diff --git "a/ViT.ipynb" "b/ViT.ipynb"
new file mode 100644--- /dev/null
+++ "b/ViT.ipynb"
@@ -0,0 +1,8768 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7oZd9vqVJWB8",
+        "outputId": "d1c240eb-928f-4719-a8d2-afb3361f9971"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
+            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
+            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
+            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
+            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
+            "\n",
+            "    To log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
+            "Enter your token (input will not be visible): \n",
+            "Add token as git credential? (Y/n) Y\n",
+            "Token is valid (permission: fineGrained).\n",
+            "The token `token1` has been saved to /root/.cache/huggingface/stored_tokens\n",
+            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
+            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
+            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
+            "\n",
+            "git config --global credential.helper store\n",
+            "\n",
+            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
+            "Token has not been saved to git credential helper.\n",
+            "Your token has been saved to /root/.cache/huggingface/token\n",
+            "Login successful.\n",
+            "The current active token is: `token1`\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install geopy > delete.txt\n",
+        "!pip install datasets > delete.txt\n",
+        "!pip install torch torchvision datasets > delete.txt\n",
+        "!pip install huggingface_hub > delete.txt\n",
+        "!rm delete.txt\n",
+        "!huggingface-cli login"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "r1QSTiT-KCpJ"
+      },
+      "outputs": [],
+      "source": [
+        "from datasets import load_dataset, Image\n",
+        "import torch\n",
+        "from torch import nn, optim\n",
+        "from torch.utils.data import DataLoader, Dataset\n",
+        "from torchvision import transforms, models\n",
+        "from datasets import load_dataset\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import torchvision.transforms as transforms\n",
+        "import numpy as np\n",
+        "import torch.nn as nn\n",
+        "import pickle as pkl\n",
+        "from torch import nn, optim\n",
+        "from geopy.distance import geodesic\n",
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
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+          ]
+        },
+        "outputId": "04167af7-cd8c-475a-a3f7-eeb80841a5cb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/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"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
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+          "data": {
+            "text/plain": [
+              "train-00007-of-00011.parquet:   0%|          | 0.00/453M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "7ce9a5e58cdb46c4adfcb329ad59ed82"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "train-00008-of-00011.parquet:   0%|          | 0.00/462M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8276cbafe66b45578a7c764b3423040e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "train-00009-of-00011.parquet:   0%|          | 0.00/455M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "5800ca36d4e444eb903ed7007807dc5e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "train-00010-of-00011.parquet:   0%|          | 0.00/458M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "75728650bb7a45bea96ce6a9928b1525"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split:   0%|          | 0/10214 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0c47c799cfb344e0aa1a8a8536853cfb"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "README.md:   0%|          | 0.00/409 