diff --git "a/Final_Inference.ipynb" "b/Final_Inference.ipynb"
--- "a/Final_Inference.ipynb"
+++ "b/Final_Inference.ipynb"
@@ -2,13 +2,13 @@
   "cells": [
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 1,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "bSu3U-SNu8v6",
-        "outputId": "494f98f4-d490-4010-8e79-9ba10fb7a06b"
+        "outputId": "b5c5357f-9389-4210-ee84-7d97e6fca753"
       },
       "outputs": [
         {
@@ -223,7 +223,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": 2,
       "metadata": {
         "id": "ENyfncieqs6i"
       },
@@ -274,18 +274,18 @@
       "metadata": {
         "id": "sjIQLKZAj2fz"
       },
-      "execution_count": 17,
+      "execution_count": 3,
       "outputs": []
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "jtZ65UWcisN1",
-        "outputId": "7ce01e4d-e6f3-4d67-e9c1-54695927f2f2"
+        "outputId": "bf6e9791-300e-4975-f87b-42ad8caaf317"
       },
       "outputs": [
         {
@@ -293,7 +293,7 @@
           "name": "stdout",
           "text": [
             "Device: cuda\n",
-            "Training Start Time (UNIX): 1767566954\n"
+            "Training Start Time (UNIX): 1767655163\n"
           ]
         }
       ],
@@ -347,7 +347,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
+      "execution_count": 5,
       "metadata": {
         "id": "cm-y1pPnOGkK"
       },
@@ -455,7 +455,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "execution_count": 6,
       "metadata": {
         "id": "FSFBSh2BisN2"
       },
@@ -587,7 +587,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 21,
+      "execution_count": 7,
       "metadata": {
         "id": "Ppmdr9qxisN3"
       },
@@ -661,7 +661,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
+      "execution_count": 8,
       "metadata": {
         "id": "MiihAqg9isN3"
       },
@@ -744,6 +744,72 @@
         "        return avg_angle"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Adhoc Code"
+      ],
+      "metadata": {
+        "id": "aysexnGW37EM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import psutil\n",
+        "import os\n",
+        "import gc\n",
+        "import ctypes\n",
+        "import platform\n",
+        "\n",
+        "class Janitor:\n",
+        "    @staticmethod\n",
+        "    def __clean_system_memory():\n",
+        "        \"\"\"\n",
+        "        Forces the system to release unreferenced memory back to the OS.\n",
+        "        Crucial for staying under the 12GB RAM limit in Colab.\n",
+        "        \"\"\"\n",
+        "        # 1. Force Python's Garbage Collector\n",
+        "        # This deletes Python objects that are no longer referenced\n",
+        "        gc.collect()\n",
+        "\n",
+        "        # 2. Force Libc to release memory back to the OS (Linux/Colab only)\n",
+        "        # Python's allocator usually keeps freed memory for future use.\n",
+        "        # malloc_trim(0) forces it to give that memory back to the system immediately.\n",
+        "        if platform.system() == \"Linux\":\n",
+        "            try:\n",
+        "                libc = ctypes.CDLL(\"libc.so.6\")\n",
+        "                libc.malloc_trim(0)\n",
+        "            except (OSError, AttributeError):\n",
+        "                pass # Ignore if we can't find libc (e.g., on Windows/Mac)\n",
+        "\n",
+        "        # Optional: Verification\n",
+        "        # You can verify the drop in RAM using psutil if you wish\n",
