diff --git "a/Finetune-SigLIP2-Image-Classification/2.SigLIP2_Finetune_ImageClassification_OnlyTrain_Splits.ipynb" "b/Finetune-SigLIP2-Image-Classification/2.SigLIP2_Finetune_ImageClassification_OnlyTrain_Splits.ipynb"
new file mode 100644--- /dev/null
+++ "b/Finetune-SigLIP2-Image-Classification/2.SigLIP2_Finetune_ImageClassification_OnlyTrain_Splits.ipynb"
@@ -0,0 +1,6876 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **✦︎ Fine-Tuning SigLIP 2 for Image Classification**\n",
+        "\n",
+        "This Colab notebook demonstrates how to fine-tune **[SigLIP 2](https://huggingface.co/google/siglip2-base-patch16-224)** — a powerful multilingual vision-language model — for **single-label image classification** tasks.\n",
+        "\n",
+        "Aslo it integrates techniques like captioning-based pretraining, self-distillation, and masked prediction into a unified training pipeline.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "🤗 **Type 2: Dataset with Only Train Split**\n",
+        "\n",
+        "In this setup, the dataset provides only a train split — there is no predefined test or validation set. This is an exceptional case, often encountered in custom or limited datasets.\n",
+        "\n",
+        "    Training Phase:\n",
+        "    The model is fine-tuned using the entire train split, with all available image-label pairs.\n",
+        "\n",
+        "    Evaluation Phase:\n",
+        "    Since no separate test split is available, a portion of the train split is typically reserved for evaluation (e.g., using a validation split via train_test_split() or K-fold cross-validation).\n",
+        "    Alternatively, the model may be evaluated directly on the training set to get a coarse idea of performance — though this may lead to overfitting.\n",
+        "\n",
+        "⚠️ Use this configuration with caution, as evaluating on training data can give an overly optimistic view of performance.\n",
+        "\n",
+        "✅ Best suited for exploratory experiments, low-resource scenarios, or datasets collected without test annotations.\n"
+      ],
+      "metadata": {
+        "id": "35lDGCTgmf0R"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "edrxauqsOe4u"
+      },
+      "source": [
+        "**1. Install the packages**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "4L0K84qCM3iB"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "!pip install evaluate datasets==3.2.0 accelerate\n",
+        "!pip install transformers==4.50.0 torchvision\n",
+        "!pip install huggingface-hub==0.31.0 hf_xet\n",
+        "#Hold tight, this will take around 2-3 minutes."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Dataset ID2Label Mapping**\n",
+        "\n",
+        "Note : The `id2label` mapping shows how numerical class IDs correspond to human-readable labels.  \n",
+        "This is **not required** for training or evaluation — it's just for **visual reference** and **debugging**.\n",
+        "\n",
+        "    To demonstrate the fine-tuning process, we use the (https://huggingface.co/datasets/prithivMLmods/Age-Classification-Set) dataset.\n",
+        "\n",
+        "    This dataset contains images labeled by age ranges, making it ideal for training and evaluating SigLIP 2 on an age-based image classification task.\n"
+      ],
+      "metadata": {
+        "id": "V7RGZZv5vCbS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "# Load the dataset\n",
+        "dataset = load_dataset(\"prithivMLmods/Age-Classification-Set\")\n",
+        "\n",
+        "# Extract unique labels\n",
+        "labels = dataset[\"train\"].features[\"label\"].names\n",
+        "\n",
+        "# Create id2label mapping\n",
+        "id2label = {str(i): label for i, label in enumerate(labels)}\n",
+        "\n",
+        "# Print the mapping\n",
+        "print(id2label)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 269,
+          "referenced_widgets": [
+            "6fb851a84fd4400198479c84691d19ea",
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+            "64a0fa6f34104551a54f74ed17626c31",
+            "a9aca943258647dbba4d4b3492b7157d",
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+            "1f2e123b85e042279f85e0fb5f6d09f0",
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+            "3e7768f4644f4711a1bbdfc30434d50c",
+            "06cf7f84fb014bcc88e6d795f9dcfc99",
+            "1ca30fa93b824dd5bbc49160c1d69f84",
+            "f9617c23358942609992568500d5e48a",
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+            "3ba41fd59e35457e8ef750bc32de922e",
+            "f9f82454cd2843e7a7cb10214e276ced",
