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pauhidalgoo/
yolov8-indoor

Name: yolov8-indoor
Availability: InStock

object-detection

Ultralytics/YOLOv8

finetune

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Finetuned YOLOv8 Models with Freezing Variations on Indoor Object Detection

Overview

This repository contains a series of finetuned YOLOv8 models, exploring the effects of freezing various layers during training. The models are available in sizes ranging from Nano to Large and include three types of freezing configurations:

Frozen Layers: All layers except the classification head are frozen.
Normal Training: No layers are frozen.
Frozen Backbone: Only the backbone layers are frozen.

The purpose of these configurations is to evaluate the trade-offs in performance, training time, and resource usage across different model sizes and freezing strategies.

Usage

The models are designed for object detection tasks and can be directly used with the YOLOv8 framework. Refer to the README for instructions on loading and fine-tuning these models for specific datasets.

Dataset

The models were trained on the Indoor Object Dataset, which includes various household objects in diverse lighting and arrangement conditions. Each model was evaluated on mAP50 and mAP50-95 metrics to assess detection performance.

Model Performance and Comparison Table

Nano

Model Type	Training Time (s)	mAP50	mAP50-95	GPU Usage
Frozen Layers	531.36	0.5908	0.4714	0.99GB
Normal Training	561.80	0.6312	0.5557	2.81GB
Frozen Backbone	537.23	0.6253	0.5407	1.59GB

Small

Model Type	Training Time (s)	mAP50	mAP50-95	GPU Usage
Frozen Layers	568.25	0.6709	0.5168	N/A
Normal Training	607.65	0.7479	0.6605	N/A
Frozen Backbone	584.74	0.7477	0.6337	N/A

Medium

Model Type	Training Time (s)	mAP50	mAP50-95	GPU Usage
Frozen Layers	608.85	0.7367	0.5945	N/A
Normal Training	662.55	0.7432	0.6485	N/A
Frozen Backbone	633.72	0.7432	0.6537	N/A

Large

Model Type	Training Time (s)	mAP50	mAP50-95	GPU Usage
Frozen Layers	648.41	0.7435	0.6092	3.92GB
Normal Training	706.58	0.7357	0.6375	14.8GB
Frozen Backbone	680.45	0.7416	0.6650	7.5GB

Key Observations

Nano Model: Freezing the backbone provides a good trade-off in GPU usage and mAP50 performance compared to normal training.
Small Model: Normal training outperforms both freezing configurations but at the cost of higher training time.
Medium Model: The frozen backbone configuration offers nearly identical performance to normal training, with slightly reduced training time.
Large Model: The frozen backbone approach achieves the highest mAP50-95 among all configurations while using significantly less GPU memory than normal training.

How to Use the Models

Clone the repository.
Install the ultralytics library (pip install ultralytics)
Load the model size and freezing configuration of your choice.

python

from ultralytics import YOLO

# Load a finetuned YOLOv8 model
model = YOLO('path-to-model.pt')

# Perform inference
results = model('path-to-image.jpg')
results.show()

If your goal is to use it in matlab, you need to:

Clone the repository.
Clone the Matlab YOLOv8 repository.
Use the convert_to_onnx.py
Load the model in Matalb like shown in 3_YOLO_matlab.mlx

pauhidalgoo/yolov8-indoor