+        "        # import psutil\n",
+        "        # process = psutil.Process(os.getpid())\n",
+        "        # print(f\"System RAM used: {process.memory_info().rss / 1e9:.2f} GB\")\n",
+        "\n",
+        "    @staticmethod\n",
+        "    def smart_cleanup(threshold_gb=8):\n",
+        "        \"\"\"\n",
+        "        Only releases memory if usage exceeds threshold_gb.\n",
+        "        Prevents unnecessary latency overhead.\n",
+        "        \"\"\"\n",
+        "        process = psutil.Process(os.getpid())\n",
+        "        current_usage = process.memory_info().rss / (1024 ** 3) # GB\n",
+        "\n",
+        "        if current_usage > threshold_gb:\n",
+        "            # Calls the deep clean function provided earlier\n",
+        "            Janitor.__clean_system_memory()\n",
+        "            # Optional: Print for your report analysis\n",
+        "            # print(f\"Memory cleaned. Was at {current_usage:.2f} GB\")"
+      ],
+      "metadata": {
+        "id": "cH7-Ild239hO"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -770,14 +836,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": 10,
       "metadata": {
         "id": "XBukVn9qrnFZ",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
         },
-        "outputId": "e00098db-5f0a-4e92-b698-62930b543f2c"
+        "outputId": "3005a19c-5923-4064-a56d-5647d0c80d50"
       },
       "outputs": [
         {
@@ -792,7 +858,7 @@
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "100%|██████████| 77/77 [00:00<00:00, 73068.19it/s]"
+            "100%|██████████| 77/77 [00:00<00:00, 23414.88it/s]"
           ]
         },
         {
@@ -898,7 +964,7 @@
             }
           },
           "metadata": {},
-          "execution_count": 23
+          "execution_count": 10
         }
       ],
       "source": [
@@ -965,42 +1031,26 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": 11,
       "metadata": {
         "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 105,
-          "referenced_widgets": [
-            "96b1904645b74a9186787e146019e4da",
-            "79e68ccbe7e14868bc1b621d9c2c3bcb",
-            "b0dd851eb3cd48c29f2eaf0eab7c3aa8",
-            "950ea68d2f8e492890a4e901ff38293d",
-            "29f2223b7d2b40a8a07ff058d0b40b58",
-            "05bec595f2db4ec8b9481ad7b4623b58",
-            "bfd5b83934a74596a02cb18c7ec46451",
-            "61bf2f24595b4619852d5f2286b1f67e",
-            "cf1a3dc687e8454a89ffd73519ab5f5d",
-            "a0ce61edaee2404ba6c19818f7956e7b",
-            "bc18c6adf87e487ab48345ce2a89fe9f"
-          ]
+          "base_uri": "https://localhost:8080/"
         },
         "id": "qJsxm9qTisN4",
-        "outputId": "c729b60a-4b60-472d-8d01-30c0d24fb26b"
+        "outputId": "0b06e2ac-c4f9-471b-cfc6-e1b955f005e0"
       },
       "outputs": [
         {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "./yolo-weights/yolov8s-pose.pt:   0%|          | 0.00/23.5M [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "96b1904645b74a9186787e146019e4da"
-            }
-          },
-          "metadata": {}
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
         }
       ],
       "source": [
@@ -1043,13 +1093,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "ffd1td1ZisN4",
-        "outputId": "52432aa2-d823-4cda-bfce-fa44d3699a8b"
+        "outputId": "90bf3b96-2992-4f8c-9ce5-0bd895f2b23a"
       },
       "outputs": [
         {
@@ -1395,43 +1445,15 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": 13,
