Update README.md

README.md

@@ -1,14 +1,33 @@
 ---
 library_name: transformers
+license: apache-2.0
+language:
+  - en
+pipeline_tag: object-detection
+tags:
+  - object-detection
+  - vision
+datasets:
+  - coco
 ---
+## D-FINE
+
+### **Overview**
+
+The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
+Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
+
+This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf)
 
 This is the HF transformers implementation for D-FINE
 
-Model: D-FINE-L-COCO
+### **Performance**
 
 D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD). 
 
-Usage:
+![COCO.png](https://huggingface.co/datasets/vladislavbro/images/resolve/main/COCO.PNG)
+
+### **How to use**
 
 ```python
 import torch
@@ -34,4 +53,12 @@ for result in results:
     for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
         score, label = score.item(), label_id.item()
         box = [round(i, 2) for i in box.tolist()]
-        print(f"{model.config.id2label[label]}: {score:.2f} {box}")
+        print(f"{model.config.id2label[label]}: {score:.2f} {box}")
+```
+
+### **Training**
+
+D-FINE is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios.
+
+### **Applications**
+D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments.