hmnshudhmn24/minecraft-mobs-yolo-dataset

🎮 Minecraft Mobs YOLO Dataset

2,585+ pre-split images with YOLO bounding box annotations for Minecraft mob detection across 5 macro-classes.

---

🧭 Overview

This dataset contains 2,585 images of Minecraft mobs annotated with bounding boxes in YOLO format. Pre-split into Train (80%) and Validation (20%) sets — ready to plug into any Ultralytics YOLO pipeline.

---

🧟 Classes (5 Macro-Classes)

ID	Class	Instances	Includes
0	creeper	594	Standard Creeper
1	skeleton	959	Standard Skeleton, Wither Skeleton, Bogged, Stray
2	spider	491	Standard Spider, Cave Spider
3	zombie	1,115	Standard Zombie, Drowned, Husk
4	enderman	177	Standard Enderman

---

📊 Dataset Details

Property	Value
Total Images	2,585
Train Split	80%
Validation Split	20%
Background Images	~625 (24%)
Format	YOLO (Ultralytics)
Total Files	5,187

Background images with zero bounding boxes are included for hard negative mining to reduce false positives.

---

🗂️ Dataset Structure

minecraft_mobs_yolo/ ├── train/ │ ├── images/ # Training images │ └── labels/ # YOLO annotation .txt files ├── val/ │ ├── images/ # Validation images │ └── labels/ # YOLO annotation .txt files ├── data.yaml # YOLO config file ├── README.md └── LICENSE

---

⚙️ data.yaml

nc: 5 names: ["creeper", "skeleton", "spider", "zombie", "enderman"] train: train/images val: val/images

---

⚡ Quick Start

from ultralytics import YOLO

model = YOLO("yolov8n.pt")

model.train( data="minecraft_mobs_yolo/data.yaml", epochs=50, imgsz=640, batch=16, name="minecraft_mobs_detector" )

results = model.val() print(results)

---

💡 Use Cases

• Minecraft mob detection and tracking
• YOLO model training and fine-tuning
• Object detection with hard negative mining
• Gaming AI research
• Real-time mob radar systems
• Computer vision project demonstrations