diff --git "a/Finetune-SigLIP2-Image-Classification/1.SigLIP2_Finetune_ImageClassification_TrainTest_Splits.ipynb" "b/Finetune-SigLIP2-Image-Classification/1.SigLIP2_Finetune_ImageClassification_TrainTest_Splits.ipynb"
new file mode 100644--- /dev/null
+++ "b/Finetune-SigLIP2-Image-Classification/1.SigLIP2_Finetune_ImageClassification_TrainTest_Splits.ipynb"
@@ -0,0 +1,7252 @@
+{
+  "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 1: Dataset with Train & Test Splits**\n",
+        "\n",
+        "In this configuration, the dataset is already organized into separate training and testing splits. This setup is ideal for straightforward supervised learning workflows.\n",
+        "\n",
+        "    Training Phase:\n",
+        "    The model is fine-tuned exclusively on the train split, where each image is paired with its corresponding class label.\n",
+        "\n",
+        "    Evaluation Phase:\n",
+        "    After training, the model's performance is assessed on the test split to measure generalization accuracy.\n",
+        "\n",
+        "✅ This approach is best suited for standard image classification tasks where the goal is to learn a discriminative mapping from images to labels using clearly separated train/test data."
+      ],
+      "metadata": {
+        "id": "35lDGCTgmf0R"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "edrxauqsOe4u"
+      },
+      "source": [
+        "**1. Install the packages**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "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 finetuning process, we will use the MNIST dataset of handwritten digit images for classification.\n",
+        "    You can find the MNIST dataset here: https://huggingface.co/datasets/ylecun/mnist"
+      ],
+      "metadata": {
+        "id": "V7RGZZv5vCbS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "# Load the dataset\n",
+        "dataset = load_dataset(\"ylecun/mnist\")\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": 281,
+          "referenced_widgets": [
+            "b6993634bca74dbfb6a5735d2f3e843e",
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+          ]
+        },
+        "id": "MwOTRQvivGrj",
+        "outputId": "1dcafb5b-393d-4791-d8a5-2823b7ee268c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:86: UserWarning: \n",
+            "Access to the secret `HF_TOKEN` has not been granted on this notebook.\n",
+            "You will not be requested again.\n",
+            "Please restart the session if you want to be prompted again.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "README.md: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b6993634bca74dbfb6a5735d2f3e843e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "train-00000-of-00001.parquet:   0%|          | 0.00/15.6M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "aaf1f76579154d84aa02b123c8c75b13"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "test-00000-of-00001.parquet:   0%|          | 0.00/2.60M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "06e85fd6af6d4f3cafe2d62fcda2085f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split:   0%|          | 0/60000 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8b6932e7cade4d9a84895ff0802a3699"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split:   0%|          | 0/10000 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "bb7b11f55bc44642a221056f84ad7a2a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'0': '0', '1': '1', '2': '2', '3': '3', '4': '4', '5': '5', '6': '6', '7': '7', '8': '8', '9': '9'}\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": null,
+      "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(\"ylecun/mnist\", split=\"train\")\n",
+        "\n",
+        "from pathlib import Path\n",
+        "\n",
+        "file_names = []\n",
+        "labels = []\n",
+        "\n",
+        "for example in dataset:\n",
+        "    file_path = str(example['image'])\n",
+        "    label = example['label']\n",
+        "\n",
+        "    file_names.append(file_path)\n",
+        "    labels.append(label)\n",
+        "\n",
+        "print(len(file_names), len(labels))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "r6YHpfeMzemH",
+        "outputId": "4e57f5a1-d1fa-4808-dfa3-c9fe43b58c2c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "60000 60000\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",
+        "y = df[['label']]\n",
+        "df = df.drop(['label'], axis=1)\n",
+        "ros = RandomOverSampler(random_state=83)\n",
+        "df, y_resampled = ros.fit_resample(df, y)\n",
+        "del y\n",
+        "df['label'] = y_resampled\n",
+        "del y_resampled\n",
+        "gc.collect()\n",
+        "\n",
+        "labels_subset = labels[:5]\n",
+        "print(labels_subset)\n",