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+            "91f0432e280c4bcd9fa2a4d1764643c8",
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+          ]
+        },
+        "id": "MwOTRQvivGrj",
+        "outputId": "a14821eb-40d1-4f43-8ce6-1a3180dafe0c"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:104: UserWarning: \n",
+            "Error while fetching `HF_TOKEN` secret value from your vault: 'Requesting secret HF_TOKEN timed out. Secrets can only be fetched when running from the Colab UI.'.\n",
+            "You are not authenticated with the Hugging Face Hub in this notebook.\n",
+            "If the error persists, please let us know by opening an issue on GitHub (https://github.com/huggingface/huggingface_hub/issues/new).\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "README.md:   0%|          | 0.00/898 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6fb851a84fd4400198479c84691d19ea"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "0000.parquet:   0%|          | 0.00/502M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "563ed9344b3e4ceeab29ffa8f1e082dd"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "0001.parquet:   0%|          | 0.00/107M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ed4ebc70089747b8b4a2698fd82b5f9a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split:   0%|          | 0/19016 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "47c17b07376a40c18f7c6479d9003518"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'0': '0-12', '1': '13-20', '2': '21-44', '3': '45-64', '4': '65+'}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Uf8nA1zcO0Jm"
+      },
+      "source": [
+        "**2. Import modules required for data manipulation, model training, and image preprocessing.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "0rssbtycmdF8"
+      },
+      "outputs": [],
+      "source": [
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "\n",
+        "import gc\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import itertools\n",
+        "from collections import Counter\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.metrics import accuracy_score, roc_auc_score, confusion_matrix, classification_report, f1_score\n",
+        "from imblearn.over_sampling import RandomOverSampler\n",
+        "import evaluate\n",
+        "from datasets import Dataset, Image, ClassLabel\n",
+        "from transformers import (\n",
+        "    TrainingArguments,\n",
+        "    Trainer,\n",
+        "    DefaultDataCollator\n",
+        ")\n",
+        "\n",
+        "from transformers import AutoImageProcessor\n",
+        "from transformers import SiglipForImageClassification\n",
+        "from transformers.image_utils import load_image\n",
+        "\n",
+        "import torch\n",
+        "from torch.utils.data import DataLoader\n",
+        "from torchvision.transforms import (\n",
+        "    CenterCrop,\n",
+        "    Compose,\n",
+        "    Normalize,\n",
+        "    RandomRotation,\n",
+        "    RandomResizedCrop,\n",
+        "    RandomHorizontalFlip,\n",
+        "    RandomAdjustSharpness,\n",
+        "    Resize,\n",
+        "    ToTensor\n",
+        ")\n",
+        "\n",
+        "from PIL import Image, ExifTags\n",
+        "from PIL import Image as PILImage\n",
+        "from PIL import ImageFile\n",
+        "# Enable loading truncated images\n",
+        "ImageFile.LOAD_TRUNCATED_IMAGES = True"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**3. Loading and Preparing the Dataset**"
+      ],
+      "metadata": {
+        "id": "YwUwky1LzhH3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from datasets import load_dataset\n",
+        "dataset = load_dataset(\"prithivMLmods/Age-Classification-Set\", split=\"train\")\n",
+        "\n",
+        "from pathlib import Path\n",
+        "\n",
+        "# Build DataFrame from dataset (for oversampling)\n",
+        "file_names = []\n",
+        "labels = []\n",
+        "for example in dataset:\n",
+        "    file_path = str(example['image'])\n",
+        "    label = example['label']\n",
+        "    file_names.append(file_path)\n",
+        "    labels.append(label)\n",
+        "\n",
+        "print(len(file_names), len(labels))\n",
+        "df = pd.DataFrame.from_dict({\"image\": file_names, \"label\": labels})\n",
+        "print(\"DataFrame shape:\", df.shape)\n",
+        "print(df.head())\n",
+        "print(\"Unique labels:\", df['label'].unique())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "r6YHpfeMzemH",
