Datasets:
File size: 2,600 Bytes
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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.
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