+        "\n",
+        "#labels_list = ['example_label_0', 'example_label_1'................,'example_label_n-1']\n",
+        "labels_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
+        "\n",
+        "label2id, id2label = {}, {}\n",
+        "for i, label in enumerate(labels_list):\n",
+        "    label2id[label] = i\n",
+        "    id2label[i] = label\n",
+        "\n",
+        "ClassLabels = ClassLabel(num_classes=len(labels_list), names=labels_list)\n",
+        "\n",
+        "print(\"Mapping of IDs to Labels:\", id2label, '\\n')\n",
+        "print(\"Mapping of Labels to IDs:\", label2id)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zH_c7k7H1-29",
+        "outputId": "831c916a-a422-4f97-8719-b4fa05eabfb1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(60000, 2)\n",
+            "[5, 0, 4, 1, 9]\n",
+            "Mapping of IDs to Labels: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9'} \n",
+            "\n",
+            "Mapping of Labels to IDs: {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**5. Mapping and Casting Labels**"
+      ],
+      "metadata": {
+        "id": "rXvzq4AY15XF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def map_label2id(example):\n",
+        "    example['label'] = ClassLabels.str2int(example['label'])\n",
+        "    return example"
+      ],
+      "metadata": {
+        "id": "JIIabbmJ0TiX"
+      },
+      "execution_count": null,
+      "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",
+        "dataset = dataset.train_test_split(test_size=0.4, shuffle=True, stratify_by_column=\"label\")\n",
+        "\n",
+        "train_data = dataset['train']\n",
+        "test_data = dataset['test']"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
+          "referenced_widgets": [
+            "653c322b3f884f3395cb2976f482d881",
+            "751b28d49f18409d9f2836ff24baf745",
+            "2b808aa84cb24c74ab015a1ffd5e364e",
+            "25d5163117774c14bb9e690c66ab295d",
+            "71b79f41f8854ea68ab98e3b004d9dee",
+            "a560dcf959494963823a4790769a98e4",
+            "e3169d55ad7a45d8bc21af5e1539cfe6",
+            "f9e8d8163ac840028eabb54fed98e60d",
+            "b8424cbbabf24d84aa026a660a9301e6",
+            "ccc4cb358ec241deae53a786525d645c",
+            "0da5738e624c4b3493945deea616c0f6",
+            "1bddb72501014a258118d62f4c4600cc",
+            "72e297020ab8471db16c83a01f9c9d68",
+            "a068e3ba345f431e8e9292af5497796e",
+            "d072a3f4103944edb8cd2c0a769d85d6",
+            "74678d351b654c79b0a24b564049860f",
+            "4cc93c9c1bef4111b5708621cd15572a",
+            "d0ea68da9978436b945e1d0805fde71a",
+            "2cb8fd1b668a4e66b54cf7790836065d",
+            "9705374958d4456c8ec212e7be8a3b78",
+            "eb6e78105ccf4bed972b555cf782db89",
+            "b53d472a59284909b6b6ef6ae8b3784f"
+          ]
+        },
+        "id": "AdBEyEI71ldo",
+        "outputId": "ce7be48b-3e4f-438c-9fb8-f683fa1ff799"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/60000 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "653c322b3f884f3395cb2976f482d881"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Casting the dataset:   0%|          | 0/60000 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1bddb72501014a258118d62f4c4600cc"
+            }
+          },
+          "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": [
+            "781e66a8c60640dbb2f137671f6d9782",
+            "4fc2f0531fe5455487bc3c835abcb6aa",
+            "8c16e395a7c647679152db85f70efd18",
+            "45433c07d27644e8ade4de76ca27c76f",
+            "5f0f70930aaf48ef928b2f293b800696",
+            "c73c03fb6e0e4de8a0012040174ef434",
+            "e810f60406aa49458394ce638cdac572",
+            "69408f6bbde947ff8faf321ccfae876b",
+            "c8cdde83db4f4898a9baff0c7c15d096",
+            "095202fdeea0405da60a479bf9802a41",
+            "ead3a7732ccd429ba7ce5349d28c968e"
+          ]
+        },
+        "id": "pcTtRIrJ3L4N",
+        "outputId": "709a8f36-20ea-4e1e-9111-c0dc74311a30"
+      },
+      "execution_count": null,
+      "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": "781e66a8c60640dbb2f137671f6d9782"
+            }
+          },
+          "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 transformations\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",
+        "\n",
+        "# Define validation transformations\n",
+        "_val_transforms = Compose([\n",
+        "    Resize((size, size)),\n",
+        "    ToTensor(),\n",
+        "    Normalize(mean=image_mean, std=image_std)\n",
+        "])"
+      ],
+      "metadata": {
+        "id": "IIHobuid30G-"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**9. Applying Transformations to the Dataset**"
+      ],
+      "metadata": {
