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TCLResearchEurope/
resnet50d_pruned_25

Name: resnet50d_pruned_25
Availability: InStock

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Model Card for resnet50d_pruned_25

This is a prunned version of the timm/resnet50d.a3_in1k model in a toch-dag format.

This model has rougly 25% of the original model FLOPs with minimal metrics drop.

Model	KMAPPs*	M Parameters	Accuracy (224x224)
timm/resnet50d.a3_in1 (baseline)	174	25.6	80.9%
resnet50d_pruned_25 (ours)	43.4 (25%)	7.2 (28%)	77.21% (↓ 3.69%)

*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 ImageNet-1k validation dataset. For details about image pre-processing, please refer to the original repository.

Model Details

Model Description

Developed by: TCL Research Europe
Model type: Classification / feature backbone
License: Apache 2.0
Finetuned from model: timm/resnet50d.a3_in1k

Model Sources

Repository: timm/resnet50d.a3_in1k

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/resnet50d_pruned_25

and now You are ready to load the model:

import torch_dag
import torch

model = torch_dag.io.load_dag_from_path('./resnet50d_pruned_25')

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