Pankaj8922/stickers-binary-v2-cleaned

Stickers Binary v2 — Cleaned

Binary SFW/NSFW sticker classification dataset. This version has been cleaned of likely label errors using cross-validated out-of-fold model predictions combined with cleanlab's find_label_issues.

Structure

This dataset has exactly two columns:

Column	Type	Description
image	image	The sticker image, 256x256, letterboxed (see below).
label	int64	0 = SFW, 1 = NSFW.

Class distribution

Split	Count	Percentage
SFW (0)	97257	71.0%
NSFW (1)	39713	29.0%
Total	136970	100%

Preprocessing: letterbox resize — REQUIRED at inference time

Every image in this dataset was resized to 256x256 using letterbox resizing: the image is scaled to fit within 256x256 while preserving its original aspect ratio, then padded with solid gray (RGB 114, 114, 114) to fill the remaining space. This avoids the distortion of a plain squash-resize and avoids losing content at the edges the way a center-crop would.

Any model trained on this dataset must receive the same letterbox preprocessing on every image at inference time — not a plain resize, not a center-crop. If inference uses a different resizing strategy than training did, the model sees a distribution of inputs it never trained on (different aspect-ratio handling, different effective content scale and position within the frame), which will silently degrade accuracy without raising an error.

Reference implementation used to build this dataset:

from PIL import Image

def letterbox_resize(img, target_size=(256, 256), pad_color=(114, 114, 114)):
    img = img.convert("RGB")
    img_ratio = img.width / img.height
    target_ratio = target_size[0] / target_size[1]
    if img_ratio > target_ratio:
        new_width = target_size[0]
        new_height = max(1, int(new_width / img_ratio))
    else:
        new_height = target_size[1]
        new_width = max(1, int(new_height * img_ratio))
    resized_img = img.resize((new_width, new_height), resample=Image.Resampling.LANCZOS)
    padded_img = Image.new("RGB", target_size, color=pad_color)
    paste_x = (target_size[0] - new_width) // 2
    paste_y = (target_size[1] - new_height) // 2
    padded_img.paste(resized_img, (paste_x, paste_y))
    return padded_img

Apply this exact function (same target_size, same pad_color) to any image before passing it to a model trained on this dataset, whether in evaluation, a serving pipeline, or downstream fine-tuning.