+        "id": "xt6kH0QW3-xf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Apply transformations to dataset\n",
+        "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",
+        "# Assuming train_data and test_data are loaded datasets\n",
+        "train_data.set_transform(train_transforms)\n",
+        "test_data.set_transform(val_transforms)"
+      ],
+      "metadata": {
+        "id": "qPiM8jas4FXm"
+      },
+      "execution_count": null,
+      "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": null,
+      "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": [
+            "e21c9e61664f4441aee0ccffe32a6f7a",
+            "b4408297fb23452b8aaa2fb4cfb37175",
+            "44ade3f1ffe046ccbe42b2fc9742ff92",
+            "893fbb49486d49878151f77e8899a7b4",
+            "c23774c9f388464992fae9e265719397",
+            "38cfb2a1b22e4cf784312e078dd2374a",
+            "c290b492226d48f182d1382f19ab77e9",
+            "c70773968ee84081a1171d8795a5ef71",
+            "a302f9dd635f4764ae98ccb9e74e57eb",
+            "4c8dce5202174a4383c2adffa1ac356c",
+            "2c593c2a2b524353904acea1f3d64444",
+            "12293eafff354937aa749417761f85dc",
+            "7d649ec6125546c1961a2b9c004591b9",
+            "3773dba9c27647e8a593c4111afc2a03",
+            "364e1ed61e524ea6865db3315fc1821d",
+            "ec8df785557147feb56e35bf1ef08a09",
+            "ec0cf454d89d418f9f9d884345864549",
+            "a5959e94adc74828baf53a333604fabd",
+            "5face235053e443a8cf9981a65177ef3",
+            "f289ed2763fb4bf8a43cd7f48735affd",
+            "af6f164cfd72459b9cc3c0483e28d5d3",
+            "66eb6593957746eb8f8ed3691b3b2677"
+          ]
+        },
+        "id": "TAJBpBcO4feE",
+        "outputId": "9490b7bb-1b33-4cfc-98d3-49595b3e0ccd"
+      },
+      "execution_count": null,
+      "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": "e21c9e61664f4441aee0ccffe32a6f7a"
+            }
+          },
+          "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": "12293eafff354937aa749417761f85dc"
+            }
+          },
+          "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": [
+            "92.891914\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**12. Defining Metrics and the Compute Function**"
+      ],
+      "metadata": {
+        "id": "_OZV3GId4kv2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "accuracy = evaluate.load(\"accuracy\")\n",
+        "\n",
+        "def compute_metrics(eval_pred):\n",
+        "    predictions = eval_pred.predictions\n",
+        "    label_ids = eval_pred.label_ids\n",
+        "\n",
+        "    predicted_labels = predictions.argmax(axis=1)\n",
+        "    acc_score = accuracy.compute(predictions=predicted_labels, references=label_ids)['accuracy']\n",
+        "\n",
+        "    return {\n",
+        "        \"accuracy\": acc_score\n",
+        "    }"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "efe333dda61d4034a330e62804472694",
+            "8ca4718d0e5446999c56400480aa5470",
+            "14ae4bbd1acf4665b02ea952a0b68629",
+            "03e1903d269a42e58fe8d701e3abaaad",
+            "86a044c358104ef18dcd54253d48287d",
+            "79c5c6eaec14454793b718ffcdef7038",
+            "080a6b82ca924c02ac59da47fb24372f",
+            "7de62f1bba6f429cbc1304f41d6f1a72",
+            "f3426b0809f34b1da658b9eacd4d5175",
+            "a450025c877b42a79cd6e2141d154602",
+            "127d6d74d9274d49ae7d599d4b1e44fa"
+          ]
+        },
+        "id": "2ZMQzLWl4ijD",
+        "outputId": "b6288dbb-f390-4ef5-f0fc-fd7348c4aa34"
+      },
+      "execution_count": null,
+      "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": "efe333dda61d4034a330e62804472694"
+            }
+          },
+          "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-4,\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=4,\n",
+        "    report_to=\"none\"\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "l2u4d3dg6PzK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**14. Initializing the Trainer**"
+      ],
+      "metadata": {
+        "id": "ASw82izY7w-s"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainer = Trainer(\n",
+        "    model,\n",
+        "    args,\n",
+        "    train_dataset=train_data,\n",
+        "    eval_dataset=test_data,\n",
+        "    data_collator=collate_fn,\n",
+        "    compute_metrics=compute_metrics,\n",
+        "    tokenizer=processor,\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "Hh9OOpq67wkh"
+      },
+      "execution_count": null,
+      "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(test_data)\n",
+        "print(outputs.metrics)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 197
+        },
+        "id": "FSK69Nd_7-RJ",
