Update README.md

README.md

@@ -16,11 +16,11 @@ pipeline_tag: object-detection
 library_name: ultralytics
 ---
 
-# 🏍️ MotoGP Team Detection & Re-ID Models
+# 🏍️ MotoReID: 
 
-<div align="center">
+<div>
 
-**Fine-tuned weights for detecting and identifying MotoGP teams from race broadcast footage.**
+A deep learning pipeline for **MotoGP team detection, tracking, and re-identification** from race broadcast footage. This system combines **YOLOv8** for robust object detection with **DINOv3** (Vision Transformer) embeddings for semantic team classification. It addresses specific challenges in high-speed sports computer vision: persistent identity tracking across extreme occlusions, rapid camera cuts, and motion blur. This project is in active development.
 
 [![GitHub](https://img.shields.io/badge/GitHub-Repository-181717?style=for-the-badge&logo=github)](https://github.com/johnamit/mgp-detect)
 
@@ -28,13 +28,13 @@ library_name: ultralytics
 
 ## Model Overview
 
-This repository contains trained model weights for the [MotoGP Team Detection](https://github.com/johnamit/mgp-detect) project — a deep learning pipeline for **real-time MotoGP team detection, tracking, and re-identification** from race broadcast footage.
+This repository contains trained model weights for the [MotoReID](https://github.com/johnamit/mgp-detect) project.
 
 ### Included Weights
 
 | File | Description | Size |
 |------|-------------|------|
-| `motogp_yolov8m_detector.pt` | Fine-tuned YOLOv8m for motorcycle detection | ~50MB |
+| `motogp_yolov8m_detector.pt` | Fine-tuned YOLOv8m for MotoGP prototype (bike) detection | ~50MB |
 | `dinov3_team_classifier.pkl` | Logistic Regression classifier trained on DINOv3 embeddings | ~35KB |
 
 ## Pipeline Architecture
@@ -95,25 +95,6 @@ git clone https://github.com/facebookresearch/dinov3.git
 ```
 Download the ViT-S/16 pretrained weights from the [DINOv3 repo](https://github.com/facebookresearch/dinov3).
 
-### 3. Inference Example
-
-```python
-from ultralytics import YOLO
-import torch
-import joblib
-
-# Load detector
-detector = YOLO(detector_path)
-
-# Load classifier
-classifier = joblib.load(classifier_path)
-
-# Run detection
-results = detector.predict(source="race_frame.jpg", conf=0.5)
-
-# For each detection, extract DINOv3 features and classify
-# See full pipeline: https://github.com/johnamit/mgp-detect
-```
 
 ## Training Details