       "metadata": {
         "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 95,
-          "referenced_widgets": [
-            "0ff1e7ba711c49328b27f605a7db8976",
-            "cbb9bdc774454aa595fb566a0fb5eaf6",
-            "8107c233e5254eac8b6b2be9d4430682",
-            "35445b91cb07407aa2146a5f4b45ab75",
-            "1241c4cfb97c4a93a7ca506f8b293dd6",
-            "16a558aee1094a40a3a2a0c979aa42d7",
-            "a2cb6489aa3c4dcebbfa0296f5b11da6",
-            "424d904e40274016a4b75dad1d0a6c37",
-            "01b7c22856774189bd94ecbca7a4b9cd",
-            "72ac9afc25814e79927704caa9c8ef12",
-            "aa0aa9d015ee414d81fe78b1b3c75b75"
-          ]
+          "base_uri": "https://localhost:8080/"
         },
         "id": "LWuMOqY_sOdg",
-        "outputId": "a9c2a3fe-230c-46a5-b5c2-d29acd4e8a94"
+        "outputId": "86e6afe4-77a4-46fa-d6ef-26d056f5e570"
       },
       "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "model-weights/final_model_weights_Pushup(…):   0%|          | 0.00/530k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "0ff1e7ba711c49328b27f605a7db8976"
-            }
-          },
-          "metadata": {}
-        },
         {
           "output_type": "stream",
           "name": "stdout",
@@ -1475,13 +1497,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 27,
+      "execution_count": 14,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "QzgdieGiw4_k",
-        "outputId": "c8b483e4-6259-4ef4-fea6-0a45df2c4c82"
+        "outputId": "140e4e3b-1831-4c44-8981-141e248e10ee"
       },
       "outputs": [
         {
@@ -1493,97 +1515,97 @@
             "Running inference on 77 test videos...\n",
             "\n",
             "\n",
-            "✓  pred=4  true=4  |      6.2ms  |  4_kling_20251209_Text_to_Video_Generate_a_588_2.mp4\n",
-            "✓  pred=3  true=3  |      5.0ms  |  3_kling_20251206_Text_to_Video_Generate_a_315_2.mp4\n",
-            "✓  pred=6  true=6  |      4.4ms  |  6_dfjewaijsldkjfsaef.mp4\n",
-            "✓  pred=4  true=4  |      5.4ms  |  4_kling_20251209_Text_to_Video_Generate_a_561_1.mp4\n",
-            "✗  pred=2  true=1  |      3.5ms  |  1_dksksjfwijf.mp4\n",
-            "✓  pred=3  true=3  |      5.4ms  |  3_kling_20251209_Image_to_Video_Generate_a_635_0.mp4\n",
-            "✓  pred=3  true=3  |      7.1ms  |  3_sdlkfjaleknaksej.mp4\n",
-            "✓  pred=2  true=2  |      3.4ms  |  2_difficult_sdafkljsalkfj.mp4\n",
-            "✓  pred=4  true=4  |      4.9ms  |  4_sadlfkjlknewkjejk.mp4\n",
-            "✓  pred=3  true=3  |      4.8ms  |  3_kling_kdjflaskdjf.mp4\n",
-            "✓  pred=3  true=3  |      3.3ms  |  3_kling_20251206_Text_to_Video_Generate_a_71_3.mp4\n",
-            "✗  pred=2  true=3  |      5.3ms  |  3_kling_dskfseu.mp4\n",
-            "✓  pred=3  true=3  |      3.4ms  |  3_sadlfkjawelnflksdjf.mp4\n",
-            "✗  pred=2  true=3  |      6.2ms  |  3_kling_20251205_Text_to_Video_On_a_playg_5028_0.mp4\n",
-            "✓  pred=3  true=3  |      5.3ms  |  3_kling_20251209_Text_to_Video_Generate_a_491_1.mp4\n",
-            "✓  pred=4  true=4  |      4.8ms  |  4_asdlkfjalsflnekj.mp4\n",
-            "✓  pred=4  true=4  |      5.6ms  |  4_kling_20251209_Text_to_Video_Generate_a_377_1.mp4\n",
-            "✓  pred=3  true=3  |      5.9ms  |  3_ewdfkjwaeoihjlkasdjf.mp4\n",
-            "✗  pred=3  true=4  |      5.6ms  |  4_kling_20251209_Text_to_Video_Generate_a_452_1.mp4\n",
