Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: apache-2.0
+license: agpl-3.0
 language:
 - en
 metrics:
@@ -8,9 +8,229 @@ metrics:
 - f1
 - recall
 tags:
+- object-detection
 - computer-vision
+- yolo
+- yolov11
 - helmet-detection
-- traffic-safety
-- object-detection
+- safety
+- traffic-monitoring
+- cctv
 pipeline_tag: object-detection
----
+library_name: ultralytics
+base_model:
+- Ultralytics/YOLO11
+---
+
+# Helmet and No-Helmet Detection — YOLO11s
+
+A YOLO11s object detection model trained for detecting **helmets** and
+**no-helmet instances** in images.
+
+The training dataset was obtained from Roboflow and was originally
+created by another Roboflow user. The dataset was forked and used for
+training this model. Dataset attribution and licensing information are
+provided below.
+
+## Model Description
+
+This model is a custom-trained **Ultralytics YOLO11s** object detection
+model.
+
+The model predicts two classes:
+
+| Class ID | Class |
+|----------|-------|
+| 0 | helmet |
+| 1 | no-helmet |
+
+The model accepts images at a nominal resolution of **640 × 640 pixels**
+and produces bounding-box detections for the two classes.
+
+### Model Architecture
+
+- Architecture: YOLO11s
+- Task: Object Detection
+- Parameters: 9,428,566
+- Layers: 181
+- GFLOPs: 21.6
+- Input image size: 640 × 640
+- Number of classes: 2
+
+The checkpoint identifies the architecture as YOLO11s and reports
+181 layers, 9,428,566 parameters, and 21.6 GFLOPs.
+
+## Intended Use
+
+This model is intended for research, experimentation, and computer
+vision applications involving helmet compliance detection.
+
+Potential applications include:
+
+- Helmet detection in CCTV footage
+- Road safety monitoring
+- Traffic violation detection
+- Industrial safety monitoring
+- Motorcycle helmet compliance analysis
+- Computer vision research
+
+The model is particularly intended as a component of a larger
+computer vision pipeline rather than as a complete traffic-violation
+system.
+
+For example:
+```text
+    CCTV Image
+         |
+         v
+    Helmet Detector
+         |
+         +---- helmet
+         |
+         +---- no-helmet
+         |
+         v
+    Person / Vehicle Association
+         |
+         v
+    Number Plate Detection
+         |
+         v
+    Violation Processing
+```
+This model itself only performs helmet/no-helmet object detection.
+
+## Classes
+
+The class mapping stored in the trained checkpoint is:
+
+    0: helmet
+    1: no-helmet
+
+The model should therefore be interpreted using this class mapping
+when processing its predictions.
+
+## Training
+
+The model was trained using a dataset exported from Roboflow.
+
+The original dataset was not created by the author of this model.
+Instead, the dataset was forked from an existing Roboflow dataset and
+subsequently used for training.
+
+The checkpoint stores the following training configuration.
+
+### Training Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Task | Detection |
+| Image size | 640 |
+| Batch size | 16 |
+| Epochs configured | 100 |
+| Pretrained | True |
+| Optimizer | Auto |
+| Workers | 8 |
+| AMP | True |
+| Seed | 0 |
+| Deterministic | True |
+| Patience | 10 |
+| Validation | True |
+| Validation split | val |
+
+The checkpoint was produced using Ultralytics version 8.3.233.
+
+The stored checkpoint metadata identifies the model as an
+`ultralytics.nn.tasks.DetectionModel`.
+
+## Data Augmentation
+
+The stored training configuration includes the following
+augmentation settings:
+
+| Augmentation | Value |
+|--------------|-------|
+| Mosaic | 1.0 |
+| MixUp | 0.12 |
+| Copy-Paste | 0.05 |
+| Horizontal Flip | 0.5 |
+| Scale | 0.6 |
+| Rotation | 4.0 |
+| Translation | 0.1 |
+| Shear | 1.0 |
+| Perspective | 0.0004 |
+
+These values are reported from the training configuration stored
+inside the model checkpoint.
+
+## Dataset
+
+### Original Dataset
+
+The training dataset was obtained from Roboflow.
+
+**Original dataset:**  
+[![Helmet-detection_yolov8-8jenr](https://img.shields.io/badge/helmet%20detection-yolov8%208jenr(Dataset)-00000)](https://app.roboflow.com/smolry/helmet-detection_yolov8-8jenr/1/images)
+
+**Original dataset author:**  
+[![Smolry](https://img.shields.io/badge/Smolry-00000)](https://app.roboflow.com/smolry)
+
+**Roboflow project:**  
+[![helmet-detection_yolov8-8jenr](https://img.shields.io/badge/helmet%20detection-yolov8%208jenr-00000)](https://app.roboflow.com/smolry/helmet-detection_yolov8-8jenr)
+
+**Dataset version:**  
+[v1 2026-01-25 2:39am]
+
+The dataset was forked from the original Roboflow project and used as
+the basis for training this model.
+
+### Dataset Attribution
+
+This model does not claim ownership of the original dataset.
+
+The dataset and its annotations remain subject to the original
+dataset's license and attribution requirements.
+
+Users of this model should consult the original dataset page and
+license before redistributing the dataset, annotations, or derived
+datasets.
+
+
+## Data Preprocessing
+
+The model was trained using the YOLO-compatible dataset configuration
+exported from Roboflow.
+
+The checkpoint references the following dataset configuration:
+
+    /content/Helmet-and-Non-Helmet-Detection--2/data.yaml
+
+The original training environment was hosted in Google Colab /
+Google Drive according to paths recorded in the checkpoint.
+
+## Model Input
+
+The model expects an image input and was trained using:
+
+    640 × 640
+
+Ultralytics handles the necessary image preprocessing during normal
+inference.
+
+## Inference
+
+Install Ultralytics:
+
+    pip install ultralytics
+
+Load the model:
+
+```python
+from ultralytics import YOLO
+
+model = YOLO("best.pt")
+
+results = model("image.jpg", imgsz=640)
+
+for result in results:
+    result.show()