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a6afc830635c4659b2aaa9767242ceb2"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "train-00000-of-00003.parquet:   0%|          | 0.00/445M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "cd0af570cfd7423b85688112df1545c3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "train-00001-of-00003.parquet:   0%|          | 0.00/444M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "acd470ae60ab422180d9a438163bebb2"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "train-00002-of-00003.parquet:   0%|          | 0.00/419M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "285aace34e1b41b0b963b35ff8724778"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split:   0%|          | 0/2556 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "dc43862cb64c41979883feeac996c2c9"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "dataset_train = load_dataset(\"CIS-5190-CIA/Training_images_augmented\", split=\"train\")\n",
+        "dataset_test = load_dataset(\"CIS-5190-CIA/Testing_images_augmented\", split=\"train\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "mX2u5ucjKF20"
+      },
+      "outputs": [],
+      "source": [
+        "class GPSImageDataset(Dataset):\n",
+        "    def __init__(self, hf_dataset, transform, lat_mean=None, lat_std=None, lon_mean=None, lon_std=None):\n",
+        "        self.hf_dataset = hf_dataset\n",
+        "        self.transform = transform\n",
+        "\n",
+        "        # Normalize the latitude and longitude\n",
+        "        self.latitudes = np.array(hf_dataset['Latitude'])\n",
+        "        self.longitudes = np.array(hf_dataset['Longitude'])\n",
+        "        self.latitude_mean = lat_mean if lat_mean is not None else self.latitudes.mean()\n",
+        "        self.latitude_std = lat_std if lat_std is not None else self.latitudes.std()\n",
+        "        self.longitude_mean = lon_mean if lon_mean is not None else self.longitudes.mean()\n",
+        "        self.longitude_std = lon_std if lon_std is not None else self.longitudes.std()\n",
+        "\n",
+        "        self.normalized_latitudes = (self.latitudes - self.latitude_mean) / self.latitude_std\n",
+        "        self.normalized_longitudes = (self.longitudes - self.longitude_mean) / self.longitude_std\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        return len(self.hf_dataset)\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        image = self.hf_dataset[idx]['image']\n",
+        "        latitude = self.normalized_latitudes[idx]\n",
+        "        longitude = self.normalized_longitudes[idx]\n",
+        "\n",
+        "        if self.transform:\n",
+        "            image = self.transform(image)\n",
+        "\n",
+        "        return image, torch.tensor([latitude, longitude], dtype=torch.float)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "xG-rQFy7KHsy"
+      },
+      "outputs": [],
+      "source": [
+        "transform = transforms.Compose([\n",
+        "    transforms.RandomResizedCrop(224),\n",
+        "    transforms.RandomHorizontalFlip(),\n",
+        "    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),\n",
+        "    transforms.ToTensor(),\n",
+        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
+        "])\n",
+        "\n",
+        "inference_transform = transforms.Compose([\n",
+        "    transforms.Resize((224, 224)),\n",
+        "    transforms.ToTensor(),\n",
+        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
+        "])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "XGwybd9tKSgW"
+      },
+      "outputs": [],
+      "source": [
+        "train_dataset = GPSImageDataset(hf_dataset=dataset_train, transform=transform)\n",
+        "lat_mean, lat_std = train_dataset.latitude_mean, train_dataset.latitude_std\n",
+        "lon_mean, lon_std = train_dataset.longitude_mean, train_dataset.longitude_std\n",
+        "\n",
+        "val_dataset = GPSImageDataset(\n",
+        "    hf_dataset=dataset_test,\n",
+        "    transform=inference_transform,\n",
+        "    lat_mean=lat_mean,\n",