+        "outputId": "8605d680-c166-47c3-d563-9432f24731c1"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "19016 19016\n",
+            "DataFrame shape: (19016, 2)\n",
+            "                                               image  label\n",
+            "0  <PIL.JpegImagePlugin.JpegImageFile image mode=...      0\n",
+            "1  <PIL.JpegImagePlugin.JpegImageFile image mode=...      0\n",
+            "2  <PIL.JpegImagePlugin.JpegImageFile image mode=...      0\n",
+            "3  <PIL.JpegImagePlugin.JpegImageFile image mode=...      0\n",
+            "4  <PIL.JpegImagePlugin.JpegImageFile image mode=...      0\n",
+            "Unique labels: [0 1 2 3 4]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**4. Creating a DataFrame and Balancing the Dataset & Working with a Subset of Labels**\n",
+        "\n",
+        "> Manual Label List (for Custom Naming & Mapping Consistency)\n",
+        "\n",
+        "We manually define the `labels_list` to:\n",
+        "\n",
+        "    Avoid auto-mapping issues that may arise due to inconsistent label formats in the dataset.\n",
+        "\n",
+        "    Support flexible naming conventions, especially when label names need to follow a specific format or order.\n",
+        "\n",
+        "    Ensure consistent behavior across different tools (like `ClassLabel`, Hugging Face datasets, and visualization libraries).\n"
+      ],
+      "metadata": {
+        "id": "kZZK-vPVzxiH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.DataFrame.from_dict({\"image\": file_names, \"label\": labels})\n",
+        "print(df.shape)\n",
+        "\n",
+        "df.head()\n",
+        "df['label'].unique()\n",
+        "\n",
+        "# Oversample to balance classes\n",
+        "y = df[['label']]\n",
+        "df_no_label = df.drop(['label'], axis=1)\n",
+        "ros = RandomOverSampler(random_state=83)\n",
+        "df_resampled, y_resampled = ros.fit_resample(df_no_label, y)\n",
+        "df_resampled['label'] = y_resampled\n",
+        "df = df_resampled  # use the oversampled DataFrame\n",
+        "del y, y_resampled, df_no_label\n",
+        "gc.collect()\n",
+        "\n",
+        "#labels_list = ['example_label_0', 'example_label_1'................,'example_label_n-1']\n",
+        "labels_list = ['Child 0-12', 'Teenager 13-20', 'Adult 21-44', 'Middle Age 45-64', 'Aged 65+']\n",
+        "\n",
+        "label2id = {label: i for i, label in enumerate(labels_list)}\n",
+        "id2label = {i: label for i, label in enumerate(labels_list)}\n",
+        "ClassLabels = ClassLabel(num_classes=len(labels_list), names=labels_list)\n",
+        "print(\"Mapping of IDs to Labels:\", id2label)\n",
+        "print(\"Mapping of Labels to IDs:\", label2id)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zH_c7k7H1-29",
+        "outputId": "0bd3c82e-83a5-4ac7-d5ec-be45ba841e48"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(19016, 2)\n",
+            "Mapping of IDs to Labels: {0: 'Child 0-12', 1: 'Teenager 13-20', 2: 'Adult 21-44', 3: 'Middle Age 45-64', 4: 'Aged 65+'}\n",
+            "Mapping of Labels to IDs: {'Child 0-12': 0, 'Teenager 13-20': 1, 'Adult 21-44': 2, 'Middle Age 45-64': 3, 'Aged 65+': 4}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**5. Mapping and Casting Labels**"
+      ],
+      "metadata": {
+        "id": "rXvzq4AY15XF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Update dataset with label mapping\n",
+        "def map_label2id(example):\n",
+        "    example['label'] = ClassLabels.str2int(example['label'])\n",
+        "    return example"
+      ],
+      "metadata": {
+        "id": "JIIabbmJ0TiX"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**6. Splitting the Dataset**"
+      ],
+      "metadata": {
+        "id": "4QyBBxjo2jOO"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dataset = dataset.map(map_label2id, batched=True)\n",
+        "dataset = dataset.cast_column('label', ClassLabels)\n",
+        "\n",
+        "full_data = dataset"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
+          "referenced_widgets": [
+            "b2122d03211d4b6394a60ca0304e83d6",
+            "78ef3165db9c41acb3f3702b968dd549",
+            "2338a996a1b24dd4890f63dc18c59e4b",
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+            "a67701dee76249c38fcada922b6b1bc9",
+            "3ef2b4e3663e44d0b7d4d9d4aa8e558f",
+            "1712beea91c640c7bb36473d3238ee1b",
+            "c737fe25708d489da4896531c2fae832",
+            "80491ac6a64c4ec891e5ac435f294e02",
+            "00879055f43949a9ae6ba66d1427db64",
+            "a70c926d48064a96ad5caa73c97d2121",
