upd readme for demo

supervisely_integration/demo/README.md

@@ -7,7 +7,7 @@ After you've trained a model in Supervisely, you can download the checkpoint fro
 
 1. **Set up environment**. Install [requirements](https://github.com/supervisely-ecosystem/RT-DETRv2/blob/main/rtdetrv2_pytorch/requirements.txt) manually, or use our pre-built docker image [DockerHub](https://hub.docker.com/r/supervisely/rt-detrv2/tags). Clone [RT-DETRv2](https://github.com/supervisely-ecosystem/RT-DETRv2) repository with model implementation.
 2. **Download** your checkpoint and model files from Supervisely Platform.
-3. **Run inference**. Refer to our demo scripts: [demo_torch.py](https://github.com/supervisely-ecosystem/RT-DETRv2/blob/main/supervisely_integration/demo/demo_torch.py), [demo_onnx.py], [demo_trt.py]
+3. **Run inference**. Refer to our demo scripts: [demo_pytorch.py](https://github.com/supervisely-ecosystem/RT-DETRv2/blob/main/supervisely_integration/demo/demo_pytorch.py), [demo_onnx.py](https://github.com/supervisely-ecosystem/RT-DETRv2/blob/main/supervisely_integration/demo/demo_onnx.py), [demo_tensorrt.py](https://github.com/supervisely-ecosystem/RT-DETRv2/blob/main/supervisely_integration/demo/demo_tensorrt.py)
 
 
 ## Step-by-step guide:
@@ -41,19 +41,25 @@ git clone https://github.com/supervisely-ecosystem/RT-DETRv2
 ### 2. Download checkpoint and model files from Supervisely Platform
 
 For RT-DETRv2, you need to download the following files:
+
+**For PyTorch inference:**
 - `checkpoint.pth` - model weights, for example `best.pth`
 - `model_config.yml` - model configuration
 - `model_meta.json` - class names
 
-Go to Team Files in Supervisely Platform and download the files:
+**ONNXRuntime and TensorRT inference require only \*.onnx and \*.engine files respectively.**
+- Exported ONNX/TensorRT models can be found in the `export` folder in Team Files after training.
+
+Go to Team Files in Supervisely Platform and download the files.
 
-![team_files_download](img/team_files_download.png)
+Files for PyTorch inference:
 
+![team_files_download](https://github.com/user-attachments/assets/796bf915-fbaf-4e93-a327-f0caa51dced4)
 
 ### 3. Run inference
 
 We provide several demo scripts to run inference with your checkpoint:
 
-- [demo_torch.py](https://github.com/supervisely-ecosystem/RT-DETRv2/blob/main/supervisely_integration/demo/demo_torch.py) - simple PyTorch inference
-- [demo_onnx.py] - ONNXRuntime inference
-- [demo_trt.py] - TensorRT inference
+- [demo_pytorch.py](https://github.com/supervisely-ecosystem/RT-DETRv2/blob/main/supervisely_integration/demo/demo_pytorch.py) - simple PyTorch inference
+- [demo_onnx.py](https://github.com/supervisely-ecosystem/RT-DETRv2/blob/main/supervisely_integration/demo/demo_onnx.py) - ONNXRuntime inference
+- [demo_tensorrt.py](https://github.com/supervisely-ecosystem/RT-DETRv2/blob/main/supervisely_integration/demo/demo_tensorrt.py) - TensorRT inference