---
license: apache-2.0
datasets:
- imagenet-1k
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- pytorch
- torch-dag
---
# Model Card for resnet50d_pruned_56

This is a prunned version of the [timm/resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k) model in a [toch-dag](https://github.com/TCLResearchEurope/torch-dag) format.

This model has rougly 56% 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_56 (ours)**          | 96.6 **(56%)** | 16.3 **(64%)** | 79.75% **(↓ 1.15%)**    |


\***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:** [timm/resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)

### Model Sources
- **Repository:** [timm/resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)



## 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/resnet50d_pruned_56
```

and now You are ready to load the model:

```
import torch_dag
import torch

model = torch_dag.io.load_dag_from_path('./resnet50d_pruned_56')

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