facesyntheticsspigacaptioned

# Dataset Card for "face_synthetics_spiga_captioned"

This is a copy of the [Microsoft FaceSynthetics dataset with SPIGA-calculated landmark annotations](https://huggingface.co/datasets/pcuenq/face_synthetics_spiga), and additional BLIP-generated captions.
For a copy of the original FaceSynthetics dataset with no extra annotations, please refer to [pcuenq/face_synthetics](https://huggingface.co/datasets/pcuenq/face_synthetics).

Here is the code for parsing the dataset and generating the BLIP captions:
```py
from transformers import pipeline

dataset_name = "pcuenq/face_synthetics_spiga"
faces = load_dataset(dataset_name)
faces = faces["train"]

captioner = pipeline("image-to-text",model="Salesforce/blip-image-captioning-large", device=0)

def caption_image_data(example):
image = example["image"]
image_caption = captioner(image)[0]['generated_text']
example['image_caption'] = image_caption
return example

faces_proc = faces.map(caption_image_data)
faces_proc.push_to_hub(f"multimodalart/face_synthetics_spiga_captioned")
```

# 「face_synthetics_spiga_captioned」数据集卡片

本数据集为[带有SPIGA计算得到的面部地标注释的Microsoft FaceSynthetics数据集](https://huggingface.co/datasets/pcuenq/face_synthetics_spiga)的副本，并额外包含BLIP生成的图像字幕。

如需获取无额外注释的原始FaceSynthetics数据集副本，请访问[pcuenq/face_synthetics](https://huggingface.co/datasets/pcuenq/face_synthetics)。

以下为解析该数据集并生成BLIP图像字幕的代码：
py
from transformers import pipeline

dataset_name = "pcuenq/face_synthetics_spiga"
faces = load_dataset(dataset_name)
faces = faces["train"]

captioner = pipeline("image-to-text",model="Salesforce/blip-image-captioning-large", device=0)

def caption_image_data(example):
image = example["image"]
image_caption = captioner(image)[0]['generated_text']
example['image_caption'] = image_caption
return example

faces_proc = faces.map(caption_image_data)
faces_proc.push_to_hub(f"multimodalart/face_synthetics_spiga_captioned")