+            "f772a6d67a024534865b9167210f7d0b",
+            "4f8daf46a27d4f2ab855e367b770216b",
+            "4ea0b30f1bde4355842666f5372e9e0c",
+            "ce0a5d8e961f447986824af54dac8cc0",
+            "60551eb5cfb64c75957d1bca1e4c0a6d",
+            "3177ec25262048eba2a4961a0b9778be",
+            "375a1ee1c9a246f89b34d200f73f421c"
+          ]
+        },
+        "id": "AdBEyEI71ldo",
+        "outputId": "d0bf90ce-7eb7-4b9a-f41d-76647f68428a"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/19016 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b2122d03211d4b6394a60ca0304e83d6"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Casting the dataset:   0%|          | 0/19016 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c737fe25708d489da4896531c2fae832"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**7. Setting Up the Model and Processor**"
+      ],
+      "metadata": {
+        "id": "pgMyNfLs2yNW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_str = \"google/siglip2-base-patch16-224\"\n",
+        "processor = AutoImageProcessor.from_pretrained(model_str)\n",
+        "\n",
+        "# Extract preprocessing parameters\n",
+        "image_mean, image_std = processor.image_mean, processor.image_std\n",
+        "size = processor.size[\"height\"]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 86,
+          "referenced_widgets": [
+            "eab3b84d6c2649748c3c433f1b343da5",
+            "9710a78ba44441658ade301cf89a08b1",
+            "ec4f028fe5504c7bb82e0e78dedc6588",
+            "8f4b289272a640acb06a8a170311bb07",
+            "cf13c7f387ad4480968b29b335e93772",
+            "64fddc20ecf948c29a47b6921c34e7f5",
+            "2e3b60634f1f42d18ed2f3be747d725c",
+            "cb169a0ad5494822a6a80dea3bddc1cf",
+            "3ee18d26ff094153b6c90d488922412b",
+            "d3a4e51f8c904be190d191aa002069da",
+            "f06f61f478304953b753b92a241b36a8"
+          ]
+        },
+        "id": "pcTtRIrJ3L4N",
+        "outputId": "019cd95d-dedf-41a3-c363-568f13a3e8a4"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "preprocessor_config.json:   0%|          | 0.00/394 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "eab3b84d6c2649748c3c433f1b343da5"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.50, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**8. Defining Data Transformations**"
+      ],
+      "metadata": {
+        "id": "ZuqaZWIp3u8F"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define training and validation transforms\n",
+        "_train_transforms = Compose([\n",
+        "    Resize((size, size)),\n",
+        "    RandomRotation(90),\n",
+        "    RandomAdjustSharpness(2),\n",
+        "    ToTensor(),\n",
+        "    Normalize(mean=image_mean, std=image_std)\n",
+        "])\n",
+        "_val_transforms = Compose([\n",
+        "    Resize((size, size)),\n",
+        "    ToTensor(),\n",
+        "    Normalize(mean=image_mean, std=image_std)\n",
+        "])"
+      ],
+      "metadata": {
+        "id": "IIHobuid30G-"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**9. Applying Transformations to the Dataset**"
+      ],
+      "metadata": {
+        "id": "xt6kH0QW3-xf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def train_transforms(examples):\n",
+        "    examples['pixel_values'] = [_train_transforms(image.convert(\"RGB\")) for image in examples['image']]\n",
+        "    return examples\n",
+        "\n",
+        "def val_transforms(examples):\n",
+        "    examples['pixel_values'] = [_val_transforms(image.convert(\"RGB\")) for image in examples['image']]\n",
+        "    return examples\n",
+        "\n",
+        "# Create training and evaluation datasets with different transforms\n",
+        "train_data = full_data.with_transform(train_transforms)\n",
+        "eval_data = full_data.with_transform(val_transforms)"
+      ],
+      "metadata": {
+        "id": "qPiM8jas4FXm"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**10. Creating a Data Collator**"
+      ],
+      "metadata": {
+        "id": "-woWc8qQ4SP0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def collate_fn(examples):\n",
+        "    pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n",
+        "    labels = torch.tensor([example['label'] for example in examples])\n",
+        "    return {\"pixel_values\": pixel_values, \"labels\": labels}"
+      ],
+      "metadata": {
+        "id": "dhosTciU4X49"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**11. Initializing the Model**"
+      ],
+      "metadata": {
