| | --- |
| | license: apache-2.0 |
| | task_categories: |
| | - image-feature-extraction |
| | - image-classification |
| | - image-to-image |
| | - text-to-image |
| | size_categories: |
| | - n<1K |
| | tags: |
| | - humans |
| | - glasses |
| | - eyewear |
| | - retrieval |
| | --- |
| | |
| |
|
| | # 📸 Persons with Spectacles |
| |
|
| | A curated image dataset of human faces annotated for the presence of spectacles (eyeglasses). |
| |
|
| | --- |
| |
|
| | ## Dataset Card for `hkanade/persons_with_spectacles` |
| |
|
| | | **Feature** | **Detail** | |
| | |-----------------------|----------------------------------------------------------------------| |
| | | **Dataset name** | `persons_with_spectacles` | |
| | | **Repository** | https://huggingface.co/datasets/hkanade/persons_with_spectacles | |
| | | **License** | apache-2.0 | |
| | | **Languages** | — | |
| | | **Tasks** | Text to Image, Image classification | |
| | | **Size** | 120 | |
| | | **File format** | Parquet | |
| | | **Dataset version** | 1.0.0 | |
| |
|
| | --- |
| |
|
| | ## 1. Dataset Source |
| |
|
| | All samples were collected from wikimedia/wit_base. |
| | |
| | --- |
| | |
| | ## 2. Usage |
| | |
| | ```python |
| | from datasets import load_dataset |
| | from PIL import Image |
| | import os |
| | import matplotlib.pyplot as plt |
| | import math, itertools |
| | import io |
| | from IPython.display import display |
| | import cv2 |
| |
|
| | # load full dataset |
| | ds = load_dataset("hkanade/persons_with_spectacles") |
| | |
| | |
| | def rec_to_pil(rec): |
| | """ |
| | Accepts either |
| | • dict/StructValue holding the raw bytes, or |
| | • raw bytes themselves, or |
| | • a PIL.Image already |
| | Returns a PIL.Image.Image |
| | """ |
| | if isinstance(rec, Image.Image): |
| | return rec # already a PIL image |
| | if isinstance(rec, (bytes, bytearray)): |
| | return Image.open(io.BytesIO(rec)) |
| | if isinstance(rec, dict): # pandas case |
| | # try common key names – adjust if yours differ |
| | for k in ("bytes", "data", 0): |
| | if k in rec: |
| | return Image.open(io.BytesIO(rec[k])) |
| | # fall‑back: take first value |
| | return Image.open(io.BytesIO(next(iter(rec.values())))) |
| | # pyarrow StructValue when you skip .to_pandas() |
| | if hasattr(rec, "values"): # StructValue → tuple |
| | return Image.open(io.BytesIO(rec.values()[0])) |
| | raise TypeError(f"Unsupported type: {type(rec)}") |
| | |
| | plt.imshow(rec_to_pil(ds["train"][0]["image"])) |
| | plt.plot() |
| | ``` |
| | |
| | --- |
| | |
| | ## 3. Columns |
| | |
| | | Column | Datatype | Description | |
| | | --------------------------------- | ---------------------------------------- | ------------------------------------------ | |
| | | `Image` | `struct<bytes: binary, path: string>` | Image | |
| | | `image_url` | `string` | URL of the Wikipedia page | |
| | | `embedding` | `fixed_size_list<element: double>[2048]` | ResNet‑50 embedding | |
| | | `caption_attribution_description` | `string` | Caption text | |
| | | `clip_emb` | `fixed_size_list<element: float>[512]` | CLIP embedding of the image | |
| | | `h` | `fixed_size_list<element: double>[32]` | Hue‑channel histogram | |
| | | `s` | `fixed_size_list<element: double>[32]` | Saturation‑channel histogram | |
| | | `v` | `fixed_size_list<element: double>[32]` | Value‑channel histogram | |
| | | `face_ok` | `bool` | Placeholder flag indicating face validity | |
| | | `sim` | `float` | Cosine similarity with the query embedding | |
| | |
| | --- |
| | |
| | ## 4. Citation |
| | |
| | ```bibtex |
| | @misc{persons_with_spectacles_2025, |
| | author = {Hrishikesh Kanade (hkanade)}, |
| | title = {Persons with Spectacles Dataset}, |
| | year = {2025}, |
| | howpublished = {\url{https://huggingface.co/datasets/hkanade/persons_with_spectacles}} |
| | } |
| | ``` |