+        "outputId": "c063fe1f-9a70-4ea8-92c3-ba6497e289be"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='6000' max='3000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [3000/3000 32:42]\n",
+              "    </div>\n",
+              "    "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='2250' max='2250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [2250/2250 56:09, 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>0.206300</td>\n",
+              "      <td>0.123116</td>\n",
+              "      <td>0.002500</td>\n",
+              "      <td>0.969292</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>2</td>\n",
+              "      <td>0.082500</td>\n",
+              "      <td>0.046990</td>\n",
+              "      <td>0.002500</td>\n",
+              "      <td>0.988250</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': 0.04699030518531799, 'test_model_preparation_time': 0.0025, 'test_accuracy': 0.98825, 'test_runtime': 306.2919, 'test_samples_per_second': 78.357, 'test_steps_per_second': 9.795}\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",
+        "def plot_confusion_matrix(cm, classes, title='Confusion Matrix', cmap=plt.cm.Reds, figsize=(10, 8)):\n",
+        "\n",
+        "    plt.figure(figsize=figsize)\n",
+        "\n",
+        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
+        "    plt.title(title)\n",
+        "    plt.colorbar()\n",
+        "\n",
+        "    tick_marks = np.arange(len(classes))\n",
+        "    plt.xticks(tick_marks, classes, rotation=90)\n",
+        "    plt.yticks(tick_marks, classes)\n",
+        "\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), horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+        "\n",
+        "    plt.ylabel('True label')\n",
+        "    plt.xlabel('Predicted label')\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "accuracy = accuracy_score(y_true, y_pred)\n",
+        "f1 = f1_score(y_true, y_pred, average='macro')\n",
+        "\n",
+        "print(f\"Accuracy: {accuracy:.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()\n",
+        "print(\"Classification report:\")\n",
+        "print()\n",
+        "print(classification_report(y_true, y_pred, target_names=labels_list, digits=4))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 989
+        },
+        "id": "nypmCSIP9Cw6",
+        "outputId": "a2d39cbe-3171-4980-dd5a-0a096ed434ba"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy: 0.9882\n",
+            "F1 Score: 0.9881\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",
+            "\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0     0.9975    0.9928    0.9951      2369\n",
+            "           1     0.9952    0.9930    0.9941      2697\n",
+            "           2     0.9962    0.9786    0.9873      2383\n",
+            "           3     0.9931    0.9906    0.9918      2453\n",
+            "           4     0.9833    0.9855    0.9844      2337\n",
+            "           5     0.9825    0.9825    0.9825      2168\n",
+            "           6     0.9907    0.9903    0.9905      2367\n",
+            "           7     0.9780    0.9932    0.9855      2506\n",
+            "           8     0.9777    0.9910    0.9843      2340\n",
+            "           9     0.9878    0.9836    0.9857      2380\n",
+            "\n",
+            "    accuracy                         0.9882     24000\n",
+            "   macro avg     0.9882    0.9881    0.9881     24000\n",
+            "weighted avg     0.9883    0.9882    0.9883     24000\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": null,
+      "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": [
+            "b45fde5e86e943e1b8d0a23402028901",
+            "ef333ad338db494191caed60b831e680",
+            "2893ef504e014efc96733b8ac5088a98",
+            "36c9b7e3a0164b6cae0cb241b216d613",
+            "28813fef7f80468b8ad4f133304790ad",
+            "65b25d0efd4f4243a37b88cd95b4fb9a",
+            "5202ed4674f9443a89b792ab5e062138",
+            "c31dd94b555746919d64c78938860e10",
+            "054e44af48464f589f60f8c3f6ec33cb",
+            "71b27215425e4a3cb66a91cbfd4c612e",
+            "97b3014dd6094aefa75e1b4ffb753825",
+            "7c56f4131f7d45598e739d1749a93c11",
+            "44ced14414b340a9827691d6bc9373c9",
+            "2ee4954ca373421f9aac624cc5ea77e8",
+            "c46d91afae684a1a817be2f00321019f",
+            "14f60e22cc7f4c98823887f83543f664",
+            "e6b60c681b224750a41b9d8349619b1f",
+            "b6380ba0517c436c97d1cb28745cfd8d",
+            "f55e36246ec04ec5ab27ac2523c1dc09",
+            "3f539bcd81fe4e6b9ffff578b4ac95ff"
+          ]
+        },
+        "id": "sJfH3v0am_O4",
+        "outputId": "60c4e4ff-7b91-4d97-9410-d5aeee698383"
+      },
+      "execution_count": null,
