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---
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](https://keras.io/api/applications/efficientnet_v2/#efficientnetv2b0-function) model in a [toch-dag](https://github.com/TCLResearchEurope/torch-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](https://github.com/TCLResearchEurope/)
- **Model type:** Classification / feature backbone
- **License:** Apache 2.0
- **Finetuned from model:** [Keras EfficientNetV2B0](https://keras.io/api/applications/efficientnet_v2/#efficientnetv2b0-function)
### Model Sources
- **Repository:** [Keras EfficientNetV2B0](https://keras.io/api/applications/efficientnet_v2/#efficientnetv2b0-function)
## How to Get Started with the Model
To load the model, You have to install [torch-dag](https://github.com/TCLResearchEurope/torch-dag#3-installation) 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)
``` |