diff --git a/README.md b/README.md
index e970e279..bdb53356 100644
--- a/README.md
+++ b/README.md
@@ -338,6 +338,11 @@ python scripts/gradio_demo.py \
 > [!TIP]
 > Live inference is provided as an independent script so that you can load this script into any deployment devices without having to install all the (large) dependencies in this repo. The script only requires `onnxruntime`, `cv2` and `numpy` to run.
 
+There are two live inference scripts:
+
+1. `live_inference.py` - Run live inference on a video, image or webcam using ONNXRuntime. This script requires a pre-exported ONNX model.
+2. `live_inference_pretrained.py` - Automatically download the pretrained model, convert to ONNX, and run inference in one go.
+
 Run live inference on a video, image or webcam using ONNXRuntime. This runs on CPU by default.
 If you would like to use the CUDA backend, install the `onnxruntime-gpu` package and uninstall the `onnxruntime` package.
 
@@ -378,6 +383,18 @@ Webcam video width at 320x240 pixels (240p):
 https://github.com/user-attachments/assets/f4afff9c-3e6d-4965-ab86-0d4de7ce1a44
 
 
+To run live inference with pretrained model on webcam, use the following command
+```bash
+python scripts/live_inference_pretrained.py --webcam
+```
+This downloads the pretrained model, converts it to ONNX, and runs inference on the webcam. All other arguments are optional but they are similar to the `live_inference.py` script.
+
+The output is as follows
+
+
+https://github.com/user-attachments/assets/5aca6044-db81-4988-937e-2447a7cbe06e
+
+
 
 
 For video inference, specify the path to the video file as the input. Output video will be saved as `onnx_result.mp4` in the current directory.
@@ -393,7 +410,12 @@ python scripts/live_inference.py
 ```
 https://github.com/user-attachments/assets/6bc1dc6a-a223-4220-954d-2dab5c75b4a8
 
-The following is an inference using the pre-trained model `deim_hgnetv2_x` trained on COCO. See how I exported the pre-trained model to onnx in this notebook [here](nbs/export.ipynb).
+
+The following is an inference using the pre-trained model `deim_hgnetv2_x` trained on COCO.
+
+```bash
+python scripts/live_inference_pretrained.py --model deim_hgnetv2_x --video video.mp4
+```
 
 https://github.com/user-attachments/assets/77070ea4-8407-4648-ade3-01cacd77b51b
 
@@ -473,6 +495,11 @@ pixi run -e cuda live-inference --onnx model.onnx --webcam --provider cuda --cla
 pixi run -e cpu live-inference --onnx model.onnx --input video.mp4 --class-names classes.txt --inference-size 320
 ```
 
+Live inference with pretrained model on webcam
+```bash
+pixi run -e cuda live-inference-pretrained --webcam
+```
+
 Launch Gradio app
 ```bash
 pixi run gradio-demo --model "best_prep.onnx" --classes "classes.txt" --examples "Rock Paper Scissors SXSW.v14i.coco/test"
diff --git a/nbs/pretrained-model-inference.ipynb b/nbs/pretrained-model-inference.ipynb
index 0668ecc8..aa9ba02b 100644
--- a/nbs/pretrained-model-inference.ipynb
+++ b/nbs/pretrained-model-inference.ipynb
@@ -58,15 +58,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2025-03-11 23:47:58.625\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m108\u001b[0m - \u001b[1mInitializing Predictor with device=auto\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:58.646\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_setup_device\u001b[0m:\u001b[36m155\u001b[0m - \u001b[1mAuto-selected device: cuda\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:58.693\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_download_checkpoint\u001b[0m:\u001b[36m189\u001b[0m - \u001b[1mUsing cached checkpoint from /home/dnth/.cache/deim/checkpoints/deim_hgnetv2_x.pth\u001b[0m\n",
-      "/home/dnth/Desktop/DEIMKit/src/deimkit/predictor.py:198: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
-      "  checkpoint = torch.load(checkpoint_path, map_location=\"cpu\")\n",
-      "\u001b[32m2025-03-11 23:47:58.812\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_setup_model_config\u001b[0m:\u001b[36m219\u001b[0m - \u001b[1mLoading configuration from model name: deim_hgnetv2_x\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:58.826\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_setup_model_config\u001b[0m:\u001b[36m226\u001b[0m - \u001b[1mUpdating model configuration for 80 classes\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:59.161\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_load_model_weights\u001b[0m:\u001b[36m239\u001b[0m - \u001b[1mSuccessfully loaded checkpoint weights\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:59.435\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m142\u001b[0m - \u001b[32m\u001b[1mPredictor initialization complete\u001b[0m\n"
+      "\u001b[32m2025-04-04 11:50:18.787\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mInitializing Predictor with device=auto\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:50:18.835\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_setup_device\u001b[0m:\u001b[36m157\u001b[0m - \u001b[1mAuto-selected device: cuda\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:50:18.837\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.model\u001b[0m:\u001b[36m_download_checkpoint\u001b[0m:\u001b[36m165\u001b[0m - \u001b[1mDownloading checkpoint for model deim_hgnetv2_x...\u001b[0m\n",
+      "deim_hgnetv2_x: 100%|██████████| 241M/241M [01:03<00:00, 3.94MiB/s] \n",
+      "\u001b[32m2025-04-04 11:51:24.283\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.model\u001b[0m:\u001b[36m_download_checkpoint\u001b[0m:\u001b[36m184\u001b[0m - \u001b[32m\u001b[1mDownloaded checkpoint to checkpoints/deim_hgnetv2_x.pth\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:24.484\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_setup_model_config\u001b[0m:\u001b[36m193\u001b[0m - \u001b[1mLoading configuration from model name: deim_hgnetv2_x\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:24.500\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_setup_model_config\u001b[0m:\u001b[36m201\u001b[0m - \u001b[1mUpdating model configuration for 80 classes\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:24.967\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_load_model_weights\u001b[0m:\u001b[36m214\u001b[0m - \u001b[1mSuccessfully loaded checkpoint weights\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.240\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m146\u001b[0m - \u001b[32m\u001b[1mPredictor initialization complete\u001b[0m\n"
      ]
     }
    ],
@@ -83,9 +83,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2025-03-11 23:47:59.444\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m378\u001b[0m - \u001b[34m\u001b[1mLoading image from path: ../assets/sample_images/95aa6ee8b4618251.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:59.753\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_single_image\u001b[0m:\u001b[36m436\u001b[0m - \u001b[34m\u001b[1mPrediction complete. Found 54 objects\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:59.754\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_single_image\u001b[0m:\u001b[36m448\u001b[0m - \u001b[34m\u001b[1mGenerating visualization\u001b[0m\n"
+      "\u001b[32m2025-04-04 11:51:25.254\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m353\u001b[0m - \u001b[34m\u001b[1mLoading image from path: ../assets/sample_images/95aa6ee8b4618251.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.559\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_single_image\u001b[0m:\u001b[36m411\u001b[0m - \u001b[34m\u001b[1mPrediction complete. Found 54 objects\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.559\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_single_image\u001b[0m:\u001b[36m423\u001b[0m - \u001b[34m\u001b[1mGenerating visualization\u001b[0m\n"
      ]
     },
     {
@@ -198,10 +198,60 @@
        "       [ 8.86776001e+02,  1.66908249e+02,  9.20016235e+02,\n",
        "         2.04338440e+02],\n",
        "       [ 5.73231079e+02,  1.72442642e+02,  6.11189819e+02,\n",
-       "         2.36817322e+02]], dtype=float32), labels=array([ 0,  0,  0, 63, 62,  0, 63,  0, 63,  0, 64,  0, 66,  0, 63,  0,  0,\n",
-       "        0,  0, 62,  0, 66,  0,  0, 62,  0,  0,  0,  0, 26,  0,  0,  0, 62,\n",
-       "       56, 64, 62, 24, 56, 56,  0, 62, 62,  0,  0, 63,  0,  0, 27, 62,  0,\n",
-       "        0,  0,  0]), scores=array([0.89853793, 0.88263005, 0.86926806, 0.84961444, 0.8242308 ,\n",
+       "         2.36817322e+02]], dtype=float32), labels=array([[ 0],\n",
+       "       [ 0],\n",
+       "       [ 0],\n",
+       "       [63],\n",
+       "       [62],\n",
+       "       [ 0],\n",
+       "       [63],\n",
+       "       [ 0],\n",
+       "       [63],\n",
+       "       [ 0],\n",
+       "       [64],\n",
+       "       [ 0],\n",
+       "       [66],\n",
+       "       [ 0],\n",
+       "       [63],\n",
+       "       [ 0],\n",
+       "       [ 0],\n",
+       "       [ 0],\n",
+       "       [ 0],\n",
+       "       [62],\n",
+       "       [ 0],\n",
+       "       [66],\n",
+       "       [ 0],\n",
+       "       [ 0],\n",
+       "       [62],\n",
+       "       [ 0],\n",
+       "       [ 0],\n",
+       "       [ 0],\n",
+       "       [ 0],\n",