+        "id": "Ow0U04HP4amM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = SiglipForImageClassification.from_pretrained(model_str, num_labels=len(labels_list))\n",
+        "model.config.id2label = id2label\n",
+        "model.config.label2id = label2id\n",
+        "\n",
+        "print(model.num_parameters(only_trainable=True) / 1e6)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153,
+          "referenced_widgets": [
+            "2cb6733897864e459d03d3aa7dd33f08",
+            "2b3b8b9a079f4c6ea2a44f09b0e40be2",
+            "572048fefc8e4121990c191344fc3e56",
+            "710afdbebdef4e008605449ccaba9a91",
+            "e6347781a5e44ead93daa32464d6e487",
+            "9b09410ac2d449ffa35f27370d92e0d6",
+            "f3e8f4465ace4c5ab85590f6e0f2fb76",
+            "22709510ea3e4b0495eaf3f4014a0581",
+            "3b5b7525fec448ef98ef59aea7739d14",
+            "b8b09ab6449e43aa94b180fc01f1a199",
+            "c8ff710d00f645419a3c88978e73a2fb",
+            "44fa1fb9a621488b81a8c600bb30327a",
+            "7110380ac3044824af687a10ba1b0978",
+            "75530be802bc406b80b6e69288b50f61",
+            "abc7a4d7410f4e5db3a242431fe8b183",
+            "e64c6be5a5614b7ba4d1514a82bf5f0b",
+            "d04824506c034843bd03064b283aae02",
+            "c2568717133648db93c6a519796423f6",
+            "d61f674a59e14cb985c240c8ebc380b9",
+            "733600c578194bd28025851cac65b855",
+            "c82b71990c4c4873b1e5aed2358cfc30",
+            "4aeeb6425a824efa9949cb369c6d75a8"
+          ]
+        },
+        "id": "TAJBpBcO4feE",
+        "outputId": "ba705f8f-32ff-4d76-db61-38bedd090090"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/253 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2cb6733897864e459d03d3aa7dd33f08"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/1.50G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "44fa1fb9a621488b81a8c600bb30327a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some weights of SiglipForImageClassification were not initialized from the model checkpoint at google/siglip2-base-patch16-224 and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "92.888069\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**12. Defining Metrics and the Compute Function**"
+      ],
+      "metadata": {
+        "id": "_OZV3GId4kv2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "accuracy_metric = evaluate.load(\"accuracy\")\n",
+        "def compute_metrics(eval_pred):\n",
+        "    predictions = eval_pred.predictions\n",
+        "    label_ids = eval_pred.label_ids\n",
+        "    predicted_labels = predictions.argmax(axis=1)\n",
+        "    acc_score = accuracy_metric.compute(predictions=predicted_labels, references=label_ids)['accuracy']\n",
+        "    return {\"accuracy\": acc_score}"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "7b99b5b40be244e5a8d1ec1e1ebaf9c0",
+            "d2fe7290854f47b089d1aea3562ef9ba",
+            "8a685b7b45c74c35a3711abb460aad31",
+            "743ead40f5bf4f1196bda35151a1da81",
+            "d48373048040480faba795c3e17ccd68",
+            "e98ee0e65dac412fbe4239042e336ca6",
+            "f0ebf47a16ce42899ee76c8a5119755f",
+            "6832b6eda54d4ed3a1ddc1d3b252065a",
+            "f5cadfc5bce1476bb2b0e805d28002e8",
+            "ad03ae202c5945bcbeaf32bbf7291840",
+            "a444a42f0f594dbcb15066e1862aa86a"
+          ]
+        },
+        "id": "2ZMQzLWl4ijD",
+        "outputId": "75447e87-72fb-42cc-ee35-81675e3ebc8e"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading builder script: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "7b99b5b40be244e5a8d1ec1e1ebaf9c0"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**13. Setting Up Training Arguments**"
+      ],
+      "metadata": {
+        "id": "sJZbgNxk7LzB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "args = TrainingArguments(\n",
+        "    output_dir=\"siglip2-image-classification/\",\n",
+        "    logging_dir='./logs',\n",
+        "    evaluation_strategy=\"epoch\",\n",
+        "    learning_rate=2e-5,\n",
+        "    per_device_train_batch_size=32,\n",
+        "    per_device_eval_batch_size=8,\n",
+        "    num_train_epochs=2,\n",
+        "    weight_decay=0.02,\n",
+        "    warmup_steps=50,\n",
+        "    remove_unused_columns=False,\n",
+        "    save_strategy='epoch',\n",
+        "    load_best_model_at_end=True,\n",
+        "    save_total_limit=2,\n",
+        "    report_to=\"none\"\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "l2u4d3dg6PzK"
+      },
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**14. Initializing the Trainer**"
