train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning: ViT on CIFAR-10\n",
    "\n",
    "Fine-tune `google/vit-base-patch16-224` on a CIFAR-10 subset.\n",
    "We freeze all layers except the classification head and train for 4 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from datasets import load_dataset\n",
    "from transformers import AutoImageProcessor, ViTForImageClassification, Trainer, TrainingArguments\n",
    "import evaluate\n",
    "from huggingface_hub import notebook_login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "notebook_login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load CIFAR-10 Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['img', 'label'],\n",
       "        num_rows: 8000\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['img', 'label'],\n",
       "        num_rows: 2000\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = load_dataset(\"uoft-cs/cifar10\")\n",
    "\n",
    "# Use a small subset for faster training\n",
    "dataset['train'] = dataset['train'].shuffle(seed=42).select(range(8000))\n",
    "dataset['test'] = dataset['test'].shuffle(seed=42).select(range(2000))\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 classes: ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n"
     ]
    }
   ],
   "source": [
    "labels = dataset['train'].features['label'].names\n",
    "print(f\"{len(labels)} classes: {labels}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1200 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_samples(ds, rows, cols):\n",
    "    samples = ds.shuffle(seed=42).select(range(rows * cols))\n",
    "    label_names = ds.features['label'].names\n",
    "    fig = plt.figure(figsize=(cols * 4, rows * 4))\n",
    "    for i in range(rows * cols):\n",
    "        img = samples[i]['img']\n",
    "        label = samples[i]['label']\n",
    "        fig.add_subplot(rows, cols, i + 1)\n",
    "        plt.imshow(img)\n",
    "        plt.title(label_names[label])\n",
    "        plt.axis('off')\n",
    "    plt.show()\n",
    "\n",
    "show_samples(dataset['train'], rows=3, cols=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "label2id = {label: idx for idx, label in enumerate(labels)}\n",
    "id2label = {idx: label for idx, label in enumerate(labels)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ViTImageProcessor {\n",
       "  \"do_normalize\": true,\n",
       "  \"do_rescale\": true,\n",
       "  \"do_resize\": true,\n",
       "  \"image_mean\": [\n",
       "    0.5,\n",
       "    0.5,\n",
       "    0.5\n",
       "  ],\n",
       "  \"image_processor_type\": \"ViTImageProcessor\",\n",
       "  \"image_std\": [\n",
       "    0.5,\n",
       "    0.5,\n",
       "    0.5\n",
       "  ],\n",
       "  \"resample\": 2,\n",
       "  \"rescale_factor\": 0.00392156862745098,\n",
       "  \"size\": {\n",
       "    \"height\": 224,\n",
       "    \"width\": 224\n",
       "  }\n",
       "}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor = AutoImageProcessor.from_pretrained('google/vit-base-patch16-224')\n",
    "processor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transforms(batch):\n",
    "    images = [img.convert('RGB') for img in batch['img']]\n",
    "    inputs = processor(images, return_tensors='pt')\n",
    "    inputs['labels'] = batch['label']\n",
    "    return inputs\n",
    "\n",
    "processed_dataset = dataset.with_transform(transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def collate_fn(batch):\n",
    "    return {\n",
    "        'pixel_values': torch.stack([x['pixel_values'] for x in batch]),\n",
    "        'labels': torch.tensor([x['labels'] for x in batch])\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = evaluate.load('accuracy')\n",
    "\n",
    "def compute_metrics(eval_preds):\n",
    "    logits, labels = eval_preds\n",
    "    predictions = np.argmax(logits, axis=1)\n",
    "    return accuracy.compute(predictions=predictions, references=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Model (Transfer Learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You passed `num_labels=10` which is incompatible to the `id2label` map of length `1000`.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3de4ca273bc4c43a9bd6413d50c7b3f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading weights:   0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mViTForImageClassification LOAD REPORT\u001b[0m from: google/vit-base-patch16-224\n",
      "Key               | Status   |                                                                                           \n",
      "------------------+----------+-------------------------------------------------------------------------------------------\n",
      "classifier.weight | MISMATCH | Reinit due to size mismatch - ckpt: torch.Size([1000, 768]) vs model:torch.Size([10, 768])\n",
      "classifier.bias   | MISMATCH | Reinit due to size mismatch - ckpt: torch.Size([1000]) vs model:torch.Size([10])          \n",
      "\n",
      "Notes:\n",
      "- MISMATCH:\tckpt weights were loaded, but they did not match the original empty weight shapes.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_params = 85,806,346 | trainable_params = 7,690\n"
     ]
    }
   ],
   "source": [
    "model = ViTForImageClassification.from_pretrained(\n",
    "    'google/vit-base-patch16-224',\n",
    "    num_labels=len(labels),\n",
    "    id2label=id2label,\n",
    "    label2id=label2id,\n",
    "    ignore_mismatched_sizes=True\n",
    ")\n",
    "\n",
    "# Freeze all layers except the classifier head\n",
    "for name, p in model.named_parameters():\n",
    "    if not name.startswith('classifier'):\n",
    "        p.requires_grad = False\n",
    "\n",
    "num_params = sum(p.numel() for p in model.parameters())\n",
    "trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "print(f\"{num_params = :,} | {trainable_params = :,}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.\n"
     ]
    }
   ],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir=\"./cifar10-vit\",\n",
    "    per_device_train_batch_size=32,\n",
    "    save_strategy=\"epoch\",\n",
    "    eval_strategy=\"epoch\",\n",
    "    logging_steps=50,\n",
    "    num_train_epochs=4,\n",
    "    learning_rate=2e-4,\n",
    "    warmup_ratio=0.1,\n",
    "    weight_decay=0.01,\n",
    "    save_total_limit=2,\n",
    "    remove_unused_columns=False,\n",