+       "       [26],\n",
+       "       [ 0],\n",
+       "       [ 0],\n",
+       "       [ 0],\n",
+       "       [62],\n",
+       "       [56],\n",
+       "       [64],\n",
+       "       [62],\n",
+       "       [24],\n",
+       "       [56],\n",
+       "       [56],\n",
+       "       [ 0],\n",
+       "       [62],\n",
+       "       [62],\n",
+       "       [ 0],\n",
+       "       [ 0],\n",
+       "       [63],\n",
+       "       [ 0],\n",
+       "       [ 0],\n",
+       "       [27],\n",
+       "       [62],\n",
+       "       [ 0],\n",
+       "       [ 0],\n",
+       "       [ 0],\n",
+       "       [ 0]]), scores=array([0.89853793, 0.88263005, 0.86926806, 0.84961444, 0.8242308 ,\n",
        "       0.79215425, 0.7875311 , 0.7805078 , 0.77490765, 0.7084651 ,\n",
        "       0.666344  , 0.6507428 , 0.6380513 , 0.6249805 , 0.578291  ,\n",
        "       0.57175404, 0.5378879 , 0.5360249 , 0.52354723, 0.49983203,\n",
@@ -211,7 +261,7 @@
        "       0.34931734, 0.34188655, 0.33969006, 0.3176732 , 0.31424755,\n",
        "       0.30397958, 0.3008672 , 0.2980086 , 0.29161483, 0.28003258,\n",
        "       0.2797553 , 0.27324918, 0.27295813, 0.2728039 , 0.26906097,\n",
-       "       0.26181173, 0.2607437 , 0.25970253, 0.2590194 ], dtype=float32), class_names=['person', 'person', 'person', 'laptop', 'tv', 'person', 'laptop', 'person', 'laptop', 'person', 'mouse', 'person', 'keyboard', 'person', 'laptop', 'person', 'person', 'person', 'person', 'tv', 'person', 'keyboard', 'person', 'person', 'tv', 'person', 'person', 'person', 'person', 'handbag', 'person', 'person', 'person', 'tv', 'chair', 'mouse', 'tv', 'backpack', 'chair', 'chair', 'person', 'tv', 'tv', 'person', 'person', 'laptop', 'person', 'person', 'tie', 'tv', 'person', 'person', 'person', 'person'], visualization=<PIL.Image.Image image mode=RGB size=1024x685 at 0x7C75293509B0>)"
+       "       0.26181173, 0.2607437 , 0.25970253, 0.2590194 ], dtype=float32), class_names=['person', 'person', 'person', 'laptop', 'tv', 'person', 'laptop', 'person', 'laptop', 'person', 'mouse', 'person', 'keyboard', 'person', 'laptop', 'person', 'person', 'person', 'person', 'tv', 'person', 'keyboard', 'person', 'person', 'tv', 'person', 'person', 'person', 'person', 'handbag', 'person', 'person', 'person', 'tv', 'chair', 'mouse', 'tv', 'backpack', 'chair', 'chair', 'person', 'tv', 'tv', 'person', 'person', 'laptop', 'person', 'person', 'tie', 'tv', 'person', 'person', 'person', 'person'], visualization=<PIL.Image.Image image mode=RGB size=1024x685 at 0x7E0D70D50E10>)"
       ]
      },
      "execution_count": 5,
@@ -295,36 +345,36 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2025-03-11 23:47:59.851\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36mpredict_batch\u001b[0m:\u001b[36m486\u001b[0m - \u001b[1mProcessing batch of 14 images with batch_size=16\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:59.852\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m523\u001b[0m - \u001b[34m\u001b[1mLoading image 0 from path: ../assets/sample_images/338cc8a7f27690dc.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:59.866\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m523\u001b[0m - \u001b[34m\u001b[1mLoading image 1 from path: ../assets/sample_images/151f78f5d01cc521.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:59.880\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m523\u001b[0m - \u001b[34m\u001b[1mLoading image 2 from path: ../assets/sample_images/336fc2e7056d5674.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:59.896\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m523\u001b[0m - \u001b[34m\u001b[1mLoading image 3 from path: ../assets/sample_images/95aa6ee8b4618251.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:59.912\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m523\u001b[0m - \u001b[34m\u001b[1mLoading image 4 from path: ../assets/sample_images/277db5b27c322ee3.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:59.925\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m523\u001b[0m - \u001b[34m\u001b[1mLoading image 5 from path: ../assets/sample_images/223adcceb2728075.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:59.938\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m523\u001b[0m - \u001b[34m\u001b[1mLoading image 6 from path: ../assets/sample_images/362de0abae134896.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:59.965\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m523\u001b[0m - \u001b[34m\u001b[1mLoading image 7 from path: ../assets/sample_images/221bf9a3ed4cc6eb.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:59.977\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m523\u001b[0m - \u001b[34m\u001b[1mLoading image 8 from path: ../assets/sample_images/130aaba4e2a90020.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:47:59.991\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m523\u001b[0m - \u001b[34m\u001b[1mLoading image 9 from path: ../assets/sample_images/93ad9ffdca68c96a.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.006\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m523\u001b[0m - \u001b[34m\u001b[1mLoading image 10 from path: ../assets/sample_images/210f8542e838a1ba.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.020\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m523\u001b[0m - \u001b[34m\u001b[1mLoading image 11 from path: ../assets/sample_images/246ff47a30d47a39.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.033\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m523\u001b[0m - \u001b[34m\u001b[1mLoading image 12 from path: ../assets/sample_images/368b7164bf919711.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.046\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m523\u001b[0m - \u001b[34m\u001b[1mLoading image 13 from path: ../assets/sample_images/245f548ec2bffe92.jpg\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.294\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m590\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 0\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.296\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m590\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 1\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.299\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m590\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 2\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.305\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m590\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 3\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.310\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m590\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 4\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.312\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m590\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 5\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.316\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m590\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 6\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.319\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m590\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 7\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.323\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m590\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 8\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.326\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m590\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 9\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.329\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m590\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 10\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.333\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m590\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 11\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.338\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m590\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 12\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.342\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m590\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 13\u001b[0m\n",
-      "\u001b[32m2025-03-11 23:48:00.345\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36mpredict_batch\u001b[0m:\u001b[36m499\u001b[0m - \u001b[32m\u001b[1mBatch processing complete. Processed 14 images\u001b[0m\n"
