{"cells": [{"cell_type": "markdown", "id": "33f39229508719ea", "metadata": {"collapsed": false}, "source": ["# Deep Active Learning with Frozen Vision Transformer"]}, {"cell_type": "markdown", "id": "fd4eb6e7", "metadata": {}, "source": ["> **_Google Colab Note:_** If the notebook fails to run after installing the needed packages, try to restart the runtime (Ctrl + M) under Runtime -> Restart session.\n", "\n", "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/scikit-activeml/scikit-activeml.github.io/blob/gh-pages/1.0/generated/tutorials_colab//05_pool_al_with_self_supervised_learning.ipynb)"]}, {"cell_type": "markdown", "id": "79402ebb", "metadata": {}, "source": ["**Notebook Dependencies**\n", "\n", "Uncomment the following cells to install all dependencies for this tutorial."]}, {"cell_type": "code", "execution_count": 1, "id": "92efee18e6c2b8ce", "metadata": {}, "outputs": [], "source": ["# !pip install scikit-activeml[opt] torch torchvision tqdm datasets transformers"]}, {"cell_type": "markdown", "id": "a773fbde", "metadata": {}, "source": ["
"]}, {"cell_type": "markdown", "id": "fa4583e83ede8da9", "metadata": {"collapsed": false}, "source": ["This tutorial aims to demonstrate a practical comparison study using our\n", "```scikit-activeml``` library. The workflow involves utilizing a\n", "self-supervised learning model, specifically ```DINOv2``` from [1], to\n", "generate embeddings for the CIFAR-100 dataset [2]. Subsequently, various\n", "active learning strategies will be employed to intelligently select samples\n", "for labeling.\n", "\n", "**Key Steps:**\n", "1. **Self-Supervised Learning Model:** Utilize the DINOv2 model to create\n", "embedding dataset for CIFAR-100 dataset.\n", "\n", "2. **Active Learning Strategies:** Employ different active learning strategies provided by our library, including:\n", " - Random Sampling,\n", " - Uncertainty Sampling,\n", " - Discriminative Active Learning,\n", " - CoreSet,\n", " - TypiClust,\n", " - Badge,\n", " - ProbCover,\n", " - DropQuery,\n", " - Falcun.\n", "\n", "3. **Batch Sample Selection:** Use each active learning strategy to select a batch of samples for labeling.\n", "\n", "4. **Plotting the results:** By the end of this notebook, we'll compare the\n", "accuracy of the aforementioned active learning strategies.\n", "\n", "**References:**\n", "\n", "[1] Oquab, M., Darcet, T., Moutakanni, T., Vo, H. V., Szafraniec, M.,\n", "Khalidov, V., ... & Bojanowski, P. DINOv2: Learning Robust Visual Features\n", "without Supervision. Transactions on Machine Learning Research.\n", "\n", "[2] Krizhevsky, A., & Hinton, G. (2009). Learning Multiple Layers of\n", "Features from Tiny Images."]}, {"cell_type": "code", "execution_count": 2, "id": "3a4b6dbb9143a5eb", "metadata": {"ExecuteTime": {"end_time": "2025-11-08T18:55:09.099806Z", "start_time": "2025-11-08T18:55:04.875268Z"}, "collapsed": false}, "outputs": [], "source": ["# Comment in for speedup, if you have cuML installed.\n", "# %load_ext cuml.accel\n", "import numpy as np\n", "import matplotlib as mlp\n", "import matplotlib.pyplot as plt\n", "import torch\n", "import warnings\n", "\n", "from datasets import load_dataset\n", "from multiprocess import set_start_method\n", "from sklearn.linear_model import LogisticRegression\n", "from skactiveml.classifier import SklearnClassifier\n", "from skactiveml.pool import (\n", " UncertaintySampling,\n", " RandomSampling,\n", " DiscriminativeAL,\n", " CoreSet,\n", " TypiClust,\n", " Badge,\n", " DropQuery,\n", " ProbCover,\n", " Falcun,\n", " SubSamplingWrapper,\n", ")\n", "from skactiveml.utils import call_func\n", "from transformers import AutoImageProcessor, Dinov2Model\n", "from tqdm import tqdm\n", "from threadpoolctl import threadpool_limits\n", "\n", "\n", "set_start_method(\"spawn\", force=True)\n", "threadpool_limits(limits=1, user_api=\"blas\")\n", "warnings.filterwarnings(\"ignore\")\n", "mlp.rcParams[\"figure.facecolor\"] = \"white\"\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""]}, {"cell_type": "markdown", "id": "3ac32ea901e08c01", "metadata": {"collapsed": false}, "source": ["## Embed CIFAR-100 Images with DINOv2"]}, {"cell_type": "markdown", "id": "7a4e3bf805669b1c", "metadata": {"collapsed": false}, "source": ["In this step, we focus on preparing the datasets using the self-supervised\n", "learning model DINOv2. DINOv2, short for \"self-distillation with no labels\",\n", " is a popular vision foundation model that excels at providing meaningful\n", " representations for image data."]