-            "✓  pred=6  true=6  |      5.3ms  |  6_kling_20251209_Text_to_Video_Generate_a_218_1.mp4\n",
-            "✗  pred=1  true=2  |      4.8ms  |  2_dkdjwkndkfw.mp4\n",
-            "✗  pred=4  true=3  |      4.7ms  |  3_sdlkjslndflkseijlkjef.mp4\n",
-            "✓  pred=2  true=2  |      4.2ms  |  2_sdkjdsflkjfwa.mp4\n",
-            "✓  pred=4  true=4  |      4.8ms  |  4_aslkjasmcalkewjlkje.mp4\n",
-            "✓  pred=4  true=4  |      5.6ms  |  4_kling_20251206_Text_to_Video_Generate_a_28_0.mp4\n",
-            "✓  pred=3  true=3  |      5.0ms  |  3_sdlkjfaslkjfalskjdf.mp4\n",
-            "✓  pred=3  true=3  |      5.6ms  |  3_kling_20251209_Text_to_Video_Generate_a_491_2.mp4\n",
-            "✓  pred=3  true=3  |      8.3ms  |  3_kling_20251206_Text_to_Video_Generate_a_712_3.mp4\n",
-            "✓  pred=4  true=4  |      5.4ms  |  4_20251209_Text_to_Video_Generate_a_561_0.mp4\n",
-            "✓  pred=3  true=3  |      3.4ms  |  3_kling_20251206_Text_to_Video_Generate_a_71_2.mp4\n",
-            "✗  pred=3  true=4  |      5.3ms  |  4_kling_20251209_Image_to_Video_Generate_a_635_1.mp4\n",
-            "✓  pred=4  true=4  |      6.7ms  |  4_pushup_1f2da596-7619-4d55-9376-069e15a42a1a_h264.mp4\n",
-            "✓  pred=4  true=4  |      5.4ms  |  4_kling_20251209_Text_to_Video_Generate_a_263_1.mp4\n",
-            "✓  pred=3  true=3  |      7.0ms  |  3_dslkaldskjflakjs.mp4\n",
-            "✓  pred=3  true=3  |      4.0ms  |  3_sdfjwaiejflkasjdf.mp4\n",
-            "✓  pred=4  true=4  |      5.3ms  |  4_kling_20251209_Text_to_Video_Generate_a_452_0.mp4\n",
-            "✓  pred=2  true=2  |      3.4ms  |  2_dfsaeklnvvalkej.mp4\n",
-            "✓  pred=4  true=4  |      5.0ms  |  4_aslkcasckmwlejk.mp4\n",
-            "✗  pred=3  true=2  |      3.7ms  |  2_difficult_2.mp4\n",
-            "✓  pred=3  true=3  |      7.2ms  |  3_sdlkfjalkjejafe.mp4\n",
-            "✓  pred=2  true=2  |      2.8ms  |  2_sdafkjaslkclaksdjkas.mp4\n",
-            "✓  pred=3  true=3  |      5.3ms  |  3_kling_20251209_Text_to_Video_Generate_a_491_0.mp4\n",
-            "✗  pred=3  true=2  |      3.4ms  |  2_kling_20251205_Text_to_Video_On_a_sandy_4976_0.mp4\n",
-            "✓  pred=2  true=2  |      6.1ms  |  2_sdlkjsaelijfksdjf.mp4\n",
-            "✓  pred=3  true=3  |      4.5ms  |  3_sdflkjliejkjdf.mp4\n",
-            "✓  pred=3  true=3  |      5.3ms  |  3_kling_20251209_Image_to_Video_Generate_a_613_1.mp4\n",
-            "✓  pred=3  true=3  |      3.4ms  |  3_kling_20251206_Text_to_Video_Generate_a_71_0.mp4\n",
-            "✓  pred=3  true=3  |      4.2ms  |  3_dsksdfjbvsdkj.mp4\n",
-            "✓  pred=3  true=3  |      4.7ms  |  3_sadklfjasbnlkjlfkj.mp4\n",
-            "✗  pred=3  true=2  |      4.0ms  |  2_sdfkjsaleijflaskdjf.mp4\n",
-            "✓  pred=3  true=3  |      5.0ms  |  3_asldkfjalwieaskdfaskdf.mp4\n",
-            "✓  pred=2  true=2  |      3.8ms  |  2_dkdmkejkeimdh.mp4\n",
-            "✓  pred=2  true=2  |      8.5ms  |  2_sdlfjlewlkjkj.mp4\n",
-            "✓  pred=2  true=2  |      5.9ms  |  2_dsalkfjalwkenlke.mp4\n",
-            "✗  pred=3  true=2  |      5.7ms  |  2_dkjd823kjf.mp4\n",
-            "✓  pred=3  true=3  |      6.3ms  |  3_kling_20251209_Text_to_Video_Generate_a_190_1.mp4\n",
-            "✓  pred=2  true=2  |      4.4ms  |  2_sdjfhafsldkjhjk.mp4\n",
-            "✓  pred=3  true=3  |      4.6ms  |  3_sadlfkjasldkfjasleijlkjfd.mp4\n",
-            "✓  pred=4  true=4  |      5.5ms  |  4_kling_20251209_Text_to_Video_Generate_a_218_0.mp4\n",