+      "\u001b[32m2025-04-04 11:51:25.667\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36mpredict_batch\u001b[0m:\u001b[36m461\u001b[0m - \u001b[1mProcessing batch of 14 images with batch_size=16\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.671\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m498\u001b[0m - \u001b[34m\u001b[1mLoading image 0 from path: ../assets/sample_images/338cc8a7f27690dc.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.702\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m498\u001b[0m - \u001b[34m\u001b[1mLoading image 1 from path: ../assets/sample_images/151f78f5d01cc521.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.713\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m498\u001b[0m - \u001b[34m\u001b[1mLoading image 2 from path: ../assets/sample_images/336fc2e7056d5674.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.731\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m498\u001b[0m - \u001b[34m\u001b[1mLoading image 3 from path: ../assets/sample_images/95aa6ee8b4618251.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.743\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m498\u001b[0m - \u001b[34m\u001b[1mLoading image 4 from path: ../assets/sample_images/277db5b27c322ee3.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.756\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m498\u001b[0m - \u001b[34m\u001b[1mLoading image 5 from path: ../assets/sample_images/223adcceb2728075.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.767\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m498\u001b[0m - \u001b[34m\u001b[1mLoading image 6 from path: ../assets/sample_images/362de0abae134896.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.778\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m498\u001b[0m - \u001b[34m\u001b[1mLoading image 7 from path: ../assets/sample_images/221bf9a3ed4cc6eb.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.788\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m498\u001b[0m - \u001b[34m\u001b[1mLoading image 8 from path: ../assets/sample_images/130aaba4e2a90020.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.802\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m498\u001b[0m - \u001b[34m\u001b[1mLoading image 9 from path: ../assets/sample_images/93ad9ffdca68c96a.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.815\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m498\u001b[0m - \u001b[34m\u001b[1mLoading image 10 from path: ../assets/sample_images/210f8542e838a1ba.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.829\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m498\u001b[0m - \u001b[34m\u001b[1mLoading image 11 from path: ../assets/sample_images/246ff47a30d47a39.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.842\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m498\u001b[0m - \u001b[34m\u001b[1mLoading image 12 from path: ../assets/sample_images/368b7164bf919711.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:25.856\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m498\u001b[0m - \u001b[34m\u001b[1mLoading image 13 from path: ../assets/sample_images/245f548ec2bffe92.jpg\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.109\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m565\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 0\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.112\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m565\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 1\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.115\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m565\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 2\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.122\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m565\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 3\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.126\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m565\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 4\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.129\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m565\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 5\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.133\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m565\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 6\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.136\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m565\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 7\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.140\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m565\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 8\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.144\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m565\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 9\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.146\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m565\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 10\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.151\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m565\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 11\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.155\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m565\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 12\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.159\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_process_image_batch\u001b[0m:\u001b[36m565\u001b[0m - \u001b[34m\u001b[1mGenerating visualization for image 13\u001b[0m\n",
+      "\u001b[32m2025-04-04 11:51:26.162\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36mpredict_batch\u001b[0m:\u001b[36m474\u001b[0m - \u001b[32m\u001b[1mBatch processing complete. Processed 14 images\u001b[0m\n"
      ]
     }
    ],
@@ -342,7 +392,8 @@
       "text/plain": [
        "[PredictionResult(boxes=array([[ 327.80664 ,   38.337944,  563.6377  ,  320.62366 ],\n",
        "        [ 966.2616  ,   67.23396 , 1023.87    ,  199.91    ]],\n",
-       "       dtype=float32), labels=array([0, 0]), scores=array([0.78109276, 0.2795733 ], dtype=float32), class_names=['person', 'person'], visualization=<PIL.Image.Image image mode=RGB size=1024x683 at 0x7C7529BC9610>),\n",
+       "       dtype=float32), labels=array([[0],\n",
+       "        [0]]), scores=array([0.78109276, 0.2795733 ], dtype=float32), class_names=['person', 'person'], visualization=<PIL.Image.Image image mode=RGB size=1024x683 at 0x7E0D7119D8D0>),\n",
        " PredictionResult(boxes=array([[5.06639160e+02, 1.94403095e+01, 8.87505615e+02, 7.28904480e+02],\n",
        "        [1.83071899e+00, 1.77414227e+01, 1.02360876e+03, 7.32558960e+02],\n",
        "        [2.79754639e+02, 1.45060539e+00, 5.00805237e+02, 4.20908813e+02],\n",
@@ -351,8 +402,15 @@
        "        [8.78440063e+02, 2.26950407e+00, 1.02342737e+03, 3.65461304e+02],\n",
        "        [5.70297241e-02, 1.45663879e+02, 4.99915390e+01, 3.26450226e+02],\n",
        "        [2.87060547e+00, 2.80488159e+02, 1.02302527e+03, 7.32256531e+02]],\n",
-       "       dtype=float32), labels=array([40, 60, 39, 39, 39, 56, 41, 60]), scores=array([0.9567754 , 0.71696526, 0.43545035, 0.39587805, 0.38604254,\n",
-       "        0.344238  , 0.32765186, 0.3247362 ], dtype=float32), class_names=['wine glass', 'dining table', 'bottle', 'bottle', 'bottle', 'chair', 'cup', 'dining table'], visualization=<PIL.Image.Image image mode=RGB size=1024x740 at 0x7C75294ED5E0>),\n",
+       "       dtype=float32), labels=array([[40],\n",
+       "        [60],\n",
+       "        [39],\n",
+       "        [39],\n",
+       "        [39],\n",
+       "        [56],\n",
+       "        [41],\n",
+       "        [60]]), scores=array([0.9567754 , 0.71696526, 0.43545035, 0.39587805, 0.38604254,\n",
+       "        0.344238  , 0.32765186, 0.3247362 ], dtype=float32), class_names=['wine glass', 'dining table', 'bottle', 'bottle', 'bottle', 'chair', 'cup', 'dining table'], visualization=<PIL.Image.Image image mode=RGB size=1024x740 at 0x7E0D702944D0>),\n",
        " PredictionResult(boxes=array([[-3.66210938e-03,  9.93643799e+01,  1.02280640e+03,\n",
        "          1.01478198e+03],\n",
        "        [ 5.33190552e+02,  1.02917358e+02,  8.48738647e+02,\n",
@@ -372,9 +430,18 @@
        "        [ 2.69857178e+02,  2.84319214e+02,  5.23064270e+02,\n",
        "          5.31761353e+02],\n",
        "        [ 3.42011658e+02,  2.15468735e+02,  5.42365906e+02,\n",
-       "          3.96996216e+02]], dtype=float32), labels=array([55, 55, 55, 60, 55, 55, 60, 55, 55, 55]), scores=array([0.7725273 , 0.7424967 , 0.4684447 , 0.44936326, 0.40752068,\n",
+       "          3.96996216e+02]], dtype=float32), labels=array([[55],\n",
+       "        [55],\n",
+       "        [55],\n",
+       "        [60],\n",
+       "        [55],\n",
+       "        [55],\n",
+       "        [60],\n",
+       "        [55],\n",
+       "        [55],\n",
+       "        [55]]), scores=array([0.7725273 , 0.7424967 , 0.4684447 , 0.44936326, 0.40752068,\n",
        "        0.33794802, 0.3349641 , 0.3305861 , 0.30056012, 0.27949557],\n",
-       "       dtype=float32), class_names=['cake', 'cake', 'cake', 'dining table', 'cake', 'cake', 'dining table', 'cake', 'cake', 'cake'], visualization=<PIL.Image.Image image mode=RGB size=1024x1024 at 0x7C75294EED20>),\n",
+       "       dtype=float32), class_names=['cake', 'cake', 'cake', 'dining table', 'cake', 'cake', 'dining table', 'cake', 'cake', 'cake'], visualization=<PIL.Image.Image image mode=RGB size=1024x1024 at 0x7E0D71B4BD90>),\n",
        " PredictionResult(boxes=array([[ 1.59410950e+02,  3.04520111e+02,  3.07281860e+02,\n",
        "          6.81950134e+02],\n",
        "        [ 7.41914001e+02,  3.09075897e+02,  9.18117981e+02,\n",
@@ -484,10 +551,61 @@
        "        [ 5.73232483e+02,  1.72442459e+02,  6.11173889e+02,\n",
        "          2.36815384e+02],\n",
