ApyHTML19/Medication_Boxes_Arabe_Latin

Medication Boxes — Arabic / Latin

This dataset contains photos of medication boxes, with packaging text in Arabic and Latin script (French and others). It is annotated for COCO instance segmentation and was built for MediSeG, which segments each box so the text on it can be read afterwards.

• —One class: 1 = medicine_box (0 = background)
• —Format: COCO JSON (polygons + bbox)
• —Images: original resolution, never resized

Structure

train/        540 images
val/           68 images
test/          68 images
annotations/
  instances_train.json
  instances_val.json
  instances_test.json

Each images entry also has three extra fields: source, original_split and original_file.

Sources

The drugs source (1,068 images) was left out because it has bounding boxes only and no segmentation.

Preparation

• —80/10/10 split (seed 42), grouped by source photo. Near-duplicates (dHash of the object crop) go into the same split, which fixes the train/val/test leakage in the original splits (128 images affected).
• —Checks: no corrupted images, no invalid annotations, no empty masks, no exact duplicates (md5).
• —Validation: every split loads with torchvision CocoDetection + wrap_dataset_for_transforms_v2, and a maskrcnn_resnet50_fpn(num_classes=2) forward pass gives finite losses.

Usage

from huggingface_hub import snapshot_download
from torchvision.datasets import CocoDetection, wrap_dataset_for_transforms_v2

root = snapshot_download("ApyHTML19/Medication_Boxes_AR_Latin", repo_type="dataset")
ds = CocoDetection(f"{root}/train", f"{root}/annotations/instances_train.json")
ds = wrap_dataset_for_transforms_v2(ds, target_keys=("boxes", "labels", "masks"))
img, target = ds[0]

Limitations

• —Small dataset (676 images), a single class, no per-drug labels.
• —The main_ar_fr polygons have 4 points, so masks are quadrilaterals that approximate the box outline.