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README.md

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+---
+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 **one detection class**:
+
+* `pill`
+
+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
+```
+
+---
+
+## 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** 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%
+
+> Replace with your actual dataset split ratios.
+
+---
+
+## Training Procedure
+
+The model was trained using **Ultralytics YOLO11** with pretrained weights.
+
+### Training Hyperparameters
+
+```yaml id="swf4hk"
+model: yolo11n.pt
+epochs: 100
+imgsz: 640
+batch: 16
+optimizer: auto
+device: 6
+```
+
+### Training Command
+
+```bash id="x0n8cd"
+yolo train device=6 \
+  model=ul://ultralytics/yolo11/yolo11n \
+  data=ul://wijai-thongsom/datasets/labeled-full-pillv2iyolov8 \
+  roject=wijai-thongsom/jolly-husky \
+  name=exp
+  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 |  0.919417 |
+| Recall    |  0.885885 |
+| mAP50     |  0.950869 |
+| mAP50-95  |  0.70499 |
+
+> Replace these values with the actual metrics from your training results.
+
+---
+
+## Inference
+
+### CLI Example
+
+```bash id="7yqz1e"
+yolo detect predict \
+  model=yolo11n.pt \
+  source=image.jpg
+```
+
+### Python Example
+
+```python id="x61czj"
+from ultralytics import YOLO
+
+model = YOLO("yolo11n.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.pt
+├── 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