        "        [ 9.74448364e+02,  4.38700562e+02,  1.02400171e+03,\n",
-       "          5.33402039e+02]], dtype=float32), labels=array([ 0,  0,  0, 63, 62,  0, 63,  0, 63,  0, 64,  0, 66,  0, 63,  0,  0,\n",
-       "         0,  0, 62,  0, 66,  0,  0, 62,  0,  0,  0,  0, 26,  0,  0,  0, 62,\n",
-       "        56, 64, 62, 24, 56, 56,  0, 62, 62,  0,  0, 63,  0,  0, 27, 62,  0,\n",
-       "         0,  0,  0, 24]), scores=array([0.89852947, 0.8826244 , 0.8692593 , 0.8496177 , 0.8242701 ,\n",
+       "          5.33402039e+02]], dtype=float32), labels=array([[ 0],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [63],\n",
+       "        [62],\n",
+       "        [ 0],\n",
+       "        [63],\n",
+       "        [ 0],\n",
+       "        [63],\n",
+       "        [ 0],\n",
+       "        [64],\n",
+       "        [ 0],\n",
+       "        [66],\n",
+       "        [ 0],\n",
+       "        [63],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [62],\n",
+       "        [ 0],\n",
+       "        [66],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [62],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [26],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [62],\n",
+       "        [56],\n",
+       "        [64],\n",
+       "        [62],\n",
+       "        [24],\n",
+       "        [56],\n",
+       "        [56],\n",
+       "        [ 0],\n",
+       "        [62],\n",
+       "        [62],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [63],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [27],\n",
+       "        [62],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [24]]), scores=array([0.89852947, 0.8826244 , 0.8692593 , 0.8496177 , 0.8242701 ,\n",
        "        0.79211164, 0.78750825, 0.78054696, 0.7750364 , 0.70845443,\n",
        "        0.66623205, 0.6508312 , 0.63816243, 0.62516147, 0.57843167,\n",
        "        0.5719267 , 0.53797   , 0.5361347 , 0.5251317 , 0.5001882 ,\n",
@@ -498,9 +616,10 @@
        "        0.30374694, 0.30090356, 0.29831818, 0.2917783 , 0.28005406,\n",
        "        0.27947924, 0.27291718, 0.2728904 , 0.27281892, 0.26928812,\n",
        "        0.26171884, 0.26042277, 0.25983995, 0.25874868, 0.25001007],\n",
-       "       dtype=float32), class_names=['person', 'person', 'person', 'laptop', 'tv', 'person', 'laptop', 'person', 'laptop', 'person', 'mouse', 'person', 'keyboard', 'person', 'laptop', 'person', 'person', 'person', 'person', 'tv', 'person', 'keyboard', 'person', 'person', 'tv', 'person', 'person', 'person', 'person', 'handbag', 'person', 'person', 'person', 'tv', 'chair', 'mouse', 'tv', 'backpack', 'chair', 'chair', 'person', 'tv', 'tv', 'person', 'person', 'laptop', 'person', 'person', 'tie', 'tv', 'person', 'person', 'person', 'person', 'backpack'], visualization=<PIL.Image.Image image mode=RGB size=1024x685 at 0x7C75294EECC0>),\n",
+       "       dtype=float32), class_names=['person', 'person', 'person', 'laptop', 'tv', 'person', 'laptop', 'person', 'laptop', 'person', 'mouse', 'person', 'keyboard', 'person', 'laptop', 'person', 'person', 'person', 'person', 'tv', 'person', 'keyboard', 'person', 'person', 'tv', 'person', 'person', 'person', 'person', 'handbag', 'person', 'person', 'person', 'tv', 'chair', 'mouse', 'tv', 'backpack', 'chair', 'chair', 'person', 'tv', 'tv', 'person', 'person', 'laptop', 'person', 'person', 'tie', 'tv', 'person', 'person', 'person', 'person', 'backpack'], visualization=<PIL.Image.Image image mode=RGB size=1024x685 at 0x7E0D70204C10>),\n",
        " PredictionResult(boxes=array([[619.104  , 271.03265, 861.1582 , 423.11118],\n",
-       "        [158.44098, 424.03067, 312.18756, 518.8823 ]], dtype=float32), labels=array([1, 1]), scores=array([0.9270227, 0.908709 ], dtype=float32), class_names=['bicycle', 'bicycle'], visualization=<PIL.Image.Image image mode=RGB size=1024x587 at 0x7C75294ED010>),\n",
+       "        [158.44098, 424.03067, 312.18756, 518.8823 ]], dtype=float32), labels=array([[1],\n",
+       "        [1]]), scores=array([0.9270227, 0.908709 ], dtype=float32), class_names=['bicycle', 'bicycle'], visualization=<PIL.Image.Image image mode=RGB size=1024x587 at 0x7E0D70205510>),\n",
        " PredictionResult(boxes=array([[2.34405746e+02, 5.83390045e+01, 6.90530884e+02, 5.64451355e+02],\n",
        "        [9.39086914e-01, 4.92897491e+02, 7.42946716e+02, 7.60584839e+02],\n",
        "        [7.71715698e+02, 3.14431549e+02, 8.84230469e+02, 3.92016357e+02],\n",
@@ -548,9 +667,53 @@
        "        [1.73308090e+02, 1.87920990e+02, 1.94129318e+02, 2.12537781e+02],\n",
        "        [1.60617584e+02, 2.05824768e+02, 1.73824707e+02, 2.35381134e+02],\n",
        "        [1.67730530e+02, 2.44348755e+02, 2.37179169e+02, 3.15651764e+02]],\n",
-       "       dtype=float32), labels=array([ 0, 60,  1,  0,  0,  2,  2, 60,  2,  0,  1,  9,  1,  0, 44, 55, 13,\n",
-       "        13, 52, 60,  2,  0,  2,  5,  1,  2, 48, 48,  0,  3,  1,  0, 52, 55,\n",
-       "         1,  0,  2,  9,  2,  1,  7, 43,  9,  9,  9,  9,  7]), scores=array([0.9450894 , 0.8822315 , 0.79589367, 0.7522484 , 0.6860587 ,\n",
+       "       dtype=float32), labels=array([[ 0],\n",
+       "        [60],\n",
+       "        [ 1],\n",
+       "        [ 0],\n",
+       "        [ 0],\n",
+       "        [ 2],\n",
+       "        [ 2],\n",
+       "        [60],\n",
+       "        [ 2],\n",
+       "        [ 0],\n",
+       "        [ 1],\n",
+       "        [ 9],\n",
+       "        [ 1],\n",
+       "        [ 0],\n",
+       "        [44],\n",
+       "        [55],\n",
+       "        [13],\n",
+       "        [13],\n",
+       "        [52],\n",
+       "        [60],\n",
+       "        [ 2],\n",
+       "        [ 0],\n",
+       "        [ 2],\n",
+       "        [ 5],\n",
+       "        [ 1],\n",
+       "        [ 2],\n",
+       "        [48],\n",
+       "        [48],\n",
+       "        [ 0],\n",
+       "        [ 3],\n",
+       "        [ 1],\n",
+       "        [ 0],\n",
+       "        [52],\n",
+       "        [55],\n",
+       "        [ 1],\n",
+       "        [ 0],\n",
+       "        [ 2],\n",
+       "        [ 9],\n",
+       "        [ 2],\n",
+       "        [ 1],\n",
+       "        [ 7],\n",
+       "        [43],\n",
+       "        [ 9],\n",
+       "        [ 9],\n",
+       "        [ 9],\n",
+       "        [ 9],\n",
+       "        [ 7]]), scores=array([0.9450894 , 0.8822315 , 0.79589367, 0.7522484 , 0.6860587 ,\n",
        "        0.6359941 , 0.6333548 , 0.5939115 , 0.58851963, 0.58526903,\n",
        "        0.5574593 , 0.546802  , 0.52649385, 0.44012004, 0.42359227,\n",
        "        0.42154673, 0.41075468, 0.40991455, 0.4036927 , 0.38872105,\n",
@@ -559,11 +722,14 @@
        "        0.30068624, 0.30021295, 0.2862656 , 0.28040218, 0.27984113,\n",
        "        0.27983758, 0.27701148, 0.27280134, 0.27113762, 0.26653907,\n",
        "        0.26583695, 0.26145327, 0.25771132, 0.25671494, 0.25505957,\n",
-       "        0.25155178, 0.2505064 ], dtype=float32), class_names=['person', 'dining table', 'bicycle', 'person', 'person', 'car', 'car', 'dining table', 'car', 'person', 'bicycle', 'traffic light', 'bicycle', 'person', 'spoon', 'cake', 'bench', 'bench', 'hot dog', 'dining table', 'car', 'person', 'car', 'bus', 'bicycle', 'car', 'sandwich', 'sandwich', 'person', 'motorcycle', 'bicycle', 'person', 'hot dog', 'cake', 'bicycle', 'person', 'car', 'traffic light', 'car', 'bicycle', 'truck', 'knife', 'traffic light', 'traffic light', 'traffic light', 'traffic light', 'truck'], visualization=<PIL.Image.Image image mode=RGB size=1024x768 at 0x7C75294EEB70>),\n",
+       "        0.25155178, 0.2505064 ], dtype=float32), class_names=['person', 'dining table', 'bicycle', 'person', 'person', 'car', 'car', 'dining table', 'car', 'person', 'bicycle', 'traffic light', 'bicycle', 'person', 'spoon', 'cake', 'bench', 'bench', 'hot dog', 'dining table', 'car', 'person', 'car', 'bus', 'bicycle', 'car', 'sandwich', 'sandwich', 'person', 'motorcycle', 'bicycle', 'person', 'hot dog', 'cake', 'bicycle', 'person', 'car', 'traffic light', 'car', 'bicycle', 'truck', 'knife', 'traffic light', 'traffic light', 'traffic light', 'traffic light', 'truck'], visualization=<PIL.Image.Image image mode=RGB size=1024x768 at 0x7E0D702049D0>),\n",
        " PredictionResult(boxes=array([[545.621  ,  85.08408, 728.9973 , 643.1565 ],\n",
        "        [ 71.0173 , 298.91974, 284.14465, 456.5686 ],\n",
        "        [713.2698 , 213.42928, 728.3601 , 229.52188],\n",
-       "        [695.1057 , 213.42659, 728.57446, 262.3471 ]], dtype=float32), labels=array([ 0, 62, 67, 67]), scores=array([0.91540164, 0.70148945, 0.49184456, 0.40175018], dtype=float32), class_names=['person', 'tv', 'cell phone', 'cell phone'], visualization=<PIL.Image.Image image mode=RGB size=1024x683 at 0x7C75294ECD10>),\n",
+       "        [695.1057 , 213.42659, 728.57446, 262.3471 ]], dtype=float32), labels=array([[ 0],\n",
+       "        [62],\n",
+       "        [67],\n",
+       "        [67]]), scores=array([0.91540164, 0.70148945, 0.49184456, 0.40175018], dtype=float32), class_names=['person', 'tv', 'cell phone', 'cell phone'], visualization=<PIL.Image.Image image mode=RGB size=1024x683 at 0x7E0D70204E50>),\n",
