metadata

license: apache-2.0
pipeline_tag: object-detection
tags:
  - pytorch
  - torch-dag

Model Card for yolov8n_pruned_73

This is a prunned version of the YOLOv8n model in a toch-dag format.

This model has rougly 73% of the original model FLOPs with small metrics drop.

Model	KMAPPs*	M Parameters	mAP50-95 (640x640)
YOLOv8n (baseline)	21.5	3.16	37.3
yolov8n_pruned_73 (ours)	15.7 (73%)	2.65 (84%)	34.9 (↓ 2.4)

*KMAPPs thousands of FLOPs per input pixel

KMAPPs(model) = FLOPs(model) / (H * W * 1000), where (H, W) is the input resolution.

The accuracy was calculated on the COCO val2017 dataset. For details about image pre-processing, please refer to the original repository.

Model Details

Model Description

Developed by: TCL Research Europe
Model type: Object detection
License: Apache 2.0
Finetuned from model: YOLOv8n

Model Sources

Repository: YOLOv8n

How to Get Started with the Model

To load the model, You have to install torch-dag library, which can be done using pip by

pip install torch-dag

then, clone this repository

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/TCLResearchEurope/yolov8n_pruned_73

and now You are ready to load the model:

import torch_dag
import torch

model = torch_dag.io.load_dag_from_path('./yolov8n_pruned_73')

model.eval()
out = model(torch.ones(1, 3, 224, 224))
print(out.shape)