| --- |
| license: mit |
| task_categories: |
| - image-segmentation |
| - object-detection |
| language: |
| - ar |
| - fr |
| tags: |
| - coco |
| - instance-segmentation |
| - medicine |
| - medication-boxes |
| - mask-rcnn |
| pretty_name: Medication Boxes (Arabic / Latin) |
| size_categories: |
| - n<1K |
| --- |
| |
| # 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 |
| ``` |
|
|
| | Split | Images | Instances | |
| |---|---:|---:| |
| | train | 540 | 806 | |
| | val | 68 | 121 | |
| | test | 68 | 100 | |
| | **Total** | **676** | **1027** | |
|
|
| Each `images` entry also has three extra fields: `source`, `original_split` and `original_file`. |
|
|
| ## Sources |
|
|
| | Source | Images | Instances | Original annotation | |
| |---|---:|---:|---| |
| | `main_ar_fr` | 557 | 874 | YOLO-seg, 4-point polygons | |
| | `medicine_packv2` | 119 | 153 | COCO polygons (24 drug classes merged into `medicine_box`) | |
|
|
| 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 |
|
|
| ```python |
| 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. |
|
|
