File size: 5,824 Bytes
293eb1f ff1d26d 14bc594 a00ef62 14bc594 9362986 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
---
license: apache-2.0
datasets:
- REILX/text-description-of-the-meme
- SWHL/ChineseOCRBench
- priyank-m/chinese_text_recognition
- fly0331/ChineseTest
language:
- zh
pipeline_tag: image-text-to-text
tags:
- llava
- qwen2
- CLIP
- zh
---
### 模型 llava-Qwen2-7B-Instruct-Chinese-CLIP-v2 扩大中文图文训练数据集，增强中文文字识别能力和表情包内涵识别能力
<img src="./images/llava_qwen_2b_chinese-clip.jpg" alt="logo" style="display: block; margin: 0 auto;" width="300" height="300">
1. 模型结构：</br>
llava-Qwen2-7B-Instruct-Chinese-CLIP = Qwen/Qwen2-7B-Instruct + multi_modal_projector + OFA-Sys/chinese-clip-vit-large-patch14-336px</br>
2. 微调模块
- vision_tower和language_model的q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj模块进行lora训练</br>
- mmp层全量训练</br>
3. 微调参数
- lora_r=32,lora_alpha=64,num_train_epochs=3,per_device_train_batch_size=1,gradient_accumulation_steps=8,high_lr=5e-4,low_lr=1e-5,model_max_length=2048.</br>
- 设备：8*A800</br>
- 训练时长：68小时06分钟
4. 数据集</br>
训练数据集对比上一代模型REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP，扩大至四种中文数据集，图片总数扩大47倍，文字总数扩大5倍。</br>
包括以下：</br>
- 使用gemini-1.5-pro, gemini-1.5-flash, yi-vision, gpt4o,claude-3.5-sonnet模型描述emo-visual-data和ChineseBQB数据集。</br>
文本描述信息通过[text-description-of-the-meme](https://huggingface.co./datasets/REILX/text-description-of-the-meme) 下载</br>
图像可通过[emo-visual-data](https://github.com/LLM-Red-Team/emo-visual-data), [ChineseBQB](https://github.com/zhaoolee/ChineseBQB)下载</br>
图片数据总量1.8G，约10835张中文表情包图片。文字总量42Mb，约24332个图像文本对描述信息。
- [priyank-m/chinese_text_recognition](https://huggingface.co./datasets/priyank-m/chinese_text_recognition)</br>
图片数据总量2.0Gb，约500000张图片。文字总量207Mb，约500000个图像文本对描述信息。
- [SWHL/ChineseOCRBench](https://huggingface.co./datasets/SWHL/ChineseOCRBench)</br>
图片数据总量134Mb，约3410张图片。文字总量1.3Mb，约3410个图像文本对描述信息。
- [fly0331/ChineseTest](https://huggingface.co./datasets/fly0331/ChineseTest)</br>
图片数据总量530Mb，约6247张图片。文字总量5.4Mb，约6247个图像文本对描述信息。
为了提升模型对中文文字的识别能力，我们引入了 priyank-m/chinese_text_recognition、SWHL/ChineseOCRBench 和 fly0331/ChineseTest 三个基于中文文字领域图片的数据集。这些数据集的融入将有效弥补 REILX/llava-Qwen2-7B-Instruct-Chinese-CLIP 模型在中文文字识别方面的不足，使其能够更好地理解和处理中文文本信息。
6. 效果展示</br>
以下测试结果显示模型能识别图像中的文字信息，且能正确识别表情包想要表达的内涵</br>
<img src="./images/llava-qwen-2-7b-OFA-Syschinese-clip-chineseOCR_pri_fly_SWH_memechinese_lora_0716_warmup0_1_fp16/1.PNG" width="800" height="400">
<img src="./images/llava-qwen-2-7b-OFA-Syschinese-clip-chineseOCR_pri_fly_SWH_memechinese_lora_0716_warmup0_1_fp16/2.PNG" width="800" height="400">
<img src="./images/llava-qwen-2-7b-OFA-Syschinese-clip-chineseOCR_pri_fly_SWH_memechinese_lora_0716_warmup0_1_fp16/3.PNG" width="800" height="400">
<img src="./images/llava-qwen-2-7b-OFA-Syschinese-clip-chineseOCR_pri_fly_SWH_memechinese_lora_0716_warmup0_1_fp16/4.PNG" width="800" height="400">
<img src="./images/llava-qwen-2-7b-OFA-Syschinese-clip-chineseOCR_pri_fly_SWH_memechinese_lora_0716_warmup0_1_fp16/5.PNG" width="800" height="400">
<img src="./images/llava-qwen-2-7b-OFA-Syschinese-clip-chineseOCR_pri_fly_SWH_memechinese_lora_0716_warmup0_1_fp16/6.PNG" width="800" height="400">
<img src="./images/llava-qwen-2-7b-OFA-Syschinese-clip-chineseOCR_pri_fly_SWH_memechinese_lora_0716_warmup0_1_fp16/7.PNG" width="800" height="400">
<img src="./images/llava-qwen-2-7b-OFA-Syschinese-clip-chineseOCR_pri_fly_SWH_memechinese_lora_0716_warmup0_1_fp16/8.PNG" width="800" height="400">
<img src="./images/llava-qwen-2-7b-OFA-Syschinese-clip-chineseOCR_pri_fly_SWH_memechinese_lora_0716_warmup0_1_fp16/9.PNG" width="800" height="400">
</br>
7. 代码</br>
推理代码
```python
from transformers import LlavaForConditionalGeneration, AutoProcessor
import torch
from PIL import Image
raw_model_name_or_path = "/保存的完整模型路径"
model = LlavaForConditionalGeneration.from_pretrained(raw_model_name_or_path, device_map="cuda:0", torch_dtype=torch.bfloat16)
processor = AutoProcessor.from_pretrained(raw_model_name_or_path)
model.eval()
def build_model_input(model, processor):
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "<image>\n 你是一位有深度的网络图片解读者，擅长解读和描述网络图片。你能洞察图片中的细微之处，对图中的人物面部表情、文字信息、情绪流露和背景寓意具有超强的理解力，描述信息需要详细。"}
]
prompt = processor.tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image = Image.open("01.PNG")
inputs = processor(text=prompt, images=image, return_tensors="pt", return_token_type_ids=False)
for tk in inputs.keys():
inputs[tk] = inputs[tk].to(model.device)
generate_ids = model.generate(**inputs, max_new_tokens=200)
generate_ids = [
oid[len(iids):] for oid, iids in zip(generate_ids, inputs.input_ids)
]
gen_text = processor.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0]
return gen_text
build_model_input(model, processor)
``` |