Update README.md

README.md

@@ -1,3 +1,72 @@
 ---
 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)
+```