+        "    lat_std=lat_std,\n",
+        "    lon_mean=lon_mean,\n",
+        "    lon_std=lon_std\n",
+        "\n",
+        ")\n",
+        "\n",
+        "train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
+        "val_dataloader = DataLoader(val_dataset, batch_size=32, shuffle=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "uG2zG_PBMDSb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 86,
+          "referenced_widgets": [
+            "e26915d7d00843f88a57affd737baf2a",
+            "fd89685df72d4278b60de860283781bf",
+            "c4ac11ace77b43cf99420a93374bd4e7",
+            "ca45196cf95d4c8f8346c4a880f9ebdd",
+            "733c61c06d1c4004a7b6bf37a9958c55",
+            "2772338e2bf04249bb95b8e8b438cbcb",
+            "6718e65d82be4790a7840609689686dd",
+            "e98a4e881abf4f0b95a724047d7dbdd9",
+            "0187afd9b6ab4a999ac1d849cd2926f6",
+            "5de32927bc8c42c288bffd2871c394c7",
+            "a9f562ed8d92422cbd916c303264a329"
+          ]
+        },
+        "outputId": "de5be0c1-790b-4c99-a78d-b7e9085d9fc5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "0it [00:00, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e26915d7d00843f88a57affd737baf2a"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from torch.optim import AdamW\n",
+        "from transformers import ViTModel\n",
+        "from tqdm.auto import tqdm"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "dpo8oPQcPYfG",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4d6c0dcb-6c0b-4a52-f5c0-bbe8607f32bb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "cpu\n"
+          ]
+        }
+      ],
+      "source": [
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "print(device)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "KnqafvQePcMa",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
+          "referenced_widgets": [
+            "6f517ea4f86f403482f9b34bad5d709d",
+            "4cc2e90a436042efbad7e79df3a94c0f",
+            "fd8e2b1dce0e4bfdb2cc66fc5b665ae8",
+            "745805f48e144eef9575b15434c14d39",
+            "5bb625d8480c4a32b687a85658264899",
+            "61dcd461422c4100b2ade1a25dd35478",
+            "09c22d76524d4dd3800afe1490ee2857",
+            "3b724961f6f447c8b8256d199748713d",
+            "81978f743300407a9f24e55543809275",
+            "6f673cea458941a9a85de5356a833534",
+            "36c579abb40a419c89f3f21791c65514",
+            "615c4a4737da4feb929b479a2716fa59",
+            "b04d7c58261147a7847b5c25d54e68ab",
+            "c73a7c32c57b4b018757af1394625349",
+            "15e6f587db23455fb531ad6411de2a26",
+            "36a4eacac9764a87b2dc506c02a40692",
+            "553cec8a0f344820945d642d2b424de5",
+            "692dab8b0abb42e9ad4c706fb83ea95b",
+            "bcd4c644bbc3424fbb9dbb7cc11f4136",
+            "565d030bf92a40bda3061261dc8ce690",
+            "b3618938953b47218dc5fa6f9861efe0",
+            "48a9669cbdc64822b3e16f363b6a8a1f"
+          ]
+        },
+        "outputId": "a47801dd-9be2-469b-dab3-88ea6ef0df4e"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/502 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6f517ea4f86f403482f9b34bad5d709d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/346M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "615c4a4737da4feb929b479a2716fa59"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "base_model = ViTModel.from_pretrained(\"google/vit-base-patch16-224-in21k\")\n",
+        "\n",
+        "class ViTForRegression(nn.Module):\n",
+        "    def __init__(self, base_model, num_outputs=2):\n",
+        "        super(ViTForRegression, self).__init__()\n",
+        "        self.base_model = base_model\n",
+        "        hidden_size = base_model.config.hidden_size\n",
+        "        self.regression_head = nn.Linear(hidden_size, num_outputs)\n",
+        "\n",
+        "    def forward(self, pixel_values):\n",
+        "        outputs = self.base_model(pixel_values=pixel_values)\n",
+        "        pooler_output = outputs.pooler_output\n",