        " PredictionResult(boxes=array([[424.3211  , 179.6821  , 514.4095  , 257.94376 ],\n",
        "        [260.55386 , 155.19977 , 393.8666  , 270.72275 ],\n",
        "        [ 95.65771 , 117.0886  , 269.27197 , 273.1783  ],\n",
@@ -578,31 +744,57 @@
        "        [579.53564 , 212.94328 , 689.0907  , 267.49313 ],\n",
        "        [ 95.6734  , 126.51809 , 393.17505 , 273.62247 ],\n",
        "        [554.2109  , 235.9383  , 593.12714 , 264.07092 ],\n",
-       "        [ 95.092224, 112.55119 , 393.05707 , 273.5855  ]], dtype=float32), labels=array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]), scores=array([0.68943435, 0.665106  , 0.5542661 , 0.5507727 , 0.46040758,\n",
+       "        [ 95.092224, 112.55119 , 393.05707 , 273.5855  ]], dtype=float32), labels=array([[8],\n",
+       "        [8],\n",
+       "        [8],\n",
+       "        [8],\n",
+       "        [8],\n",
+       "        [8],\n",
+       "        [8],\n",
+       "        [8],\n",
+       "        [8],\n",
+       "        [8],\n",
+       "        [8],\n",
+       "        [8],\n",
+       "        [8],\n",
+       "        [8],\n",
+       "        [8]]), scores=array([0.68943435, 0.665106  , 0.5542661 , 0.5507727 , 0.46040758,\n",
        "        0.45880103, 0.43566883, 0.40994436, 0.4001922 , 0.37548688,\n",
        "        0.30045223, 0.27943224, 0.26711544, 0.26193473, 0.2512777 ],\n",
-       "       dtype=float32), class_names=['boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat'], visualization=<PIL.Image.Image image mode=RGB size=1024x683 at 0x7C75294ECEF0>),\n",
+       "       dtype=float32), class_names=['boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat', 'boat'], visualization=<PIL.Image.Image image mode=RGB size=1024x683 at 0x7E0D7014DC90>),\n",
        " PredictionResult(boxes=array([[2.2390503e+02, 1.9052693e+02, 6.7956946e+02, 6.0262982e+02],\n",
        "        [6.7685510e+02, 5.1469067e+02, 7.5505920e+02, 5.6018738e+02],\n",
        "        [2.8964825e+02, 4.8724002e+02, 4.7840387e+02, 5.8926813e+02],\n",
        "        [3.1784857e+02, 4.9751697e+02, 4.7368231e+02, 5.7966547e+02],\n",
        "        [4.8936462e-01, 4.2978781e+02, 2.3865834e+02, 6.7926904e+02]],\n",
-       "       dtype=float32), labels=array([ 0, 41, 63, 63, 62]), scores=array([0.8863109 , 0.5313867 , 0.5248599 , 0.40107745, 0.3089608 ],\n",
-       "       dtype=float32), class_names=['person', 'cup', 'laptop', 'laptop', 'tv'], visualization=<PIL.Image.Image image mode=RGB size=1024x685 at 0x7C75294EC980>),\n",
+       "       dtype=float32), labels=array([[ 0],\n",
+       "        [41],\n",
+       "        [63],\n",
+       "        [63],\n",
+       "        [62]]), scores=array([0.8863109 , 0.5313867 , 0.5248599 , 0.40107745, 0.3089608 ],\n",
+       "       dtype=float32), class_names=['person', 'cup', 'laptop', 'laptop', 'tv'], visualization=<PIL.Image.Image image mode=RGB size=1024x685 at 0x7E0D711EA150>),\n",
        " PredictionResult(boxes=array([[ 4.2780542e+02,  1.5069971e+01,  9.7997266e+02,  6.7155322e+02],\n",
        "        [ 2.2182373e+01,  1.0445609e+02,  3.5739850e+02,  5.1991125e+02],\n",
        "        [-4.8427582e-02,  2.0301846e+02,  6.2602463e+01,  3.6837134e+02],\n",
        "        [ 4.2779562e+02,  1.5097492e+01,  9.7904858e+02,  6.7130902e+02],\n",
        "        [ 2.2337738e+01,  1.0480997e+02,  3.5725595e+02,  5.1993842e+02]],\n",
-       "       dtype=float32), labels=array([56, 56, 56, 57, 57]), scores=array([0.94877666, 0.9346415 , 0.77918357, 0.5413766 , 0.50754297],\n",
-       "       dtype=float32), class_names=['chair', 'chair', 'chair', 'couch', 'couch'], visualization=<PIL.Image.Image image mode=RGB size=1024x680 at 0x7C75294EC7A0>),\n",
+       "       dtype=float32), labels=array([[56],\n",
+       "        [56],\n",
+       "        [56],\n",
+       "        [57],\n",
+       "        [57]]), scores=array([0.94877666, 0.9346415 , 0.77918357, 0.5413766 , 0.50754297],\n",
+       "       dtype=float32), class_names=['chair', 'chair', 'chair', 'couch', 'couch'], visualization=<PIL.Image.Image image mode=RGB size=1024x680 at 0x7E0D70D522D0>),\n",
        " PredictionResult(boxes=array([[150.30363, 186.02875, 594.8532 , 760.8926 ],\n",
-       "        [185.82806, 177.42165, 323.4549 , 285.49905]], dtype=float32), labels=array([ 0, 35]), scores=array([0.92683053, 0.3182136 ], dtype=float32), class_names=['person', 'baseball glove'], visualization=<PIL.Image.Image image mode=RGB size=1024x768 at 0x7C75294EC650>),\n",
+       "        [185.82806, 177.42165, 323.4549 , 285.49905]], dtype=float32), labels=array([[ 0],\n",
+       "        [35]]), scores=array([0.92683053, 0.3182136 ], dtype=float32), class_names=['person', 'baseball glove'], visualization=<PIL.Image.Image image mode=RGB size=1024x768 at 0x7E0D70D506D0>),\n",
        " PredictionResult(boxes=array([[580.36194  ,   1.2676103, 967.0814   , 813.922    ],\n",
        "        [104.97034  , 269.29166  , 534.50073  , 815.042    ],\n",
        "        [104.97034  , 269.29166  , 534.50073  , 815.042    ],\n",
        "        [104.326294 , 269.39062  , 534.2277   , 815.8475   ]],\n",
-       "       dtype=float32), labels=array([39, 39, 41, 41]), scores=array([0.9395074 , 0.67832315, 0.44287342, 0.3188903 ], dtype=float32), class_names=['bottle', 'bottle', 'cup', 'cup'], visualization=<PIL.Image.Image image mode=RGB size=1024x819 at 0x7C75294EC410>),\n",
+       "       dtype=float32), labels=array([[39],\n",
+       "        [39],\n",
+       "        [41],\n",
+       "        [41]]), scores=array([0.9395074 , 0.67832315, 0.44287342, 0.3188903 ], dtype=float32), class_names=['bottle', 'bottle', 'cup', 'cup'], visualization=<PIL.Image.Image image mode=RGB size=1024x819 at 0x7E0D70D53050>),\n",
        " PredictionResult(boxes=array([[5.00248901e+02, 2.82743713e+02, 6.37413818e+02, 5.54798096e+02],\n",
        "        [1.98227798e+02, 4.69237701e+02, 3.86902954e+02, 6.81980713e+02],\n",
        "        [7.20214844e-03, 4.56817139e+02, 1.68124146e+02, 6.81365601e+02],\n",
@@ -646,9 +838,49 @@
        "        [2.03034241e+02, 1.60103806e+02, 5.30494080e+02, 4.07366211e+02],\n",
        "        [1.22524536e+02, 4.59525055e+02, 2.16819458e+02, 6.65633606e+02],\n",
        "        [3.09696503e+02, 5.18654175e+02, 4.57013275e+02, 6.46841553e+02]],\n",
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   md5: 6797b005cd0f439c4c5c9ac565783700
   constrains:
   - vs2015_runtime >=14.29.30037
-  arch: x86_64
-  platform: win
   license: LicenseRef-MicrosoftWindowsSDK10
   purls: []
   size: 559710
@@ -6233,8 +5975,6 @@ packages:
   md5: d3f0381e38093bde620a8d85f266ae55
   depends:
   - vc14_runtime >=14.42.34433
-  arch: x86_64
-  platform: win
   track_features:
   - vc14
   license: BSD-3-Clause
@@ -6249,8 +5989,6 @@ packages:
   - ucrt >=10.0.20348.0
   constrains:
   - vs2015_runtime 14.42.34438.* *_26
-  arch: x86_64
-  platform: win
   license: LicenseRef-MicrosoftVisualCpp2015-2022Runtime
   license_family: Proprietary
   purls: []
@@ -6318,8 +6056,6 @@ packages:
   - libgcc >=13
   - libsodium >=1.0.20,<1.0.21.0a0
   - libstdcxx >=13
-  arch: x86_64
-  platform: linux
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
@@ -6333,8 +6069,6 @@ packages:
   - krb5 >=1.21.3,<1.22.0a0
   - libcxx >=18
   - libsodium >=1.0.20,<1.0.21.0a0
-  arch: arm64
-  platform: osx
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
@@ -6349,8 +6083,6 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
@@ -6375,8 +6107,6 @@ packages:
   - libgcc >=13
   - libstdcxx >=13
   - libzlib >=1.3.1,<2.0a0
-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -6388,8 +6118,6 @@ packages:
   depends:
   - __osx >=11.0
   - libzlib >=1.3.1,<2.0a0
-  arch: arm64
-  platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -6403,8 +6131,6 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
diff --git a/pyproject.toml b/pyproject.toml
index c981b721..a15f3811 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,7 +11,6 @@ dependencies = [
     "calflops>=0.3.2,<0.4",
     "transformers>=4.49.0,<5",
     "opencv-python>=4.11.0.86,<5",
-    "gdown>=5.2.0,<6",
     "loguru>=0.7.3,<0.8",
     "matplotlib",
     "onnx>=1.17.0,<2",
@@ -66,6 +65,7 @@ default = { features = ["cpu"] }
 [tool.pixi.tasks]
 quickstart = "python scripts/quickstart.py"
 live-inference = "python scripts/live_inference.py"
+live-inference-pretrained = "python scripts/live_inference_pretrained.py"
 train-model = "python scripts/train.py"
 gradio-demo = "python scripts/gradio_demo.py"
 export = "python scripts/export.py"
diff --git a/scripts/live_inference_pretrained.py b/scripts/live_inference_pretrained.py
new file mode 100644
index 00000000..2406d213
--- /dev/null
+++ b/scripts/live_inference_pretrained.py
@@ -0,0 +1,617 @@
+import argparse
+import colorsys
+import time
+from pathlib import Path
+from typing import Optional, Tuple
+
+import cv2
+import numpy as np
+import onnxruntime as ort
+from loguru import logger
+
+from deimkit import list_models, load_model
+from deimkit.config import Config
+from deimkit.exporter import Exporter
+
+ort.preload_dlls()
+
+
+def generate_colors(num_classes: int) -> list[tuple[int, int, int]]:
+    """Generate distinct colors for visualization."""
