metadata
license: apache-2.0
datasets:
- imagenet-1k
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- pytorch
- torch-dag
Model Card for efficientnetv2_b0_pruned_61
This is a prunned version of the Keras EfficientNetV2B0 model in a toch-dag format.
This model has rougly 61% of the original model FLOPs with minimal metrics drop.
Model | KMAPPs* | M Parameters | Accuracy (224x224) |
---|---|---|---|
Keras EfficientNetV2B (baseline) | 29 | 7.1 | 78.69% |
efficientnetv2_b0_pruned_61 (ours) | 17.7 (61%) | 6.5 (92%) | 78.14% (↓ 0.55%) |
*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: Keras EfficientNetV2B0
Model Sources
- Repository: Keras EfficientNetV2B0
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/efficientnetv2_b0_pruned_61
and now You are ready to load the model:
import torch_dag
import torch
model = torch_dag.io.load_dag_from_path('./efficientnetv2_b0_pruned_61')
model.eval()
out = model(torch.ones(1, 3, 224, 224))
print(out.shape)