+        "        predictions = self.regression_head(pooler_output)\n",
+        "        return predictions\n",
+        "\n",
+        "model = ViTForRegression(base_model).to(device)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RCXW7BygPeLw",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "a7afbbb61271438fa1932270b9f993e8",
+            "8c5c584df1fe4691892f81c3ad455791",
+            "ddc82735cf194f47a9a9d33c422d39cd",
+            "09bdb3894e404a9e8f579d457faef35a",
+            "26832cdc6d414e4289bc4f9b37f42edf",
+            "a5bd0f12ad7442dc8eae045006f4b929",
+            "4ab33af0ef334d38a7865d9a5a29c91c",
+            "b3a75e41f28248c4976424677bec0bd2",
+            "9ed42bee652549b68866fa23a40cde61",
+            "e9ad68d35d084fef8cb35ef169cab9c9",
+            "11c03754daea4e76ab85b841d2de1ea0"
+          ]
+        },
+        "outputId": "1ed47b54-ee5e-4493-ba5b-ab73a54313c7"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Training Epoch 1/10:   0%|          | 0/320 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a7afbbb61271438fa1932270b9f993e8"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "criterion = nn.MSELoss()\n",
+        "optimizer = AdamW(model.parameters(), lr=1e-4, weight_decay=1e-4)\n",
+        "\n",
+        "num_epochs = 10\n",
+        "for epoch in range(num_epochs):\n",
+        "  # ----- Training -----\n",
+        "  model.train()\n",
+        "  train_loss = 0.0\n",
+        "  train_geo_dist_sq = 0.0  # sum of squared geodesic distances\n",
+        "  for images, targets in tqdm(train_dataloader, desc=f\"Training Epoch {epoch+1}/{num_epochs}\"):\n",
+        "    images = images.to(device)\n",
+        "    targets = targets.to(device)\n",
+        "\n",
+        "    optimizer.zero_grad()\n",
+        "    outputs = model(images)\n",
+        "    loss = criterion(outputs, targets)\n",
+        "    loss.backward()\n",
+        "    optimizer.step()\n",
+        "\n",
+        "    train_loss += loss.item() * images.size(0)\n",
+        "\n",
+        "    # Compute geodesic distances for RMSE\n",
+        "    preds_denorm = outputs.detach().cpu().numpy() * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])\n",
+        "    actuals_denorm = targets.cpu().numpy() * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])\n",
+        "\n",
+        "    for actual, pred in zip(actuals_denorm, preds_denorm):\n",
+        "      distance = geodesic((actual[0], actual[1]), (pred[0], pred[1])).meters\n",
+        "      train_geo_dist_sq += distance ** 2\n",
+        "\n",
+        "  train_loss /= len(train_dataloader.dataset)\n",
+        "  train_rmse = np.sqrt(train_geo_dist_sq / len(train_dataloader.dataset))\n",
+        "\n",
+        "  # ----- Validation -----\n",
+        "  model.eval()\n",
+        "  val_loss = 0.0\n",
+        "  val_geo_dist_sq = 0.0\n",
+        "  with torch.no_grad():\n",
+        "    for images, targets in val_dataloader:\n",
+        "      images = images.to(device)\n",
+        "      targets = targets.to(device)\n",
+        "\n",
+        "      outputs = model(images)\n",
+        "      loss = criterion(outputs, targets)\n",
+        "      val_loss += loss.item() * images.size(0)\n",
+        "\n",
+        "      preds_denorm = outputs.cpu().numpy() * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])\n",
+        "      actuals_denorm = targets.cpu().numpy() * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])\n",
+        "\n",
+        "      for actual, pred in zip(actuals_denorm, preds_denorm):\n",
+        "        distance = geodesic((actual[0], actual[1]), (pred[0], pred[1])).meters\n",
+        "        val_geo_dist_sq += distance ** 2\n",
+        "\n",
+        "    val_loss /= len(val_dataloader.dataset)\n",
+        "    val_rmse = np.sqrt(val_geo_dist_sq / len(val_dataloader.dataset))\n",
+        "\n",
+        "    print(f\"Epoch [{epoch+1}/{num_epochs}]\")\n",
+        "    print(f\"  Training Loss: {train_loss:.4f}, Training RMSE (meters): {train_rmse:.4f}\")\n",