+      ],
+      "metadata": {
+        "id": "ASw82izY7w-s"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainer = Trainer(\n",
+        "    model=model,\n",
+        "    args=args,\n",
+        "    train_dataset=train_data,\n",
+        "    eval_dataset=eval_data,\n",
+        "    data_collator=collate_fn,\n",
+        "    compute_metrics=compute_metrics,\n",
+        "    tokenizer=processor,\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "Hh9OOpq67wkh"
+      },
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**15. Evaluating, Training, and Predicting**"
+      ],
+      "metadata": {
+        "id": "roNYBEmA7-mp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainer.evaluate()\n",
+        "\n",
+        "trainer.train()\n",
+        "\n",
+        "trainer.evaluate()\n",
+        "\n",
+        "outputs = trainer.predict(eval_data)\n",
+        "print(outputs.metrics)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 197
+        },
+        "id": "FSK69Nd_7-RJ",
+        "outputId": "5d4133d9-6e25-4d51-b007-1fdf11f55213"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='4754' max='2377' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [2377/2377 22:18]\n",
+              "    </div>\n",
+              "    "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='1190' max='1190' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [1190/1190 35:07, Epoch 2/2]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Epoch</th>\n",
+              "      <th>Training Loss</th>\n",
+              "      <th>Validation Loss</th>\n",
+              "      <th>Model Preparation Time</th>\n",
+              "      <th>Accuracy</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>1</td>\n",
+              "      <td>0.752000</td>\n",
+              "      <td>0.434574</td>\n",
+              "      <td>0.002600</td>\n",
+              "      <td>0.834876</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>2</td>\n",
+              "      <td>0.408700</td>\n",
+              "      <td>0.222109</td>\n",
+              "      <td>0.002600</td>\n",
+              "      <td>0.917911</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": []
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'test_loss': 0.22210857272148132, 'test_model_preparation_time': 0.0026, 'test_accuracy': 0.9179112326461927, 'test_runtime': 301.4337, 'test_samples_per_second': 63.085, 'test_steps_per_second': 7.886}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**16. Computing Additional Metrics and Plotting the Confusion Matrix**"
+      ],
+      "metadata": {
+        "id": "4lrr8wcI8944"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_true = outputs.label_ids\n",
+        "y_pred = outputs.predictions.argmax(1)\n",
+        "\n",
+        "\n",
+        "def plot_confusion_matrix(cm, classes, title='Confusion Matrix', cmap=plt.cm.Reds, figsize=(10, 8)):\n",
+        "    plt.figure(figsize=figsize)\n",
+        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
+        "    plt.title(title)\n",
+        "    plt.colorbar()\n",
+        "    tick_marks = np.arange(len(classes))\n",
+        "    plt.xticks(tick_marks, classes, rotation=90)\n",
+        "    plt.yticks(tick_marks, classes)\n",
+        "    fmt = '.0f'\n",
+        "    thresh = cm.max() / 2.0\n",
+        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+        "        plt.text(j, i, format(cm[i, j], fmt),\n",
+        "                 horizontalalignment=\"center\",\n",
+        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+        "    plt.ylabel('True label')\n",
+        "    plt.xlabel('Predicted label')\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "acc = accuracy_score(y_true, y_pred)\n",
+        "f1 = f1_score(y_true, y_pred, average='macro')\n",
+        "print(f\"Accuracy: {acc:.4f}\")\n",
+        "print(f\"F1 Score: {f1:.4f}\")\n",
+        "\n",
+        "if len(labels_list) <= 150:\n",
+        "    cm = confusion_matrix(y_true, y_pred)\n",
+        "    plot_confusion_matrix(cm, labels_list, figsize=(8, 6))\n",
+        "\n",
+        "print(\"\\nClassification Report:\")\n",
+        "print(classification_report(y_true, y_pred, target_names=labels_list, digits=4))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 885
+        },
+        "id": "nypmCSIP9Cw6",
+        "outputId": "d90f187e-3754-4b69-bc0a-87ee125bb908"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy: 0.9179\n",
+            "F1 Score: 0.8951\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Classification Report:\n",
