Mot20 Yolo26s Pedestrian

--- license: mit library_name: ultralytics pipeline_tag: object-detection tags: - yolo26 - pedestrian-detection - mot20 - object-detection - computer-vision --- # 🚶 MOT20 YOLO26s Pedestrian Detector YOLO26s fine-tuned on the **MOT20 benchmark** for pedestrian detection in dense crowd scenes. This repository contains the trained **`best.pt`** weights for pedestrian detection. Multi-object tracking is performed separately using **ByteTrack** and is available in the accompanying GitHub repository and Hugging Face Space. --- ## Model Details | Item | Value | | --------------------- | -------------------- | | Architecture | YOLO26s | | Task | Pedestrian Detection | | Dataset | MOT20 | | Classes | Person | | Training Resolution | 640 × 640 | | Evaluation Resolution | 1280 × 1280 | ### Training Data **Training Sequences** * MOT20-01 * MOT20-03 * MOT20-05 **Validation Sequence** * MOT20-02 The validation sequence was completely withheld during training. --- ## Detection Performance Evaluation was performed on the **MOT20-02 validation split** using: ```python imgsz = 1280 conf = 0.25 iou = 0.50 ``` | Metric | Value | | ------------ | ---------: | | Precision | **94.57%** | | Recall | **91.30%** | | [email protected] | **92.10%** | | [email protected]:0.95 | **67.49%** | The detector achieves high pedestrian detection accuracy on dense crowd scenes, providing reliable detections suitable for downstream multi-object tracking. --- ## Tracking Reference When combined with **ByteTrack**, the complete tracking pipeline achieved the following results on **MOT20-02**: | Metric | Value | | ------ | --------: | | HOTA | **50.0%** | | DetA | **55.7%** | | AssA | **45.1%** | | LocA | **85.0%** | > **Note:** These tracking metrics are reported for the complete **YOLO26s + ByteTrack** pipeline and are **not** produced by the detector alone. --- ## Usage ### Load the Model ```python from ultralytics import YOLO model = YOLO("best.pt") ``` ### Run Inference ```python results = model.predict( source="image.jpg", imgsz=1280, conf=0.25 ) ``` ### Download from Hugging Face ```python from huggingface_hub import hf_hub_download from ultralytics import YOLO weights = hf_hub_download( repo_id="Halftom/mot20-yolo26s-pedestrian", filename="best.pt" ) model = YOLO(weights) ``` --- ## Related Resources 📂 **GitHub Repository** ``` https://github.com/Haftom19/MOT20-Ped...

Source context: 6 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo Halftom/mot20-yolo26s-pedestrian