+    hsv_tuples = [(x / num_classes, 0.8, 0.9) for x in range(num_classes)]
+    colors = []
+    for hsv in hsv_tuples:
+        rgb = colorsys.hsv_to_rgb(*hsv)
+        colors.append(tuple(int(255 * x) for x in rgb))
+    return colors
+
+
+def draw_boxes(
+    image: np.ndarray,
+    labels: np.ndarray,
+    boxes: np.ndarray,
+    scores: np.ndarray,
+    threshold: float = 0.3,
+    class_names: Optional[list[str]] = None,
+) -> np.ndarray:
+    """Draw bounding boxes on the image with detection results.
+
+    Args:
+        image: Input image array in BGR format
+        labels: Array of class labels
+        boxes: Array of bounding box coordinates [x1, y1, x2, y2]
+        scores: Array of confidence scores
+        threshold: Minimum confidence threshold for displaying detections
+        class_names: Optional list of class names for labels
+
+    Returns:
+        np.ndarray: Image with drawn bounding boxes in BGR format
+    """
+    # Generate colors for classes
+    num_classes = len(class_names) if class_names else 91
+    colors = generate_colors(num_classes)
+
+    # Filter detections by threshold
+    valid_indices = scores > threshold
+    labels = labels[valid_indices]
+    boxes = boxes[valid_indices]
+    scores = scores[valid_indices]
+
+    for j, (lbl, box, score) in enumerate(zip(labels, boxes, scores)):
+        # Get color for this class
+        class_idx = int(lbl)
+        color = colors[class_idx % len(colors)]
+
+        # Use box coordinates directly
+        box_coords = [
+            int(box[0]),  # x1
+            int(box[1]),  # y1
+            int(box[2]),  # x2
+            int(box[3]),  # y2
+        ]
+
+        # Draw rectangle
+        cv2.rectangle(
+            image,
+            (box_coords[0], box_coords[1]),
+            (box_coords[2], box_coords[3]),
+            color,
+            2,
+        )
+
+        # Prepare label text
+        if class_names and class_idx < len(class_names):
+            label_text = f"{class_names[class_idx]} {score:.2f}"
+        else:
+            label_text = f"Class {class_idx} {score:.2f}"
+
+        # Get text size
+        (text_width, text_height), _ = cv2.getTextSize(
+            label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2
+        )
+
+        # Draw text background
+        cv2.rectangle(
+            image,
+            (box_coords[0], box_coords[1] - text_height - 4),
+            (box_coords[0] + text_width + 4, box_coords[1]),
+            color,
+            -1,  # Filled rectangle
+        )
+
+        # Calculate text color based on background brightness
+        brightness = (color[0] * 299 + color[1] * 587 + color[2] * 114) / 1000
+        text_color = (0, 0, 0) if brightness > 128 else (255, 255, 255)
+
+        # Draw text
+        cv2.putText(
+            image,
+            label_text,
+            (box_coords[0] + 2, box_coords[1] - 2),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.7,
+            text_color,
+            2,
+        )
+
+    return image
+
+
+def draw_text_overlay(
+    image: np.ndarray,
+    fps: float,
+    provider: str,
+    video_width: int,
+    show_overlay: bool = True,
+) -> np.ndarray:
+    """Draw text overlays (FPS, width, provider) on the detection frame."""
+    if not show_overlay:
+        return image
+
+    # Add video width display at top left with dark green background
+    width_text = f"Width: {int(video_width)}px"
+    text_size = cv2.getTextSize(width_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
+
+    # Draw dark green background rectangle
+    cv2.rectangle(
+        image,
+        (5, 5),  # Slight padding from corner
+        (text_size[0] + 15, 35),  # Add padding around text
+        (0, 100, 0),  # Dark green in BGR
+        -1,  # Filled rectangle
+    )
+
+    # Draw width text
+    cv2.putText(
+        image,
+        width_text,
+        (10, 30),
+        cv2.FONT_HERSHEY_SIMPLEX,
+        0.8,
+        (255, 255, 255),  # White text
+        2,
+    )
+
+    # Add FPS display
+    fps_text = f"FPS: {fps:.1f}"
+    text_size = cv2.getTextSize(fps_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
+    text_x = image.shape[1] - text_size[0] - 10
+    text_y = 30
+
+    # Draw FPS background rectangle
+    cv2.rectangle(
+        image,
+        (text_x - 5, text_y - text_size[1] - 5),
+        (text_x + text_size[0] + 5, text_y + 5),
+        (139, 0, 0),
+        -1,
+    )
+
+    # Draw FPS text
+    cv2.putText(
+        image,
+        fps_text,
+        (text_x, text_y),
+        cv2.FONT_HERSHEY_SIMPLEX,
+        0.8,
+        (255, 255, 255),
+        2,
+    )
+
+    # Add provider display
+    provider_text = f"Provider: {provider}"
+    text_size = cv2.getTextSize(provider_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
+    text_x = (image.shape[1] - text_size[0]) // 2
+    text_y = image.shape[0] - 20
+
+    # Draw provider background rectangle
+    cv2.rectangle(
+        image,
+        (text_x - 5, text_y - text_size[1] - 5),
+        (text_x + text_size[0] + 5, text_y + 5),
+        (0, 0, 139),
+        -1,
+    )
+
+    # Draw provider text
+    cv2.putText(
+        image,
+        provider_text,
+        (text_x, text_y),
+        cv2.FONT_HERSHEY_SIMPLEX,
+        0.8,
+        (255, 255, 255),
+        2,
+    )
+
+    return image
+
+
+def load_onnx_model(model_path: str, provider: str = "cpu") -> ort.InferenceSession:
+    """Initialize and load the ONNX model with specified provider."""
+    if provider == "cpu":
+        providers = ["CPUExecutionProvider"]
+    elif provider == "cuda":
+        providers = [
+            (
+                "CUDAExecutionProvider",
+                {
+                    "arena_extend_strategy": "kNextPowerOfTwo",
+                    "gpu_mem_limit": 2 * 1024 * 1024 * 1024,
+                    "cudnn_conv_algo_search": "EXHAUSTIVE",
+                    "do_copy_in_default_stream": True,
+                },
+            ),
+            "CPUExecutionProvider",
+        ]
+    elif provider == "tensorrt":
+        providers = [
+            (
+                "TensorrtExecutionProvider",
+                {
+                    "trt_fp16_enable": False,
+                    "trt_engine_cache_enable": True,
+                    "trt_engine_cache_path": "./trt_cache",
+                    "trt_timing_cache_enable": True,
+                },
+            ),
+            "CPUExecutionProvider",
+        ]
+    else:
+        raise ValueError(f"Unsupported provider: {provider}")
+
+    try:
+        logger.info(f"Loading ONNX model with providers: {providers}...")