+            "                  precision    recall  f1-score   support\n",
+            "\n",
+            "      Child 0-12     0.9434    0.9804    0.9615      2193\n",
+            "  Teenager 13-20     0.9127    0.6936    0.7882      1779\n",
+            "     Adult 21-44     0.9142    0.9749    0.9436      9999\n",
+            "Middle Age 45-64     0.9078    0.8528    0.8794      3785\n",
+            "        Aged 65+     0.9391    0.8690    0.9027      1260\n",
+            "\n",
+            "        accuracy                         0.9179     19016\n",
+            "       macro avg     0.9234    0.8742    0.8951     19016\n",
+            "    weighted avg     0.9178    0.9179    0.9156     19016\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**17. Saving the Model and Uploading to Hugging Face Hub**"
+      ],
+      "metadata": {
+        "id": "tmHX19Zd9KF4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainer.save_model()"
+      ],
+      "metadata": {
+        "id": "q0F5O5A89QqA"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import notebook_login, HfApi\n",
+        "notebook_login()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17,
+          "referenced_widgets": [
+            "014b0b703a2341cfac47ca4823a9fb7a",
+            "58309ea94a3a459b9e9cafcf63b68c86",
+            "8fe209981d2845fe8480772df79ed41b",
+            "88fd0562f7274c749191da6f61bbff6a",
+            "9901e195ae39475cb48d2639689c2dc5",
+            "a7663509d091452896b4917e1b26d9dc",
+            "9dc4bc0dabd14681a473a47d9c44d878",
+            "c53bed53fcc04dbdbbb63dd474e731a2",
+            "263344e0c82f452a88dcc13a44bb7bce",
+            "de9dc963638a4522be7eff694f3c39b1",
+            "01a7f43c0e714ae3bd7c4a7f386a8fe1",
+            "5b2316a7e8ab4407abf0cf547347bfa8",
+            "c1197b3212cc42e88b02c18f64f2fd8b",
+            "13357c4426a949b38e4f6b865db0c36f",
+            "e351e6e7443f4d4ebabcf8ae80b6cc00",
+            "249162fdf53f43dfa82bc932120725e3",
+            "6f521bb2b213432c99bb54aea612a142",
+            "bdcb58d7dbac436bb148a5003d0707be",
+            "8dcbf00b9120470d9956dff051e7840e",
+            "488eaeba013b412aaa46de33df10c996"
+          ]
+        },
+        "id": "sJfH3v0am_O4",
+        "outputId": "a095f77c-d44a-4d4b-a725-06cb7f94499c"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "014b0b703a2341cfac47ca4823a9fb7a"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 136,
+          "referenced_widgets": [
+            "929b478b8c93433b88085ce74de51efe",
+            "446017e518cb4cb58c26cc315d54d25f",
+            "c0e05443e5684b9fbc8ee67b46333946",
+            "e73eadadcb434545a1a9de072920fd93",
+            "af5de1f515e8400698e55ab59c71b188",
+            "5f6a00c88d2842b49039db4220c5fddf",
+            "9252529737634e09b9685e604e1b6ea6",
+            "b15fda830c154e38bc6fd091bac8469e",
+            "186226fc49244c3babe38f37d2dcbcee",
+            "fa9628883cb04afbb9ca989d11fbf502",
+            "705916ea75e347f898cef15b2d000014"
+          ]
+        },
+        "id": "LQLQunHCmdF8",
+        "outputId": "0423cea8-5c24-4817-d8e2-a97d1907a20b"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Uploading...:   0%|          | 0.00/2.49G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "929b478b8c93433b88085ce74de51efe"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "CommitInfo(commit_url='https://huggingface.co/prithivMLmods/Age_Classification_SigLIP2/commit/f270c2f7ae2e87f84095dcc4d726903e3d1ab28c', commit_message='Upload folder using huggingface_hub', commit_description='', oid='f270c2f7ae2e87f84095dcc4d726903e3d1ab28c', pr_url=None, repo_url=RepoUrl('https://huggingface.co/prithivMLmods/Age_Classification_SigLIP2', endpoint='https://huggingface.co', repo_type='model', repo_id='prithivMLmods/Age_Classification_SigLIP2'), pr_revision=None, pr_num=None)"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ],
+      "source": [
+        "api = HfApi()\n",
+        "repo_id = f\"prithivMLmods/Age-Classification-SigLIP2\"\n",
+        "\n",
+        "api.upload_folder(\n",
+        "    folder_path=\"siglip2-image-classification/\",\n",
+        "    path_in_repo=\".\",\n",
+        "    repo_id=repo_id,\n",
+        "    repo_type=\"model\",\n",
+        "    revision=\"main\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> **Quickstart Demo with Transformers🤗 & Gradio**\n",
+        "\n",
+        "You can run a live demo using **Gradio** and your fine-tuned SigLIP 2 model. The demo takes an input image and predicts the **age group** (e.g., Child, Teenager, Adult, etc.) based on the trained model."