+        "    print(f\"  Validation Loss: {val_loss:.4f}, Validation RMSE (meters): {val_rmse:.4f}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# !pip install optuna > delete.txt\n",
+        "# !rm delete.txt"
+      ],
+      "metadata": {
+        "id": "G0YF0sBmgqiS"
+      },
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# import optuna\n",
+        "# from optuna.pruners import MedianPruner"
+      ],
+      "metadata": {
+        "id": "N3W4Uxs8oGaM"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# def objective(trial):\n",
+        "#     num_epochs = trial.suggest_int(\"num_epochs\", 3, 10)\n",
+        "#     learning_rate = trial.suggest_loguniform(\"learning_rate\", 1e-5, 1e-3)\n",
+        "#     weight_decay = trial.suggest_loguniform(\"weight_decay\", 1e-6, 1e-2)\n",
+        "\n",
+        "#     base_model = ViTModel.from_pretrained(\"google/vit-base-patch16-224-in21k\")\n",
+        "#     model = ViTForRegression(base_model).to(device)\n",
+        "\n",
+        "#     criterion = nn.MSELoss()\n",
+        "#     optimizer = AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)\n",
+        "\n",
+        "#     best_val_loss = float(\"inf\")\n",
+        "\n",
+        "#     for epoch in range(num_epochs):\n",
+        "#         # ----- Training -----\n",
+        "#         model.train()\n",
+        "#         train_loss = 0.0\n",
+        "#         train_geo_dist_sq = 0.0\n",
+        "\n",
+        "#         for images, targets in train_dataloader:\n",
+        "#             images = images.to(device)\n",
+        "#             targets = targets.to(device)\n",
+        "\n",
+        "#             optimizer.zero_grad()\n",
+        "#             outputs = model(images)\n",
+        "#             loss = criterion(outputs, targets)\n",
+        "#             loss.backward()\n",
+        "#             optimizer.step()\n",
+        "\n",
+        "#             train_loss += loss.item() * images.size(0)\n",
+        "\n",
+        "#             preds_denorm = outputs.detach().cpu().numpy() * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])\n",
+        "#             actuals_denorm = targets.cpu().numpy() * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])\n",
+        "\n",
+        "#             for actual, pred in zip(actuals_denorm, preds_denorm):\n",
+        "#                 distance = geodesic((actual[0], actual[1]), (pred[0], pred[1])).meters\n",
+        "#                 train_geo_dist_sq += distance ** 2\n",
+        "\n",
+        "#         train_loss /= len(train_dataloader.dataset)\n",
+        "#         train_rmse = np.sqrt(train_geo_dist_sq / len(train_dataloader.dataset))\n",
+        "\n",
+        "#         # ----- Validation -----\n",
+        "#         model.eval()\n",
+        "#         val_loss = 0.0\n",
+        "#         val_geo_dist_sq = 0.0\n",
+        "#         with torch.no_grad():\n",
+        "#             for images, targets in val_dataloader:\n",
+        "#                 images = images.to(device)\n",
+        "#                 targets = targets.to(device)\n",
+        "\n",
+        "#                 outputs = model(images)\n",
+        "#                 loss = criterion(outputs, targets)\n",
+        "#                 val_loss += loss.item() * images.size(0)\n",
+        "\n",
+        "#                 preds_denorm = outputs.cpu().numpy() * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])\n",
+        "#                 actuals_denorm = targets.cpu().numpy() * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])\n",
+        "\n",
+        "#                 for actual, pred in zip(actuals_denorm, preds_denorm):\n",
+        "#                     distance = geodesic((actual[0], actual[1]), (pred[0], pred[1])).meters\n",
+        "#                     val_geo_dist_sq += distance ** 2\n",
+        "\n",
+        "#         val_loss /= len(val_dataloader.dataset)\n",
+        "#         val_rmse = np.sqrt(val_geo_dist_sq / len(val_dataloader.dataset))\n",
+        "\n",