    "    push_to_hub=False,\n",
    "    report_to='none',\n",
    "    load_best_model_at_end=True,\n",
    "    hub_model_id=\"adisaljusi/cifar10-vit\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adisaljusi/repos/computer_vision_classification_model_comparison/.venv/lib/python3.13/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
      "  super().__init__(loader)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1000' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1000/1000 33:26, Epoch 4/4]\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>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.231645</td>\n",
       "      <td>0.216139</td>\n",
       "      <td>0.949500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.155080</td>\n",
       "      <td>0.151578</td>\n",
       "      <td>0.956500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.122988</td>\n",
       "      <td>0.138988</td>\n",
       "      <td>0.958000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.109668</td>\n",
       "      <td>0.136265</td>\n",
       "      <td>0.959500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a39a3b5a4a0463aa3ca0db5ca627dcd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adisaljusi/repos/computer_vision_classification_model_comparison/.venv/lib/python3.13/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
      "  super().__init__(loader)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af0dd23d8c074c9d940d187194f0b057",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adisaljusi/repos/computer_vision_classification_model_comparison/.venv/lib/python3.13/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
      "  super().__init__(loader)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2fa5bf7f3e624b979d8a90abfa659f26",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adisaljusi/repos/computer_vision_classification_model_comparison/.venv/lib/python3.13/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
      "  super().__init__(loader)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1659298b9bb453f829b9f7630d8e201",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1000, training_loss=0.34358246326446534, metrics={'train_runtime': 2007.7865, 'train_samples_per_second': 15.938, 'train_steps_per_second': 0.498, 'total_flos': 2.479921468932096e+18, 'train_loss': 0.34358246326446534, 'epoch': 4.0})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    data_collator=collate_fn,\n",
    "    compute_metrics=compute_metrics,\n",
    "    train_dataset=processed_dataset['train'],\n",
    "    eval_dataset=processed_dataset['test'],\n",
    "    processing_class=processor,\n",
    ")\n",
    "\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_history = trainer.state.log_history\n",
    "\n",
    "train_steps = [x['step'] for x in log_history if 'loss' in x]\n",
    "train_loss = [x['loss'] for x in log_history if 'loss' in x]\n",
    "eval_steps = [x['step'] for x in log_history if 'eval_loss' in x]\n",
    "eval_loss = [x['eval_loss'] for x in log_history if 'eval_loss' in x]\n",
    "eval_acc = [x['eval_accuracy'] for x in log_history if 'eval_accuracy' in x]\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "ax1.plot(train_steps, train_loss, label='Train Loss')\n",
    "ax1.plot(eval_steps, eval_loss, marker='o', label='Eval Loss')\n",
    "ax1.set_xlabel('Step')\n",
    "ax1.set_ylabel('Loss')\n",
    "ax1.set_title('Training & Eval Loss')\n",
    "ax1.legend()\n",
    "ax1.grid(True)\n",
    "\n",
    "ax2.plot(eval_steps, eval_acc, marker='o', color='green')\n",
    "ax2.set_xlabel('Step')\n",
    "ax2.set_ylabel('Accuracy')\n",
    "ax2.set_title('Eval Accuracy')\n",
    "ax2.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adisaljusi/repos/computer_vision_classification_model_comparison/.venv/lib/python3.13/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
      "  super().__init__(loader)\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1200 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_predictions(rows, cols):\n",
    "    samples = dataset['test'].shuffle(seed=42).select(range(rows * cols))\n",
    "    processed_samples = samples.with_transform(transforms)\n",
    "    predictions = trainer.predict(processed_samples).predictions.argmax(axis=1)\n",
    "    fig = plt.figure(figsize=(cols * 4, rows * 4))\n",
    "    for i in range(rows * cols):\n",
    "        img = samples[i]['img']\n",
    "        prediction = predictions[i]\n",
    "        label = f\"label: {id2label[samples[i]['label']]}\\npredicted: {id2label[prediction]}\"\n",
    "        fig.add_subplot(rows, cols, i + 1)\n",
    "        plt.imshow(img)\n",
    "        plt.title(label)\n",
    "        plt.axis('off')\n",
    "\n",
    "show_predictions(rows=3, cols=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save & Push to Hugging Face Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "975d3f122a654c06a749ea34c3faf104",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['./cifar10-vit/preprocessor_config.json']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dir = \"./cifar10-vit\"\n",
    "trainer.save_model(model_dir)\n",
    "processor.save_pretrained(model_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3e1e036dd234470fb69dd00f8c013b61",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c498d046fbcb464bb81297e3101ddcc1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing Files (0 / 0): |          |  0.00B /  0.00B            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "520b7241e1244a6693d0fecc8932c0b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "New Data Upload: |          |  0.00B /  0.00B            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/adisaljusi/cifar10-vit/commit/5ecbece28abb1c71e69f1b42dfe16e7ac20424d7', commit_message='Upload ViT fine-tuned on CIFAR-10', commit_description='', oid='5ecbece28abb1c71e69f1b42dfe16e7ac20424d7', pr_url=None, repo_url=RepoUrl('https://huggingface.co/adisaljusi/cifar10-vit', endpoint='https://huggingface.co', repo_type='model', repo_id='adisaljusi/cifar10-vit'), pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.push_to_hub(commit_message=\"Upload ViT fine-tuned on CIFAR-10\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "computer_vision_classification_model_comparison (3.13.2)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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