added model export to readme and cli

README.md

@@ -46,8 +46,10 @@ pip install trolo
 The D-FINE model redefines regression tasks in DETR-based detectors using Fine-grained Distribution Refinement (FDR).
 [Official Paper](https://arxiv.org/abs/2410.13842) | [Official Repo](https://github.com/Peterande/D-FINE)
 ![D-FINE Model Stats](https://raw.githubusercontent.com/Peterande/storage/master/figs/stats_padded.png)
+</details>
 
 ( All models will be automatically downloaded when you pass the name for any task)
+
 | Model | Dataset | AP<sup>val</sup> | #Params | Latency | GFLOPs |
 | :---: | :---: | :---: |  :---: | :---: | :---: |
 `dfine-n` | COCO | **42.8** | 4M | 2.12ms | 7
@@ -56,7 +58,7 @@ The D-FINE model redefines regression tasks in DETR-based detectors using Fine-g
 `dfine-l` | COCO | **54.0** | 31M | 8.07ms | 91
 `dfine-x` | COCO | **55.8** | 62M | 12.89ms | 202
 
-</details>
+
 
 <details>
 <summary><b>RT-DETR v3 (Coming Soon)</b></summary>
@@ -109,6 +111,23 @@ poltted_preds = predictor.visualize(show=True, save=True) # or get visualized ou
 Visit Inference Docs for more details
 
 
+
+### Model export    
+ Example export command: 
+ ```bash 
+ trolo export --model dfine-n --export_format onnx --input_size 640  
+ ```
+  Python API: 
+
+```
+python from trolo.inference import ModelExporter model_path = "/path/to/model"  
+input_size = 640 # Inference resolution  
+export_format = "onnx"  
+exporter = ModelExporter(model=model_path) exporter.export(input_size=input_size, export_format=export_format)  
+``` 
+Visit Export Docs for more details
+
+
 ### Training
 <b>Example training command:</b>
 ```bash

trolo/cli/cli.py

@@ -2,6 +2,7 @@
 from pathlib import Path
 from trolo.trainers.detection import DetectionTrainer
 from trolo.inference.detection import DetectionPredictor
+from trolo.export.exporter import ModelExporter
 from trolo.utils.smart_defaults import (
     infer_device,
     infer_pretrained_model,
@@ -82,6 +83,18 @@ def eval(model, device, batch_size):
     eval_detection(model, device=device, **overrides)
 
 
+@cli.command()
+@click.option("--model", "-m", type=str, default=DEFAULT_MODEL, help="Model name or path")
+@click.option("--input_size", "-i", type=int, default=640, help="Inference input size")
+@click.option("--export_format", "-e", type=str, default=None, help="Export format")
+def export(model, input_size, export_format):
+    """Run inference on images using a trained model"""
+
+    # Initialize predictor with smart defaults
+    exporter = ModelExporter(model=infer_pretrained_model(model))
+    exporter.export(input_size=input_size, export_format=export_format)
+
+
 def main():
     cli()