+        "#         trial.report(val_loss, epoch)\n",
+        "\n",
+        "#         print(f\"Epoch [{epoch+1}/{num_epochs}]\")\n",
+        "#         print(f\"  Training Loss: {train_loss:.4f}, Training RMSE (m): {train_rmse:.4f}\")\n",
+        "#         print(f\"  Validation Loss: {val_loss:.4f}, Validation RMSE (m): {val_rmse:.4f}\")\n",
+        "\n",
+        "#         if trial.should_prune():\n",
+        "#             raise optuna.TrialPruned()\n",
+        "\n",
+        "#         if val_loss < best_val_loss:\n",
+        "#             best_val_loss = val_loss\n",
+        "\n",
+        "#     return best_val_loss"
+      ],
+      "metadata": {
+        "id": "BFTDk4B4gx4U"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# pruner = MedianPruner(n_startup_trials=2, n_warmup_steps=1)\n",
+        "\n",
+        "# study = optuna.create_study(direction=\"minimize\", pruner=pruner)\n",
+        "# study.optimize(objective, n_trials=10, timeout=None)\n",
+        "\n",
+        "# print(\"Best trial:\")\n",
+        "# trial = study.best_trial\n",
+        "# print(f\"  Value: {trial.value}\")\n",
+        "# print(\"  Params: \")\n",
+        "# for key, value in trial.params.items():\n",
+        "#     print(f\"    {key}: {value}\")"
+      ],
+      "metadata": {
+        "id": "qwBX-TzHhGfq",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "eee6d8c7-8376-40bb-8973-30f9a7d48a09"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2024-12-16 18:01:14,382] A new study created in memory with name: no-name-4351846d-f1fa-483c-8ce9-8755bc63ab0a\n",
+            "<ipython-input-18-564456813dae>:3: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+            "  learning_rate = trial.suggest_loguniform(\"learning_rate\", 1e-5, 1e-3)\n",
+            "<ipython-input-18-564456813dae>:4: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+            "  weight_decay = trial.suggest_loguniform(\"weight_decay\", 1e-6, 1e-2)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [1/4]\n",
+            "  Training Loss: 0.3352, Training RMSE (m): 53.1289\n",
+            "  Validation Loss: 0.1393, Validation RMSE (m): 33.5326\n",
+            "Epoch [2/4]\n",
+            "  Training Loss: 0.1247, Training RMSE (m): 32.4027\n",
+            "  Validation Loss: 0.0673, Validation RMSE (m): 23.6043\n",
+            "Epoch [3/4]\n",
+            "  Training Loss: 0.0870, Training RMSE (m): 27.0406\n",
+            "  Validation Loss: 0.0470, Validation RMSE (m): 19.9312\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2024-12-16 18:40:08,830] Trial 0 finished with value: 0.03906576256726829 and parameters: {'num_epochs': 4, 'learning_rate': 4.118310216117835e-05, 'weight_decay': 0.00851912661916253}. Best is trial 0 with value: 0.03906576256726829.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [4/4]\n",
+            "  Training Loss: 0.0723, Training RMSE (m): 24.8742\n",
+            "  Validation Loss: 0.0391, Validation RMSE (m): 18.0416\n",
+            "Epoch [1/10]\n",
+            "  Training Loss: 0.3588, Training RMSE (m): 54.7761\n",
+            "  Validation Loss: 0.1960, Validation RMSE (m): 40.3484\n",
+            "Epoch [2/10]\n",
+            "  Training Loss: 0.2095, Training RMSE (m): 42.0244\n",
+            "  Validation Loss: 0.1089, Validation RMSE (m): 29.5526\n",
+            "Epoch [3/10]\n",
+            "  Training Loss: 0.1465, Training RMSE (m): 35.0081\n",
+            "  Validation Loss: 0.1201, Validation RMSE (m): 31.1180\n",
+            "Epoch [4/10]\n",
+            "  Training Loss: 0.1515, Training RMSE (m): 35.2810\n",
+            "  Validation Loss: 0.1042, Validation RMSE (m): 28.8841\n",
+            "Epoch [5/10]\n",
+            "  Training Loss: 0.1371, Training RMSE (m): 33.6605\n",
+            "  Validation Loss: 0.0879, Validation RMSE (m): 26.5345\n",
+            "Epoch [6/10]\n",
+            "  Training Loss: 0.1402, Training RMSE (m): 34.1774\n",
+            "  Validation Loss: 0.0705, Validation RMSE (m): 23.8248\n",
+            "Epoch [7/10]\n",