+      ],
+      "metadata": {
+        "id": "6qq6fbETaVoc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%%capture\n",
+        "!pip install gradio"
+      ],
+      "metadata": {
+        "id": "sdNJAx2gaoOu"
+      },
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import gradio as gr\n",
+        "from transformers import AutoImageProcessor\n",
+        "from transformers import SiglipForImageClassification\n",
+        "from transformers.image_utils import load_image\n",
+        "from PIL import Image\n",
+        "import torch\n",
+        "\n",
+        "# Load model and processor\n",
+        "model_name = \"prithivMLmods/Age-Classification-SigLIP2\" # Alter your model path\n",
+        "model = SiglipForImageClassification.from_pretrained(model_name)\n",
+        "processor = AutoImageProcessor.from_pretrained(model_name)\n",
+        "\n",
+        "def age_classification(image):\n",
+        "    \"\"\"Predicts the age group of a person from an image.\"\"\"\n",
+        "    image = Image.fromarray(image).convert(\"RGB\")\n",
+        "    inputs = processor(images=image, return_tensors=\"pt\")\n",
+        "\n",
+        "    with torch.no_grad():\n",
+        "        outputs = model(**inputs)\n",
+        "        logits = outputs.logits\n",
+        "        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()\n",
+        "\n",
+        "    #-------------------------------------------------------------------------#\n",
+        "    # Modify the label list to match your model's setup\n",
+        "    labels = {\n",
+        "        \"0\": \"Child 0-12\",\n",
+        "        \"1\": \"Teenager 13-20\",\n",
+        "        \"2\": \"Adult 21-44\",\n",
+        "        \"3\": \"Middle Age 45-64\",\n",
+        "        \"4\": \"Aged 65+\"\n",
+        "    }\n",
+        "    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}\n",
+        "\n",
+        "    return predictions\n",
+        "    #-------------------------------------------------------------------------#\n",
+        "\n",
+        "# Create Gradio interface\n",
+        "iface = gr.Interface(\n",
+        "    fn=age_classification,\n",
+        "    inputs=gr.Image(type=\"numpy\"),\n",
+        "    outputs=gr.Label(label=\"Prediction Scores\"),\n",
+        "    title=\"Age Group Classification\",\n",
+        "    description=\"Upload an image to predict the person's age group.\"\n",
+        ")\n",
+        "\n",
+        "# Launch the app\n",
+        "if __name__ == \"__main__\":\n",
+        "    iface.launch()"
+      ],
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+      "execution_count": 22,
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+        {
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+          "data": {
+            "text/plain": [
+              "config.json: 0.00B [00:00, ?B/s]"
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+          "data": {
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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "It looks like you are running Gradio on a hosted a Jupyter notebook. For the Gradio app to work, sharing must be enabled. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
+            "\n",
+            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
+            "* Running on public URL: https://529ec9e6bae2b3961a.gradio.live\n",
+            "\n",
+            "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<div><iframe src=\"https://529ec9e6bae2b3961a.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Demo Inference Screenshots**\n",
+        "\n",
+        "The demo inference screenshots are provided below for your reference.  \n",
+        "These examples showcase the model’s predictions on sample inputs after fine-tuning.\n",
+        "\n",
+        "> 🧪 Feel free to try your own examples in the Colab notebook!\n",
+        "\n",
+        "| ![Screenshot 1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ccdvZ-blbYtHxHAMSZbtz.png) | ![Screenshot 2](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Yh8GQOM-saLCu_cUTh3Zj.png) |\n",
+        "|:--:|:--:|\n",
+        "| ![Screenshot 3](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/9RwNBosF52g--LiaI0eqr.png) | ![Screenshot 4](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/VTkOBtHdNposCJnEZVjjx.png) |\n"
+      ],
+      "metadata": {
+        "id": "VsDymOmQg3Xa"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "This notebook provides a complete pipeline to fine-tune **SigLIP 2** for single-label image classification tasks.  \n",
+        "By leveraging advanced training techniques like **captioning-based pretraining**, **self-distillation**, and **masked prediction**, you can boost performance on a wide range of datasets. Whether you're working with multilingual labels, imbalanced classes, or custom naming schemes, this setup is flexible and ready for experimentation.\n",
+        "\n",
+        "    You can find the fine-tuned model here: https://huggingface.co/prithivMLmods/Age-Classification-SigLIP2\n",
+        "\n",
+        "Ready to adapt for your own datasets and downstream tasks!"
+      ],
+      "metadata": {
+        "id": "FWsjtCcGg26I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**🎉 Happy Fine-Tuning!**\n",
+        "\n",
+        "> **Made with 🤗 x ❤️ by [Prithiv Sakthi](https://www.linkedin.com/in/prithiv-sakthi/)**\n",
+        "\n",
+        "Feel free to fork, modify, and explore!"
+      ],
+      "metadata": {
+        "id": "ukvIhV4ZZkrM"
+      }
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
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+      "provenance": []
+    },
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