-            "✓  pred=4  true=4  |      5.4ms  |  4_kling_20251206_Text_to_Video_Generate_a_315_3.mp4\n",
-            "✓  pred=3  true=3  |      3.8ms  |  3_dsjlaeijlksjdfie.mp4\n",
-            "✓  pred=3  true=3  |     11.3ms  |  3_dkk873lkjlksajdf.mp4\n",
-            "✗  pred=3  true=2  |      3.0ms  |  2_kling_20251206_Text_to_Video_Generate_a_71_1.mp4\n",
-            "✗  pred=3  true=4  |      3.2ms  |  4_dssalsdkfjweijf.mp4\n",
-            "✓  pred=5  true=5  |      4.5ms  |  5_sdfkljweoijlkjdsflkjweaij.mp4\n",
-            "✗  pred=3  true=2  |      4.1ms  |  2_sadfasjldkfjaseifj.mp4\n",
-            "✓  pred=4  true=4  |      5.4ms  |  4_kling_20251209_Text_to_Video_Generate_a_190_0.mp4\n",
-            "✓  pred=3  true=3  |      8.3ms  |  3_kling_20251206_Text_to_Video_Generate_a_315_0.mp4\n",
-            "✓  pred=3  true=3  |      4.9ms  |  3_kling_20251206_Text_to_Video_Generate_a_17_0.mp4\n",
-            "✓  pred=5  true=5  |      4.4ms  |  5_sadfjhaslfkjasdlkfjsa.mp4\n",
-            "✓  pred=3  true=3  |      5.4ms  |  3_kling_20251209_Text_to_Video_Generate_a_403_1.mp4\n",
-            "✗  pred=4  true=3  |      4.5ms  |  3_kling_20251205_Text_to_Video_On_a_playg_5064_0.mp4\n",
-            "✗  pred=5  true=4  |      3.8ms  |  4_sadflkjasldkjfalseij.mp4\n",
-            "✓  pred=3  true=3  |      3.5ms  |  3_kling_20251205_Text_to_Video_In_a_grass_4697_0.mp4\n",
-            "✓  pred=4  true=4  |      5.3ms  |  4_kling_20251207_Text_to_Video_Generate_a_521_1.mp4\n",
-            "✗  pred=6  true=7  |      4.6ms  |  7_sadkjfkljekj.mp4\n",
-            "✓  pred=4  true=4  |      5.3ms  |  4_kling_20251206_Text_to_Video_Generate_a_58_0.mp4\n",
+            "✓  pred=4  true=4  |      8.8ms  |  4_pushup_1f2da596-7619-4d55-9376-069e15a42a1a_h264.mp4\n",
+            "✓  pred=3  true=3  |      5.3ms  |  3_sadlfkjasldkfjasleijlkjfd.mp4\n",
+            "✓  pred=3  true=3  |      4.9ms  |  3_sadklfjasbnlkjlfkj.mp4\n",
+            "✓  pred=2  true=2  |      4.5ms  |  2_kling_20251206_Text_to_Video_Generate_a_71_1.mp4\n",
+            "✓  pred=3  true=3  |     10.3ms  |  3_kling_20251209_Text_to_Video_Generate_a_491_2.mp4\n",
+            "✗  pred=4  true=3  |      5.2ms  |  3_ewdfkjwaeoihjlkasdjf.mp4\n",
+            "✓  pred=2  true=2  |      4.7ms  |  2_sdjfhafsldkjhjk.mp4\n",
+            "✗  pred=3  true=2  |      3.7ms  |  2_kling_20251205_Text_to_Video_On_a_sandy_4976_0.mp4\n",
+            "✓  pred=4  true=4  |      6.2ms  |  4_kling_20251209_Text_to_Video_Generate_a_561_1.mp4\n",
+            "✓  pred=3  true=3  |      5.7ms  |  3_sdfjwaiejflkasjdf.mp4\n",
+            "✓  pred=4  true=4  |      6.0ms  |  4_kling_20251209_Text_to_Video_Generate_a_377_1.mp4\n",
+            "✓  pred=2  true=2  |      5.0ms  |  2_sdlfjlewlkjkj.mp4\n",
+            "✓  pred=2  true=2  |      3.6ms  |  2_difficult_sdafkljsalkfj.mp4\n",
+            "✗  pred=4  true=3  |      4.9ms  |  3_kling_20251205_Text_to_Video_On_a_playg_5064_0.mp4\n",
+            "✗  pred=1  true=2  |      5.2ms  |  2_dkdjwkndkfw.mp4\n",
+            "✗  pred=3  true=4  |      5.9ms  |  4_kling_20251209_Text_to_Video_Generate_a_452_1.mp4\n",
+            "✓  pred=3  true=3  |      4.3ms  |  3_dsjlaeijlksjdfie.mp4\n",
+            "✓  pred=3  true=3  |      5.9ms  |  3_kling_20251209_Text_to_Video_Generate_a_190_1.mp4\n",
+            "✓  pred=3  true=3  |      5.0ms  |  3_sdflkjliejkjdf.mp4\n",
+            "✓  pred=3  true=3  |      5.5ms  |  3_sdlkjfaslkjfalskjdf.mp4\n",
