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89
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Growth Rate:
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PSImera / apex_enemy_detect
Model's Last Updated: June 29 2026
object-detection
Introduction of apex_enemy_detect
Model Details of apex_enemy_detect
apex_enemy_detect
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
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
| YOLOv8n (nano) | YOLOv8m (medium) |
|---|---|
|
|
Precision-Recall Curves
| YOLOv8n (nano) | YOLOv8m (medium) |
|---|---|
|
|
Runs of PSImera apex_enemy_detect on huggingface.co
189
Total runs
-1
24-hour runs
-4
3-day runs
-8
7-day runs
85
30-day runs
More Information About apex_enemy_detect huggingface.co Model
Url of apex_enemy_detect
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Provider of apex_enemy_detect huggingface.co
Other API from PSImera
Updated:June 29 2026
Total runs:
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Run Growth:
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