---
license: mit
language:
- en
base_model:
- Ultralytics/YOLO11
pipeline_tag: object-detection
---

# YOLO11 Pill Detection Model

## Model Description

This model is a custom-trained **YOLO11 object detection model** developed for detecting **full pills** in images.
It was trained using a labeled dataset prepared in **Roboflow** and exported in **YOLO format**.

The model identifies pills by predicting:

* Bounding boxes
* Confidence scores
* Object class labels

Currently, the model supports **three detection classes**:

* `pill`
* `capsule`
* `tablet`

This project is designed for pharmaceutical object detection research and computer vision experimentation.

---

## Model Details

### Model Type

* **Architecture:** YOLO11
* **Task:** Object Detection
* **Framework:** Ultralytics YOLO

### Classes

```yaml id="5m4e5l"
names:
  0: pill
  1: capsule
  2: tablet
```

---

## Intended Uses

### Primary Use Cases

This model is intended for:

* Pill detection in images
* Pharmaceutical automation experiments
* Computer vision prototyping
* Medication localization in images/video

### Out-of-Scope Use

This model is **not intended for**:

* Medical diagnosis
* Drug verification in clinical workflows
* Safety-critical pharmaceutical decisions

Predictions may be inaccurate under challenging imaging conditions.

---

## Training Data

The model was trained on a **custom Roboflow dataset** containing images of [**Labeled_full_pill Computer Vision Model**](https://universe.roboflow.com/cocotoyolo-rg00w/labeled_full_pill/model/2) annotated with bounding boxes.

### Dataset Characteristics

* Annotated in Roboflow
* Exported in YOLO8 format
* Single object class: `pill`

### Dataset Split

Example:

* **Train:** 70%
* **Validation:** 20%
* **Test:** 10%

---

## Training Procedure

The model was trained using **Ultralytics YOLO11** with pretrained weights.

### Training Hyperparameters

```yaml id="swf4hk"
model: yolo11x.pt
epochs: 100
imgsz: 640
batch: 16
optimizer: auto
device: 6
```

### Training Command

```bash id="x0n8cd"
yolo train device=3 \
  model=ul://ultralytics/yolo11/yolo11x \
  data=ul://wijai-thongsom/datasets/labeled-full-pillv2iyolov8 \
  roject=wijai-thongsom/jolly-husky \
  name=yolo11x
  epochs=100 \
  imgsz=640 \
  batch=-1
```

---

## Evaluation Results

Model performance was evaluated on the validation set using standard object detection metrics.

### Metrics

| Metric    | Value |
| --------- | ----: |
| Precision |  0.953978 |
| Recall    |  0.932336 |
| mAP50     |  0.965024 |
| mAP50-95  |  0.728589 |

> Replace these values with the actual metrics from your training results.

---

## Inference

### CLI Example

```bash id="7yqz1e"
yolo detect predict \
  model=yolo11x.pt \
  source=image.jpg
```

### Python Example

```python id="x61czj"
from ultralytics import YOLO

model = YOLO("yolo11x.pt")
results = model("image.jpg")

for result in results:
    print(result.boxes)
```

---

## Limitations

The model performance may degrade in cases such as:

* Poor lighting
* Motion blur
* Partial occlusion
* Overlapping pills
* Pill appearances not represented in the training dataset

Performance is dependent on image quality and dataset diversity.

---

## Bias and Risks

Because this model was trained on a custom dataset, its predictions may be biased toward:

* Specific pill colors
* Particular lighting conditions
* Limited pill shapes and sizes
* Background styles present in training data

Use caution when applying the model to images outside the training distribution.

---

## Environmental Impact

Training object detection models requires computational resources that consume energy.

Training setup example:

* **Hardware:** GPU
* **Framework:** Ultralytics YOLO11
* **Epochs:** 100

For reproducibility, document:

* GPU type
* Training duration
* Energy consumption estimate

---

## Model Files

Typical files included in this repository:

```bash id="5nucyy"
.
├── README.md
├── yolo11n.onnx
├── yolo11m.onnx
├── yolo11x.onnx
├── data.yaml
└── results.png
```

---

## Citation

If you use this model, please cite:

```bibtex id="54bb7l"
@misc{yolo11-pill-detection,
  title={YOLO11 Pill Detection Model},
  author={Wijai Thongsom},
  year={2026},
  publisher={Hugging Face}
}
```

---

## License

This model is released under the **MIT License**.

---

## Acknowledgments

* **Ultralytics** for YOLO11
* **Roboflow** for dataset annotation/export
* **Hugging Face Hub** for model hosting