+            "✓  pred=3  true=3  |      5.3ms  |  3_sdlkfjalkjejafe.mp4\n",
+            "✓  pred=4  true=4  |      5.1ms  |  4_sadlfkjlknewkjejk.mp4\n",
+            "✓  pred=2  true=2  |      5.0ms  |  2_sdlkjsaelijfksdjf.mp4\n",
+            "✓  pred=3  true=3  |      5.2ms  |  3_dslkaldskjflakjs.mp4\n",
+            "✗  pred=2  true=3  |      5.5ms  |  3_kling_dskfseu.mp4\n",
+            "✓  pred=6  true=6  |      5.9ms  |  6_kling_20251209_Text_to_Video_Generate_a_218_1.mp4\n",
+            "✗  pred=3  true=2  |      4.5ms  |  2_sdfkjsaleijflaskdjf.mp4\n",
+            "✓  pred=3  true=3  |      7.6ms  |  3_kling_20251209_Text_to_Video_Generate_a_491_1.mp4\n",
+            "✗  pred=3  true=2  |      4.3ms  |  2_sadfasjldkfjaseifj.mp4\n",
+            "✗  pred=6  true=7  |      5.2ms  |  7_sadkjfkljekj.mp4\n",
+            "✗  pred=2  true=3  |      5.4ms  |  3_kling_20251205_Text_to_Video_On_a_playg_5028_0.mp4\n",
+            "✓  pred=4  true=4  |      5.1ms  |  4_aslkjasmcalkewjlkje.mp4\n",
+            "✗  pred=3  true=2  |      4.1ms  |  2_difficult_2.mp4\n",
+            "✓  pred=3  true=3  |      5.4ms  |  3_kling_20251206_Text_to_Video_Generate_a_315_2.mp4\n",
+            "✓  pred=2  true=2  |      4.6ms  |  2_sdkjdsflkjfwa.mp4\n",
+            "✓  pred=2  true=2  |      3.5ms  |  2_dfsaeklnvvalkej.mp4\n",
+            "✗  pred=3  true=4  |      3.5ms  |  4_dssalsdkfjweijf.mp4\n",
+            "✓  pred=4  true=4  |      6.0ms  |  4_kling_20251209_Text_to_Video_Generate_a_588_2.mp4\n",
+            "✓  pred=3  true=3  |      5.4ms  |  3_dkk873lkjlksajdf.mp4\n",
+            "✓  pred=2  true=2  |      3.2ms  |  2_sdafkjaslkclaksdjkas.mp4\n",
+            "✓  pred=4  true=4  |      5.9ms  |  4_kling_20251206_Text_to_Video_Generate_a_58_0.mp4\n",
+            "✓  pred=4  true=4  |      5.2ms  |  4_aslkcasckmwlejk.mp4\n",
+            "✓  pred=4  true=4  |      6.4ms  |  4_kling_20251206_Text_to_Video_Generate_a_315_3.mp4\n",
+            "✓  pred=4  true=4  |      6.6ms  |  4_kling_20251207_Text_to_Video_Generate_a_521_1.mp4\n",
+            "✓  pred=6  true=6  |      4.6ms  |  6_dfjewaijsldkjfsaef.mp4\n",
+            "✗  pred=4  true=3  |      4.8ms  |  3_sdlkjslndflkseijlkjef.mp4\n",
+            "✓  pred=4  true=4  |      6.8ms  |  4_kling_20251209_Text_to_Video_Generate_a_218_0.mp4\n",
+            "✗  pred=5  true=4  |      4.1ms  |  4_sadflkjasldkjfalseij.mp4\n",
+            "✓  pred=3  true=3  |      8.7ms  |  3_sdlkfjaleknaksej.mp4\n",
+            "✓  pred=2  true=2  |      4.1ms  |  2_dsalkfjalwkenlke.mp4\n",
+            "✓  pred=5  true=5  |      4.7ms  |  5_sadfjhaslfkjasdlkfjsa.mp4\n",
+            "✗  pred=2  true=1  |      3.9ms  |  1_dksksjfwijf.mp4\n",
+            "✗  pred=3  true=2  |      6.9ms  |  2_dkjd823kjf.mp4\n",
+            "✓  pred=4  true=4  |      5.4ms  |  4_asdlkfjalsflnekj.mp4\n",
+            "✓  pred=5  true=5  |      6.9ms  |  5_sdfkljweoijlkjdsflkjweaij.mp4\n",
+            "✗  pred=4  true=3  |      3.5ms  |  3_sadlfkjawelnflksdjf.mp4\n",
+            "✓  pred=3  true=3  |      5.2ms  |  3_kling_kdjflaskdjf.mp4\n",
+            "✓  pred=2  true=2  |      4.0ms  |  2_dkdmkejkeimdh.mp4\n",
+            "✓  pred=3  true=3  |      6.1ms  |  3_kling_20251209_Text_to_Video_Generate_a_491_0.mp4\n",
+            "✓  pred=3  true=3  |      6.9ms  |  3_asldkfjalwieaskdfaskdf.mp4\n",
+            "✓  pred=3  true=3  |      3.6ms  |  3_kling_20251206_Text_to_Video_Generate_a_71_2.mp4\n",
+            "✓  pred=3  true=3  |      3.7ms  |  3_kling_20251206_Text_to_Video_Generate_a_71_0.mp4\n",