}, {"cell_type": "code", "execution_count": 3, "id": "3b27a5c395f5897e", "metadata": {"ExecuteTime": {"end_time": "2025-11-08T18:55:25.655039Z", "start_time": "2025-11-08T18:55:09.144763Z"}, "collapsed": false}, "outputs": [], "source": ["# Download data.\n", "ds = load_dataset(\"uoft-cs/cifar100\")\n", "\n", "# Download DINOv2 ViT/S-14 as embedding model.\n", "processor = AutoImageProcessor.from_pretrained(\n", " \"facebook/dinov2-small\", use_fast=True\n", ")\n", "model = Dinov2Model.from_pretrained(\"facebook/dinov2-small\").to(device).eval()\n", "\n", "\n", "# Embed CIFAR-100 images.\n", "def embed(batch, processor, device, model):\n", " import torch\n", "\n", " inputs = processor(images=batch[\"img\"], return_tensors=\"pt\").to(device)\n", " with torch.no_grad():\n", " out = model(**inputs).last_hidden_state[:, 0]\n", " return {\"emb\": out.cpu().numpy()}\n", "\n", "\n", "embed_kwargs = {\n", " \"processor\": processor,\n", " \"model\": model,\n", " \"device\": device,\n", "}\n", "ds = ds.map(\n", " embed,\n", " batched=True,\n", " batch_size=32,\n", " num_proc=2,\n", " fn_kwargs=embed_kwargs,\n", " remove_columns=[\"img\"],\n", ")\n", "X_pool = np.stack(ds[\"train\"][\"emb\"], dtype=np.float32)\n", "y_pool = np.array(ds[\"train\"][\"fine_label\"], dtype=np.int64)\n", "X_test = np.stack(ds[\"test\"][\"emb\"], dtype=np.float32)\n", "y_test = np.array(ds[\"test\"][\"fine_label\"], dtype=np.int64)"]}, {"cell_type": "markdown", "id": "cf4565232ae31432", "metadata": {"collapsed": false}, "source": ["## Random Seed Management"]}, {"cell_type": "markdown", "id": "b1ce38a2-0305-44eb-abf5-cb69c41c3f98", "metadata": {}, "source": ["To ensure experiment reproducibility, it's important to set random states for all components that might use them. For simplicity, we set a single fixed random state and use helper functions to generate new seeds and random states. It's important to note that the ```master_random_state``` should only be used to create new random states or random seeds."]}, {"cell_type": "code", "execution_count": 4, "id": "11e6ce648a9ab110", "metadata": {"ExecuteTime": {"end_time": "2025-11-08T18:55:25.703150Z", "start_time": "2025-11-08T18:55:25.701134Z"}, "collapsed": false}, "outputs": [], "source": ["master_random_state = np.random.RandomState(0)\n", "\n", "\n", "def gen_seed(random_state: np.random.RandomState):\n", " \"\"\"\n", " Generate a seed for a random number generator.\n", "\n", " Parameters:\n", " - random_state (np.random.RandomState): Random state object.\n", "\n", " Returns:\n", " - int: Generated seed.\n", " \"\"\"\n", " return random_state.randint(0, 2**31)\n", "\n", "\n", "def gen_random_state(random_state: np.random.RandomState):\n", " \"\"\"\n", " Generate a new random state object based on a given random state.\n", "\n", " Parameters:\n", " - random_state (np.random.RandomState): Random state object.\n", "\n", " Returns:\n", " - np.random.RandomState: New random state object.\n", " \"\"\"\n", " return np.random.RandomState(gen_seed(random_state))"]}, {"cell_type": "markdown", "id": "24cc0c5019b852b5", "metadata": {"collapsed": false}, "source": ["## Classification Models and Query Strategies"]}, {"cell_type": "markdown", "id": "91a3743e-cb9e-49a9-a239-30c9f54839fa", "metadata": {}, "source": ["The embeddings we have computed can be used as an input to a classification model. For this guide, we use `LogisticRegression` from `sklearn`. Moreover, we handle the creation of query strategies using factory functions to simplify the separation of query strategies across repetitions."]