+      "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": "b45fde5e86e943e1b8d0a23402028901"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 118,
+          "referenced_widgets": [
+            "6b83723741104513a0c1e4d22ee84429",
+            "9c5d6fbd9ca648f685b47528404f5883",
+            "2bc95d1d43c843e5aa2544c96d494b40",
+            "eac39288076845139119e09088dc4046",
+            "fbf0b9f098ec45aea6695ba73bc57c64",
+            "0d372462dbaa4f4db627a227914725aa",
+            "1d2343592792476fb14c7af5a5f828c7",
+            "671e02c850894471b258bba3f226eef3",
+            "0e38d16c9fdc48798127ad562bc32786",
+            "81f1cda2e7d74c1bb506984a2815cdf0",
+            "6cc8cfb0ad7546518ce493c30deea6a7"
+          ]
+        },
+        "id": "LQLQunHCmdF8",
+        "outputId": "939f9854-3cfa-455a-f070-e75570e95b24"
+      },
+      "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": "6b83723741104513a0c1e4d22ee84429"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "CommitInfo(commit_url='https://huggingface.co/prithivMLmods/mnist_siglip2/commit/5e3eb7bd47f6fe10c0637de53b2d46cf3dcf83ce', commit_message='Upload folder using huggingface_hub', commit_description='', oid='5e3eb7bd47f6fe10c0637de53b2d46cf3dcf83ce', pr_url=None, repo_url=RepoUrl('https://huggingface.co/prithivMLmods/mnist_siglip2', endpoint='https://huggingface.co', repo_type='model', repo_id='prithivMLmods/mnist_siglip2'), pr_revision=None, pr_num=None)"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ],
+      "source": [
+        "api = HfApi()\n",
+        "repo_id = f\"prithivMLmods/Mnist-Digits-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",
+        "This demo uses a fine-tuned **SigLIP 2** model to classify handwritten digits from the **MNIST** dataset. Powered by Transformers and Gradio, it provides an interactive interface for real-time predictions."
+      ],
+      "metadata": {
+        "id": "6qq6fbETaVoc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%%capture\n",
+        "!pip install gradio"
+      ],
+      "metadata": {
+        "id": "sdNJAx2gaoOu"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import gradio as gr\n",
+        "from transformers import AutoImageProcessor, 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/Mnist-Digits-SigLIP2\" # Alter your model path\n",
+        "model = SiglipForImageClassification.from_pretrained(model_name)\n",
+        "processor = AutoImageProcessor.from_pretrained(model_name)\n",
+        "\n",
+        "def classify_digit(image):\n",
+        "    \"\"\"Predicts the digit in the given handwritten digit 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\": \"0\", \"1\": \"1\", \"2\": \"2\", \"3\": \"3\", \"4\": \"4\",\n",
+        "        \"5\": \"5\", \"6\": \"6\", \"7\": \"7\", \"8\": \"8\", \"9\": \"9\"\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=classify_digit,\n",
+        "    inputs=gr.Image(type=\"numpy\"),\n",
+        "    outputs=gr.Label(label=\"Prediction Scores\"),\n",
+        "    title=\"MNIST Digit Classification 🔢\",\n",
+        "    description=\"Upload a handwritten digit image (0-9) to recognize it using MNIST-Digits-SigLIP2.\"\n",
+        ")\n",
+        "\n",
+        "# Launch the app\n",
+        "if __name__ == \"__main__\":\n",
+        "    iface.launch()"
+      ],
+      "metadata": {
+        "colab": {
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+        "outputId": "8a293f4b-2641-4f5c-cf4f-3edf6c382466"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json: 0.00B [00:00, ?B/s]"
+            ],
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+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/372M [00:00<?, ?B/s]"
+            ],
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+              "version_major": 2,
+              "version_minor": 0,
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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "preprocessor_config.json:   0%|          | 0.00/394 [00:00<?, ?B/s]"
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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://f4a078c41f01f49878.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://f4a078c41f01f49878.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/QWzvjpAr5NpggQFPQsmjw.png) | ![Screenshot 2](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ED1J2V4T51y-3vFcEiCK2.png) |\n",
+        "|:--:|:--:|\n",
+        "| ![Screenshot 3](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/YdCllznr25S3kVXwRTzOi.png) | ![Screenshot 4](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Zozc8LQoSBoqjGvyqBkwy.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/Mnist-Digits-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",
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