+            "✓  pred=4  true=4  |      7.5ms  |  4_kling_20251206_Text_to_Video_Generate_a_28_0.mp4\n",
+            "✓  pred=3  true=3  |      5.9ms  |  3_kling_20251209_Text_to_Video_Generate_a_403_1.mp4\n",
+            "✓  pred=3  true=3  |      3.8ms  |  3_kling_20251206_Text_to_Video_Generate_a_71_3.mp4\n",
+            "✓  pred=3  true=3  |      4.6ms  |  3_dsksdfjbvsdkj.mp4\n",
+            "✓  pred=3  true=3  |      6.2ms  |  3_kling_20251209_Image_to_Video_Generate_a_613_1.mp4\n",
+            "✗  pred=3  true=4  |      5.9ms  |  4_kling_20251209_Image_to_Video_Generate_a_635_1.mp4\n",
+            "✓  pred=3  true=3  |      7.9ms  |  3_kling_20251206_Text_to_Video_Generate_a_712_3.mp4\n",
+            "✓  pred=4  true=4  |      6.9ms  |  4_kling_20251209_Text_to_Video_Generate_a_190_0.mp4\n",
+            "✓  pred=4  true=4  |      5.9ms  |  4_20251209_Text_to_Video_Generate_a_561_0.mp4\n",
+            "✓  pred=3  true=3  |      5.1ms  |  3_kling_20251206_Text_to_Video_Generate_a_17_0.mp4\n",
+            "✓  pred=3  true=3  |      8.6ms  |  3_kling_20251206_Text_to_Video_Generate_a_315_0.mp4\n",
+            "✓  pred=3  true=3  |      6.0ms  |  3_kling_20251209_Image_to_Video_Generate_a_635_0.mp4\n",
+            "✓  pred=3  true=3  |      3.6ms  |  3_kling_20251205_Text_to_Video_In_a_grass_4697_0.mp4\n",
+            "✓  pred=4  true=4  |      6.8ms  |  4_kling_20251209_Text_to_Video_Generate_a_452_0.mp4\n",
+            "✓  pred=4  true=4  |      5.9ms  |  4_kling_20251209_Text_to_Video_Generate_a_263_1.mp4\n",
             "\n",
             "==================================================\n",
             "SUMMARY\n",
             "==================================================\n",
             "Total videos:         77\n",
-            "Correct:              [60]\n",
-            "Incorrect:                [17]\n",
+            "Correct:              [59]\n",
+            "Incorrect:                [18]\n",
             "\n",
-            "ACCURACY:             77.92%\n",
+            "ACCURACY:             76.62%\n",
             "\n",
-            "Total time:           534.31s\n",
-            "Avg per video:        5.1ms\n",
-            "Min latency:          2.8ms\n",
-            "Max latency:          11.3ms\n",
+            "Total time:           569.25s\n",
+            "Avg per video:        5.4ms\n",
+            "Min latency:          3.2ms\n",
+            "Max latency:          10.3ms\n",
             "==================================================\n"
           ]
         }
@@ -1598,6 +1620,8 @@
         "    all_labels = []\n",
         "    all_times = []\n",
         "\n",
+        "    janitor = Janitor()\n",
+        "\n",
         "    print(\"\\n\")\n",
         "\n",
         "    with torch.no_grad():\n",
@@ -1631,6 +1655,7 @@
         "\n",
         "            all_preds.extend(preds.cpu().numpy())\n",
         "            all_labels.extend(labels.cpu().numpy())\n",
+        "            janitor.smart_cleanup()\n",
         "\n",
         "    accuracy = correct / total\n",
         "    return accuracy, all_preds, all_labels, all_times\n",
@@ -1692,10 +1717,130 @@
         "    print(f\"Min latency:          {min(times):.1f}ms\")\n",
         "    print(f\"Max latency:          {max(times):.1f}ms\")\n",
         "    print(\"=\"*50)\n",
-        "    return accuracy, preds, labels\n",
+        "    return accuracy, preds, labels, times\n",
         "\n",