}, {"cell_type": "code", "execution_count": 5, "id": "d356c3752b0a58a1", "metadata": {"ExecuteTime": {"end_time": "2025-11-08T18:55:25.747222Z", "start_time": "2025-11-08T18:55:25.744228Z"}, "collapsed": false}, "outputs": [], "source": ["n_features, classes = X_pool.shape[1], np.unique(y_pool)\n", "missing_label = -1\n", "clf = SklearnClassifier(\n", " LogisticRegression(verbose=0, tol=1e-3, C=0.01, max_iter=10000),\n", " classes=classes,\n", " random_state=gen_seed(master_random_state),\n", " missing_label=-1,\n", ")\n", "\n", "\n", "def create_query_strategy(name, random_state):\n", " return query_strategy_factory_functions[name](random_state)\n", "\n", "\n", "query_strategy_factory_functions = {\n", " \"RandomSampling\": lambda random_state: RandomSampling(\n", " random_state=gen_seed(random_state), missing_label=missing_label\n", " ),\n", " \"UncertaintySampling\": lambda random_state: UncertaintySampling(\n", " random_state=gen_seed(random_state), missing_label=missing_label\n", " ),\n", " \"DiscriminativeAL\": lambda random_state: DiscriminativeAL(\n", " random_state=gen_seed(random_state), missing_label=missing_label\n", " ),\n", " \"CoreSet\": lambda random_state: CoreSet(\n", " random_state=gen_seed(random_state), missing_label=missing_label\n", " ),\n", " \"TypiClust\": lambda random_state: TypiClust(\n", " random_state=gen_seed(random_state), missing_label=missing_label\n", " ),\n", " \"Badge\": lambda random_state: Badge(\n", " random_state=gen_seed(random_state), missing_label=missing_label\n", " ),\n", " \"DropQuery\": lambda random_state: DropQuery(\n", " random_state=gen_seed(random_state),\n", " missing_label=missing_label,\n", " ),\n", " \"ProbCover\": lambda random_state: ProbCover(\n", " random_state=gen_seed(random_state), missing_label=missing_label\n", " ),\n", " \"Falcun\": lambda random_state: Falcun(\n", " random_state=gen_seed(random_state), missing_label=missing_label\n", " ),\n", "}"]}, {"cell_type": "markdown", "id": "9023c37048ec8f54", "metadata": {"collapsed": false}, "source": ["## Experiment Parameters\n", "\n", "For this experiment, we need to define how the strategies should be compared against one another. Here the number of repetitions (```n_reps```), the number of cycles (```n_cycles```), and the size of each query (```query_batch_size```) need to be defined. "]}, {"cell_type": "code", "execution_count": 6, "id": "e103670190e73a3f", "metadata": {"ExecuteTime": {"end_time": "2025-11-08T18:55:25.789678Z", "start_time": "2025-11-08T18:55:25.788190Z"}, "collapsed": false}, "outputs": [], "source": ["n_reps = 3\n", "n_cycles = 20\n", "query_batch_size = 100\n", "query_strategy_names = query_strategy_factory_functions.keys()"]}, {"cell_type": "markdown", "id": "66b8fff1b12211ed", "metadata": {"collapsed": false}, "source": ["## Experiment Loop\n", "\n", "The actual experiment loops over all query strategies. The accuracy for the test set is stored for each cycle and repetition in the `results` dictionary."]}, {"cell_type": "code", "execution_count": 7, "id": "718629bb-1c4f-4707-9eb1-9d1df901cd84", "metadata": {"ExecuteTime": {"end_time": "2025-11-08T19:13:43.735332Z", "start_time": "2025-11-08T18:55:25.831259Z"}}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Repeat 1 with RandomSampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:01<00:00, 18.78it/s]\n", "Repeat 2 with RandomSampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:00<00:00, 20.43it/s]\n", "Repeat 3 with RandomSampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:00<00:00, 20.37it/s]\n", "Repeat 1 with UncertaintySampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:01<00:00, 17.06it/s]\n", "Repeat 2 with UncertaintySampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:01<00:00, 17.08it/s]\n", "Repeat 3 with UncertaintySampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:01<00:00, 17.24it/s]\n", "Repeat 1 with DiscriminativeAL: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [01:18<00:00, 3.90s/it]\n", "Repeat 2 with DiscriminativeAL: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [01:18<00:00, 3.91s/it]\n", "Repeat 3 with DiscriminativeAL: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [01:18<00:00, 3.91s/it]\n", "Repeat 1 with CoreSet: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:05<00:00, 3.46it/s]\n", "Repeat 2 with