+        session = ort.InferenceSession(model_path, providers=providers)
+        logger.info(f"Using provider: {session.get_providers()[0]}")
+        return session
+    except Exception as e:
+        logger.warning(
+            f"Error creating inference session with providers {providers}: {e}"
+        )
+        logger.info("Attempting to fall back to CPU execution...")
+        session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
+        return session
+
+
+def process_frame(
+    frame: np.ndarray,
+    session: ort.InferenceSession,
+    class_names: list[str] | None,
+    threshold: float,
+    target_width: int,
+) -> np.ndarray:
+    """Process a single frame through the object detection model."""
+    # Calculate scaling and padding
+    height, width = frame.shape[:2]
+    scale = target_width / max(height, width)
+    new_height = int(height * scale)
+    new_width = int(width * scale)
+
+    # Calculate padding
+    y_offset = (target_width - new_height) // 2
+    x_offset = (target_width - new_width) // 2
+
+    # Create model input with padding
+    model_input = np.zeros((target_width, target_width, 3), dtype=np.uint8)
+    model_input[y_offset : y_offset + new_height, x_offset : x_offset + new_width] = (
+        cv2.resize(frame, (new_width, new_height))
+    )
+
+    # Prepare input data
+    im_data = np.ascontiguousarray(
+        model_input.transpose(2, 0, 1),
+        dtype=np.float32,
+    )
+    im_data = np.expand_dims(im_data, axis=0)
+    orig_size = np.array([[target_width, target_width]], dtype=np.int64)
+
+    # Run inference
+    input_name = session.get_inputs()[0].name
+    outputs = session.run(
+        output_names=None,
+        input_feed={input_name: im_data, "orig_target_sizes": orig_size},
+    )
+
+    # Process outputs
+    labels, boxes, scores = outputs
+
+    # Scale boxes back to original frame size
+    boxes = boxes[0]  # Remove batch dimension
+    boxes[:, [0, 2]] = (boxes[:, [0, 2]] - x_offset) / scale  # x coordinates
+    boxes[:, [1, 3]] = (boxes[:, [1, 3]] - y_offset) / scale  # y coordinates
+
+    # Draw detections
+    return draw_boxes(
+        frame,
+        labels[0],
+        boxes,
+        scores[0],
+        threshold=threshold,
+        class_names=class_names,
+    )
+
+
+def setup_model_and_export(
+    model_name: str, class_names: list[str], output_dir: str = "./checkpoints"
+) -> Tuple[str, str]:
+    """
+    Download pretrained model, convert to ONNX, and prepare for inference.
+
+    Args:
+        model_name: Name of the model to use (e.g. 'deim_hgnetv2_x')
+        class_names: List of class names for the model
+        output_dir: Directory to save model checkpoints and ONNX file
+
+    Returns:
+        Tuple containing:
+            - Path to the ONNX model file
+            - Path to the class names file
+    """
+    # Create output directory
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Load the model
+    logger.info(f"Loading model {model_name}...")
+    model = load_model(model_name, class_names=class_names)
+
+    # Save config
+    config_path = output_dir / f"{model_name}.yml"
+    model.cfg.save(str(config_path))
+
+    # Export to ONNX
+    logger.info("Converting model to ONNX format...")
+    config = Config(str(config_path))
+    exporter = Exporter(config)
+
+    onnx_path = output_dir / f"{model_name}.onnx"
+    exporter.to_onnx(
+        checkpoint_path=str(output_dir / f"{model_name}.pth"),
+        output_path=str(onnx_path),
+    )
+
+    # Save class names
+    class_names_path = output_dir / "coco_classes.txt"
+    with open(class_names_path, "w") as f:
+        for name in class_names:
+            f.write(f"{name}\n")
+
+    return str(onnx_path), str(class_names_path)
+
+
+def run_inference(
+    model_path: str,
+    input_source: str | int,
+    class_names_path: str | None = None,
+    threshold: float = 0.3,
+    provider: str = "cpu",
+    inference_size: int = 640,
+) -> None:
+    """Run object detection on images or video streams."""
+    # Load model and class names
+    session = load_onnx_model(model_path, provider)
+    class_names = None
+    if class_names_path:
+        try:
+            with open(class_names_path, "r") as f:
+                class_names = [line.strip() for line in f.readlines()]
+            logger.info(f"Loaded {len(class_names)} class names")
+        except Exception as e:
+            logger.warning(f"Error loading class names: {e}")
+
+    # Determine if input is image file or video source
+    if isinstance(input_source, str) and any(
+        input_source.lower().endswith(ext) for ext in [".jpg", ".jpeg", ".png", ".bmp"]
+    ):
+        # Handle single image
+        image = cv2.imread(input_source)
+        if image is None:
+            raise RuntimeError(f"Failed to load image: {input_source}")
+
+        result = process_frame(image, session, class_names, threshold, inference_size)
+
+        # Save and display result
+        output_path = "detection_result.jpg"
+        cv2.imwrite(output_path, result)
+        logger.info(f"Detection complete. Result saved to {output_path}")
+
+        cv2.imshow("Detection Result", result)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+
+    else:
+        # Handle video/webcam
+        cap = cv2.VideoCapture(input_source)
+        if not cap.isOpened():
+            raise RuntimeError(f"Failed to open video source: {input_source}")
+
+        # Configure video capture
+        if isinstance(input_source, int):  # Webcam settings
+            cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc("M", "J", "P", "G"))
+            cap.set(cv2.CAP_PROP_FPS, 100)
+            cap.set(cv2.CAP_PROP_FRAME_WIDTH, inference_size)
+            cap.set(cv2.CAP_PROP_FRAME_HEIGHT, int(inference_size * 9 / 16))
+
+        prev_time = time.time()
+        fps_display = 0
+        show_overlay = True
+
+        try:
+            while True:
+                ret, frame = cap.read()
+                if not ret:
+                    break
+
+                # Calculate FPS
+                current_time = time.time()
+                fps_display = (
+                    1 / (current_time - prev_time)
+                    if current_time - prev_time > 0
+                    else 0
+                )
+                prev_time = current_time
+
+                # Process frame
+                result = process_frame(
+                    frame, session, class_names, threshold, inference_size
+                )
+
+                # Add overlay
+                result = draw_text_overlay(
+                    result,
+                    fps_display,
+                    session.get_providers()[0],
+                    inference_size,
+                    show_overlay,
+                )
+
+                # Display result
+                cv2.imshow("Detection", result)
+
+                # Handle key presses
+                key = cv2.waitKey(1) & 0xFF
+                if key == ord("q"):
+                    break
+                elif key == ord("t"):
+                    show_overlay = not show_overlay
+
+        finally:
+            cap.release()
+            cv2.destroyAllWindows()
+
+
+def main():
+    # COCO class names
+    coco_classes = [
+        "person",
+        "bicycle",
+        "car",
+        "motorcycle",
+        "airplane",
+        "bus",
+        "train",
+        "truck",
+        "boat",
+        "traffic light",
+        "fire hydrant",
+        "stop sign",
+        "parking meter",
+        "bench",
+        "bird",
+        "cat",
+        "dog",
+        "horse",
+        "sheep",
+        "cow",
+        "elephant",
+        "bear",
+        "zebra",
+        "giraffe",
+        "backpack",
+        "umbrella",
+        "handbag",
+        "tie",
+        "suitcase",
+        "frisbee",
+        "skis",
+        "snowboard",
+        "sports ball",
+        "kite",
+        "baseball bat",
+        "baseball glove",
+        "skateboard",
+        "surfboard",
+        "tennis racket",
+        "bottle",
+        "wine glass",
+        "cup",
+        "fork",
+        "knife",
+        "spoon",
+        "bowl",
+        "banana",
+        "apple",
+        "sandwich",
+        "orange",
+        "broccoli",
+        "carrot",
+        "hot dog",
+        "pizza",
+        "donut",
+        "cake",
+        "chair",
+        "couch",
+        "potted plant",
+        "bed",
+        "dining table",
+        "toilet",
+        "tv",
+        "laptop",
+        "mouse",
+        "remote",
+        "keyboard",
+        "cell phone",
+        "microwave",
+        "oven",
+        "toaster",
+        "sink",
+        "refrigerator",
+        "book",
+        "clock",
+        "vase",
+        "scissors",