+            "  Training Loss: 0.1583, Training RMSE (m): 36.3012\n",
+            "  Validation Loss: 0.0983, Validation RMSE (m): 28.3246\n",
+            "Epoch [8/10]\n",
+            "  Training Loss: 0.1314, Training RMSE (m): 32.9326\n",
+            "  Validation Loss: 0.0622, Validation RMSE (m): 22.9911\n",
+            "Epoch [9/10]\n",
+            "  Training Loss: 0.1239, Training RMSE (m): 32.2246\n",
+            "  Validation Loss: 0.0748, Validation RMSE (m): 25.0666\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[I 2024-12-16 20:16:05,267] Trial 1 finished with value: 0.062155471884402125 and parameters: {'num_epochs': 10, 'learning_rate': 0.00030935006538154286, 'weight_decay': 0.00025249514372756814}. Best is trial 0 with value: 0.03906576256726829.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [10/10]\n",
+            "  Training Loss: 0.1010, Training RMSE (m): 28.9392\n",
+            "  Validation Loss: 0.0982, Validation RMSE (m): 27.6546\n",
+            "Epoch [1/5]\n",
+            "  Training Loss: 0.2956, Training RMSE (m): 49.8060\n",
+            "  Validation Loss: 0.1254, Validation RMSE (m): 31.9073\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "m6cdbBJsQGF_"
+      },
+      "outputs": [],
+      "source": [
+        "dataset_test = load_dataset(\"gydou/released_img\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hr7rNQe4QK5u"
+      },
+      "outputs": [],
+      "source": [
+        "test_dataset = GPSImageDataset(\n",
+        "    hf_dataset=dataset_test['train'],\n",
+        "    transform=inference_transform,\n",
+        "    lat_mean=lat_mean,\n",
+        "    lat_std=lat_std,\n",
+        "    lon_mean=lon_mean,\n",
+        "    lon_std=lon_std\n",
+        ")\n",
+        "\n",
+        "test_dataloader = DataLoader(\n",
+        "    test_dataset,\n",
+        "    batch_size=32,\n",
+        "    shuffle=False,\n",
+        "    num_workers=4\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BVDcw3SLQMiA"
+      },
+      "outputs": [],
+      "source": [
+        "def evaluate_final_rmse(ensemble_model, data_loader, lat_mean, lon_mean, lat_std, lon_std):\n",
+        "    \"\"\"\n",
+        "    Evaluate the ensemble model on a given dataset and compute final RMSE in meters.\n",
+        "    \"\"\"\n",
+        "    ensemble_model.eval()\n",
+        "    total_loss = 0.0\n",
+        "    total_samples = 0\n",
+        "\n",
+        "    with torch.no_grad():\n",
+        "        for images, targets in data_loader:\n",
+        "            images, targets = images.to(device), targets.to(device)\n",
+        "            outputs = ensemble_model(images)\n",
+        "            preds_denorm = outputs.cpu().numpy() * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])\n",
+        "            actuals_denorm = targets.cpu().numpy() * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])\n",
+        "\n",
+        "            for pred, actual in zip(preds_denorm, actuals_denorm):\n",
+        "                distance = geodesic((actual[0], actual[1]), (pred[0], pred[1])).meters\n",
+        "                total_loss += distance ** 2\n",
+        "            total_samples += targets.size(0)\n",
+        "\n",
+        "    final_loss = total_loss / total_samples\n",
+        "    final_rmse = np.sqrt(final_loss)\n",
+        "\n",
+        "    return final_loss, final_rmse"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "j9juk1IBQOCX"
+      },
+      "outputs": [],
+      "source": [
+        "final_loss, final_rmse = evaluate_final_rmse(\n",
+        "    ensemble_model=model,\n",
+        "    data_loader=test_dataloader,\n",
+        "    lat_mean=lat_mean,\n",
+        "    lon_mean=lon_mean,\n",
+        "    lat_std=lat_std,\n",
+        "    lon_std=lon_std\n",
+        ")\n",
+        "\n",
+        "print(f\"Final Test Loss (m^2): {final_loss:.4f}\")\n",
+        "print(f\"Final Test RMSE (m): {final_rmse:.4f}\")"
+      ]
+    }
+  ],
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