Update README.md

README.md

@@ -1,3 +1,68 @@
 ---
 license: apache-2.0
+pipeline_tag: object-detection
+tags:
+- pytorch
+- torch-dag
 ---
+# Model Card for yolov8n_pruned_73
+
+This is a prunned version of the [YOLOv8n](https://github.com/ultralytics/ultralytics#models) model in a [toch-dag](https://github.com/TCLResearchEurope/torch-dag) format.
+
+This model has rougly 73% of the original model FLOPs with small metrics drop.
+
+
+| Model         | KMAPPs* | M Parameters | mAP50-95 (640x640) |
+| -----------   | ------- | ------------ | ------------------ |
+| **YOLOv8n (baseline)**   | 21.5 | 3.16 | 37.3 |
+| **yolov8n_pruned_73 (ours)**  | 15.7 **(73%)** | 2.65 **(84%)** | 34.9 **(↓ 2.4)** |
+
+
+\***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  COCO val2017 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:** Object detection
+- **License:** Apache 2.0
+- **Finetuned from model:** [YOLOv8n](https://github.com/ultralytics/ultralytics#models)
+
+### Model Sources
+- **Repository:** [YOLOv8n](https://github.com/ultralytics/ultralytics#models)
+
+
+
+## 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/yolov8n_pruned_73
+```
+
+and now You are ready to load the model:
+
+```
+import torch_dag
+import torch
+
+model = torch_dag.io.load_dag_from_path('./yolov8n_pruned_73')
+
+model.eval()
+out = model(torch.ones(1, 3, 224, 224))
+print(out.shape)
+```