+        "teddy bear",
+        "hair drier",
+        "toothbrush",
+    ]
+
+    parser = argparse.ArgumentParser(
+        description="Download, convert and run DEIM object detection"
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="deim_hgnetv2_s",
+        choices=list_models(),
+        help="Model architecture to use",
+    )
+    parser.add_argument("--image", type=str, help="Path to input image (optional)")
+    parser.add_argument("--webcam", action="store_true", help="Use webcam input")
+    parser.add_argument("--video", type=str, help="Path to input video file (optional)")
+    parser.add_argument(
+        "--inference-size",
+        type=int,
+        default=640,
+        help="Size for inference processing (default: 640)",
+    )
+    parser.add_argument(
+        "--provider",
+        type=str,
+        choices=["cpu", "cuda", "tensorrt"],
+        default="cuda", # If cuda is not available, OnnxRuntime will fall back to CPU
+        help="ONNXRuntime provider to use for inference",
+    )
+    parser.add_argument(
+        "--threshold",
+        type=float,
+        default=0.35,
+        help="Detection confidence threshold (default: 0.35)",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        default="./checkpoints",
+        help="Directory to save model files",
+    )
+
+    args = parser.parse_args()
+
+    # Setup model and export to ONNX
+    onnx_path, class_names_path = setup_model_and_export(
+        args.model, coco_classes, args.output_dir
+    )
+
+    # Determine input source
+    if args.webcam:
+        input_source = 0
+    elif args.video:
+        input_source = args.video
+    elif args.image:
+        input_source = args.image
+    else:
+        parser.error("Either --image, --video, or --webcam must be specified")
+
+    # Run inference
+    run_inference(
+        model_path=onnx_path,
+        input_source=input_source,
+        class_names_path=class_names_path,
+        threshold=args.threshold,
+        provider=args.provider,
+        inference_size=args.inference_size,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/deimkit/model.py b/src/deimkit/model.py
index 20f2455e..f929c313 100644
--- a/src/deimkit/model.py
+++ b/src/deimkit/model.py
@@ -1,4 +1,6 @@
 from typing import TYPE_CHECKING, Any, Dict, Optional
+import os
+from pathlib import Path
 
 from loguru import logger
 
@@ -111,3 +113,76 @@ def update_setting(
         config.update(**updates)  
 
     return config  
+
+MODEL_CHECKPOINT_URLS = {
+    "deim_hgnetv2_n": "https://github.com/dnth/DEIMKit/releases/download/v0.1.0/deim_dfine_hgnetv2_n_coco_160e.pth",  # Nano model
+    "deim_hgnetv2_s": "https://github.com/dnth/DEIMKit/releases/download/v0.1.0/deim_dfine_hgnetv2_s_coco_120e.pth",  # Small model
+    "deim_hgnetv2_m": "https://github.com/dnth/DEIMKit/releases/download/v0.1.0/deim_dfine_hgnetv2_m_coco_90e.pth",  # Medium model
+    "deim_hgnetv2_l": "https://github.com/dnth/DEIMKit/releases/download/v0.1.0/deim_dfine_hgnetv2_l_coco_50e.pth",  # Large model
+    "deim_hgnetv2_x": "https://github.com/dnth/DEIMKit/releases/download/v0.1.0/deim_dfine_hgnetv2_x_coco_50e.pth",  # XLarge model
+}
+
+DEFAULT_CACHE_DIR = os.path.expanduser("./checkpoints")
+
+def get_model_checkpoint(model_name: str, custom_checkpoint: Optional[str] = None) -> str:
+    """Get the path to a model checkpoint, downloading it if necessary.
+    
+    Args:
+        model_name: Name of the model to get checkpoint for
+        custom_checkpoint: Optional path to a custom checkpoint file
+        
+    Returns:
+        Path to the checkpoint file
+    
+    Raises:
+        ValueError: If model_name is invalid or checkpoint cannot be found/downloaded
+    """
+    if model_name not in MODEL_CHECKPOINT_URLS:
+        raise ValueError(f"Invalid model_name: {model_name}. Must be one of {list(MODEL_CHECKPOINT_URLS.keys())}")
+        
+    if custom_checkpoint is not None:
+        if not os.path.exists(custom_checkpoint):
+            raise ValueError(f"Custom checkpoint not found: {custom_checkpoint}")
+        logger.info(f"Using custom checkpoint: {custom_checkpoint}")
+        return custom_checkpoint
+
+    return _download_checkpoint(model_name, MODEL_CHECKPOINT_URLS[model_name])
+
+def _download_checkpoint(model_name: str, url: str) -> str:
+    """Download checkpoint from URL if not already present"""
+    import requests
+    from tqdm import tqdm
+
+    # Create cache directory if it doesn't exist
+    cache_dir = Path(DEFAULT_CACHE_DIR)
+    cache_dir.mkdir(parents=True, exist_ok=True)
+
+    # Construct local path
+    local_path = cache_dir / f"{model_name}.pth"
+
+    # Download if not already present
+    if not local_path.exists():
+        logger.info(f"Downloading checkpoint for model {model_name}...")
+        response = requests.get(url, stream=True)
+        response.raise_for_status()
+        
+        # Get file size for progress bar
+        total_size = int(response.headers.get('content-length', 0))
+        
+        # Download with progress bar
+        with open(local_path, 'wb') as f, tqdm(
+            desc=f"{model_name}",
+            total=total_size,
+            unit='iB',
+            unit_scale=True,
+            unit_divisor=1024,
+        ) as pbar:
+            for data in response.iter_content(chunk_size=1024):
+                size = f.write(data)
+                pbar.update(size)
+            
+        logger.success(f"Downloaded checkpoint to {local_path}")
+    else:
+        logger.info(f"Using cached checkpoint from {local_path}")
+
+    return str(local_path)  
diff --git a/src/deimkit/predictor.py b/src/deimkit/predictor.py
index 95b5549a..12d7e54d 100644
--- a/src/deimkit/predictor.py
+++ b/src/deimkit/predictor.py
@@ -15,15 +15,6 @@
 from .config import Config
 from .visualization import draw_on_image
 
-MODEL_CHECKPOINT_URLS = {
-    "deim_hgnetv2_n": "1ZPEhiU9nhW4M5jLnYOFwTSLQC1Ugf62e",  # Nano model
-    "deim_hgnetv2_s": "1tB8gVJNrfb6dhFvoHJECKOF5VpkthhfC",  # Small model
-    "deim_hgnetv2_m": "18Lj2a6UN6k_n_UzqnJyiaiLGpDzQQit8",  # Medium model
-    "deim_hgnetv2_l": "1PIRf02XkrA2xAD3wEiKE2FaamZgSGTAr",  # Large model
-    "deim_hgnetv2_x": "1dPtbgtGgq1Oa7k_LgH1GXPelg1IVeu0j",  # XLarge model
-}
-
-DEFAULT_CACHE_DIR = os.path.expanduser("./checkpoints")
 DEFAULT_IMAGE_SIZE = (640, 640)
 DEFAULT_MEAN = [0.485, 0.456, 0.406]
 DEFAULT_STD = [0.229, 0.224, 0.225]
@@ -156,10 +147,8 @@ def __init__(
 
     def _validate_model_name(self, model_name: str) -> None:
         """Validate that the model name is supported"""
-        if model_name not in MODEL_CHECKPOINT_URLS:
-            raise ValueError(
-                f"Invalid model_name: {model_name}. Must be one of {list(MODEL_CHECKPOINT_URLS.keys())}"
-            )
+        # This method is now empty as the model names are validated in the model.py
+        pass
 
     def _setup_device(self, device: str) -> str:
         """Set up and validate the device for inference"""
@@ -172,36 +161,8 @@ def _get_checkpoint_path(
         self, model_name: str, custom_checkpoint: Optional[str]
     ) -> str:
         """Get the path to the checkpoint file"""
-        if custom_checkpoint is not None:
-            if not os.path.exists(custom_checkpoint):
-                raise ValueError(f"Custom checkpoint not found: {custom_checkpoint}")
-            logger.info(f"Using custom checkpoint: {custom_checkpoint}")
-            return custom_checkpoint
-
-        # Download or use cached checkpoint
-        return self._download_checkpoint(model_name, MODEL_CHECKPOINT_URLS[model_name])
-
-    def _download_checkpoint(self, model_name: str, file_id: str) -> str:
-        """Download checkpoint from Google Drive if not already present"""
-        import gdown
-
-        # Create cache directory if it doesn't exist
-        cache_dir = Path(DEFAULT_CACHE_DIR)
-        cache_dir.mkdir(parents=True, exist_ok=True)
-
-        # Construct local path
-        local_path = cache_dir / f"{model_name}.pth"
-
-        # Download if not already present
-        if not local_path.exists():
-            logger.info(f"Downloading checkpoint for model {model_name}...")
-            url = f"https://drive.google.com/uc?id={file_id}"
-            gdown.download(url, str(local_path), quiet=False)
-            logger.success(f"Downloaded checkpoint to {local_path}")
-        else:
-            logger.info(f"Using cached checkpoint from {local_path}")
-
-        return str(local_path)
+        from .model import get_model_checkpoint
+        return get_model_checkpoint(model_name, custom_checkpoint)
 
     def _load_checkpoint_state(self, checkpoint_path: str) -> Tuple[Dict, int]:
         """Load checkpoint and extract state dict and number of classes"""