智能助手提问,智能助手给出回答。问题和答案都是 GPT 自动生成的。 Detailed description:从人工设计的问题列表中任选一个,要求 GPT 回答该问题,并详细描述图...
Table 2. Captioning results on the UCM-captions dataset. The results on the UAV dataset shown in Table 3 illustrate that when fine-tuning RS-LLaVA solely on the UAV dataset, it exhibits better performance compared to fine-tuning the model on the RS-instructions dataset. We also observe tha...