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PSImera/apex_enemy_detect

sourceHugging Facemitupdated 3mo agoView on Hugging Face

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Model Card

apexenemydetect

Русская версия

YOLOv8 models for detecting enemies in Apex Legends gameplay footage. Two variants: nano (speed) and medium (accuracy).

Apex Enemy Detect Demo — a tool to analyze gameplay videos, detect enemies, and fix stretched resolution issues.

Models

	YOLOv8n (nano)	YOLOv8m (medium)
File	`apex_detect_v8n_v2.1.pt`	`apex_detect_v8m_v2.1.pt`
Parameters	~3.2M	~25.9M
Precision	0.932	0.943
Recall	0.877	0.883
mAP@50	0.930	0.938
mAP@50-95	0.756	0.796
Better for	Speed / low-end GPU	Accuracy

Usage

python

from ultralytics import YOLO

model = YOLO("apex_detect_v8m_v2.1.pt")
results = model.predict("frame.jpg", conf=0.35, iou=0.5, imgsz=640)

Or use it automatically via the Apex Enemy Detect Demo app — the models are loaded from here on first run.

Training Setup

Parameter	Value
Dataset	`apex_detect_v2_p1_converted`
Epochs	200 (patience 100)
Batch size	16
Image size	640×640
Optimizer	AdamW (lr=0.001)
Augmentations	HSV (S/V ±0.3), horizontal flip (p=0.5), random erasing (p=0.4)

Fine-tuned from Ultralytics COCO pretrained weights on a custom Apex Legends enemy dataset.

Training Curves

<table> <tr><th>YOLOv8n (nano)</th><th>YOLOv8m (medium)</th></tr> <tr> <td><img src="runv8nv2.1/results.png"></td> <td><img src="runv8mv2.1/results.png"></td> </tr> </table>

Precision-Recall Curves

<table> <tr><th>YOLOv8n (nano)</th><th>YOLOv8m (medium)</th></tr> <tr> <td><img src="runv8nv2.1/BoxPRcurve.png"></td> <td><img src="runv8mv2.1/BoxPRcurve.png"></td> </tr> </table>