---
license: apache-2.0
datasets:
  - visdrone
model-index:
  - name: ENOT-AutoDL/yolov8s_visdrone
    results:
      - task:
          type: object-detection
          dataset:
            type: visdrone
            name: visdrone
        metrics:
          - type: precision
            value: 49,4
            name: mAP50(baseline)
          - type: precision
            value: 48,4
            name: mAP50(GMACs x2)
          - type: precision
            value: 46,0
            name: mAP50(GMACs x3)
library_name: ultralytics
pipeline_tag: object-detection
tags:
- yolov8
- ENOT-AutoDL
- yolo
- vision
- ultralytics
- object-detection
---
# ENOT-AutoDL YOLOv8 optimization on VisDrone dataset

This repository contains models accelerated with [ENOT-AutoDL](https://pypi.org/project/enot-autodl/) framework.
We trained yolov8s on VisDrone dataset and used it as our baseline.
Also we provide simple python script to measure flops and metrics.

## YOLOv8 Small

| Model                     |   GMACs     | Image Size  |    mAP50    |  mAP50-95   |
|---------------------------|:-----------:|:-----------:|:-----------:|:-----------:|
| **[YOLOv8 Ultralytics Baseline](https://docs.ultralytics.com/datasets/detect/visdrone/#dataset-yaml)** | 14,28 | 640 | 40,2 | 24,2 |
| **YOLOv8n Enot Baseline** |    8,57     |     928     |    42,9     |    26,0     |
| **YOLOv8s Enot Baseline** |    30,03    |     928     |    49,4     |    30,6     |
| **YOLOv8s (x2)**          | 15,01 (x2)  |     928     | 48,3 (-1,1) | 29,8 (-0,8) |
| **YOLOv8s (x3)**          | 10,01 (x3)  |     928     | 46,0 (-3,4) | 28,3 (-2,3) |

# Validation

To validate results, follow this steps:

1. Install all required packages:
```bash
pip install -r requrements.txt
```

2. Use validation script:
```bash
python validate.py enot_neural_architecture_selection_x2/weights/best.pt --imgsz 928
```

3. Use measure_macs script:
```bash
python measure_macs.py enot_neural_architecture_selection_x2/weights/best.pt --imgsz 928
```