-        "_, _, _ = run_inference(model)"
+        "accuracy, preds, labels, latencys = run_inference(model)"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Performance Analysis"
+      ],
+      "metadata": {
+        "id": "F3YDAhQmGfNJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import re\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# 1. Put outputs into dataframe\n",
+        "labels_array = np.array(labels).flatten()\n",
+        "preds_array = preds.flatten()\n",
+        "\n",
+        "df = pd.DataFrame({\n",
+        "    'prediction': labels_array,\n",
+        "    'ground_truth': preds_array,\n",
+        "    'latency': latencys\n",
+        "})\n",
+        "df[['prediction', 'ground_truth']] = df[['prediction', 'ground_truth']].astype(int)\n",
+        "df['latency'] = df['latency'].astype(float)\n",
+        "df['error'] = df['prediction'] - df['ground_truth']\n",
+        "\n",
+        "# 2. RUN-TIME (LATENCY) DISTRIBUTION\n",
+        "plt.hist(df['latency'], bins=15, color='skyblue', edgecolor='black')\n",
+        "plt.title('Run-time (Latency) Distribution')\n",
+        "plt.xlabel('Latency (ms)')\n",
+        "plt.ylabel('Frequency')\n",
+        "plt.axvline(df['latency'].mean(), color='red', linestyle='--', label=f\"Mean: {df['latency'].mean():.2f}ms\")\n",
+        "plt.legend()\n",
+        "plt.savefig('latency_distribution.png')\n",
+        "plt.show()\n",
+        "\n",
+        "# 3. ERROR DISTRIBUTION\n",
+        "error_counts = df['error'].value_counts().sort_index()\n",
+        "plt.bar(error_counts.index, error_counts.values, color='salmon', edgecolor='black')\n",
+        "plt.title('Error Distribution (Pred - True)')\n",
+        "plt.xlabel('Error Magnitude')\n",
+        "plt.ylabel('Count')\n",
+        "plt.xticks(error_counts.index)\n",
+        "plt.savefig('error_distribution.png')\n",
+        "plt.show()\n",
+        "\n",
+        "# 4. TABLES: Summary & Confusion Matrix\n",
+        "summary = pd.DataFrame({\n",
+        "    \"Metric\": [\"Accuracy\", \"Mean Latency\", \"MAE (Mean Abs Error)\"],\n",
+        "    \"Value\": [f\"{(df['error'] == 0).mean():.2%}\", f\"{df['latency'].mean():.2f} ms\", f\"{df['error'].abs().mean():.2f}\"]\n",
+        "})\n",
+        "conf_matrix = pd.crosstab(df['ground_truth'], df['prediction'], rownames=['Actual'], colnames=['Predicted'])\n",
+        "\n",
+        "print(summary)\n",
+        "print(\"\\nConfusion Matrix:\\n\", conf_matrix)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "QmGaFcQQGe31",
+        "outputId": "ec49d899-3b90-4ffb-99c7-7ff76f33197b"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                 Metric    Value\n",
+            "0              Accuracy   76.62%\n",
+            "1          Mean Latency  5.44 ms\n",
+            "2  MAE (Mean Abs Error)     0.23\n",
+            "\n",
+            "Confusion Matrix:\n",
+            " Predicted  1   2   3   4  5  6  7\n",
+            "Actual                           \n",
+            "1          0   1   0   0  0  0  0\n",
+            "2          1  10   2   0  0  0  0\n",
+            "3          0   5  28   3  0  0  0\n",
+            "4          0   0   4  17  0  0  0\n",
+            "5          0   0   0   1  2  0  0\n",
+            "6          0   0   0   0  0  2  1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "ES-eNMG1GxP8"
+      },
+      "execution_count": 15,
+      "outputs": []
     }
   ],
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