CoreSet: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:05<00:00, 3.47it/s]\n", "Repeat 3 with CoreSet: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:05<00:00, 3.48it/s]\n", "Repeat 1 with TypiClust: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:09<00:00, 2.20it/s]\n", "Repeat 2 with TypiClust: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:09<00:00, 2.20it/s]\n", "Repeat 3 with TypiClust: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:11<00:00, 1.78it/s]\n", "Repeat 1 with Badge: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [04:01<00:00, 12.09s/it]\n", "Repeat 2 with Badge: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [04:01<00:00, 12.06s/it]\n", "Repeat 3 with Badge: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [04:01<00:00, 12.06s/it]\n", "Repeat 1 with DropQuery: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:04<00:00, 4.38it/s]\n", "Repeat 2 with DropQuery: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:04<00:00, 4.38it/s]\n", "Repeat 3 with DropQuery: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:04<00:00, 4.37it/s]\n", "Repeat 1 with ProbCover: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:30<00:00, 1.51s/it]\n", "Repeat 2 with ProbCover: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:30<00:00, 1.52s/it]\n", "Repeat 3 with ProbCover: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:30<00:00, 1.51s/it]\n", "Repeat 1 with Falcun: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:01<00:00, 10.88it/s]\n", "Repeat 2 with Falcun: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:01<00:00, 10.74it/s]\n", "Repeat 3 with Falcun: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:01<00:00, 10.83it/s]\n"]}], "source": ["results = {}\n", "for qs_name in query_strategy_names:\n", " accuracies = np.full((n_reps, n_cycles + 1), np.nan)\n", " for i_rep in range(n_reps):\n", " y_train = np.full(shape=len(X_pool), fill_value=missing_label)\n", "\n", " qs = create_query_strategy(\n", " qs_name,\n", " random_state=gen_random_state(np.random.RandomState(i_rep)),\n", " )\n", " qs = SubSamplingWrapper(\n", " query_strategy=qs,\n", " missing_label=missing_label,\n", " random_state=gen_random_state(np.random.RandomState(i_rep)),\n", " exclude_non_subsample=True,\n", " max_candidates=0.1,\n", " )\n", " clf.fit(X_pool, y_train)\n", " accuracies[i_rep, 0] = clf.score(X_test, y_test)\n", "\n", " for c in tqdm(\n", " range(1, n_cycles + 1), desc=f\"Repeat {i_rep + 1} with {qs_name}\"\n", " ):\n", " query_idx = call_func(\n", " qs.query,\n", " X=X_pool,\n", " y=y_train,\n", " batch_size=query_batch_size,\n", " clf=clf,\n", " fit_clf=False,\n", " discriminator=clf,\n", " update=True,\n", " )\n", " y_train[query_idx] = y_pool[query_idx]\n", " clf.fit(X_pool, y_train)\n", " accuracies[i_rep, c] = clf.score(X_test, y_test)\n", "\n", " results[qs_name] = accuracies"]}, {"cell_type": "markdown", "id": "998e52cb1a2107b", "metadata": {"collapsed": false}, "source": ["## Resulting Plotting\n", "\n", "We use learning curves to compare strategies. We visualize the average accuracy over all repetitions. In addition, the legend provides insight into the area under the learning curve, which indicates the average accuracy over all cycles."]}, {"cell_type": "code", "execution_count": 8, "id": "7ed6ebe52431f489", "metadata": {"ExecuteTime": {"end_time": "2025-11-08T19:13:43.920250Z", "start_time": "2025-11-08T19:13:43.782135Z"}, "collapsed": false}, "outputs": [{"data": {"image/png": 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", "text/plain": ["
"]}, "metadata": {}, "output_type": "display_data"}], "source": ["plt.figure(figsize=(16, 9))\n", "for qs_name in query_strategy_names:\n", " key = qs_name\n", " result = results[key]\n", " reshaped_result = result.reshape((-1, n_cycles + 1))\n", " errorbar_mean = np.mean(reshaped_result, axis=0)\n", " errorbar_std = np.std(reshaped_result, axis=0)\n", " plt.errorbar(\n", " np.arange(n_cycles + 1) * query_batch_size,\n", " errorbar_mean,\n", " errorbar_std,\n", " label=f\"{qs_name}: ALCU={np.mean(errorbar_mean):.3f}\",\n", " alpha=0.5,\n", " )\n", " plt.yticks(np.arange(0, 1.0, 0.1))\n", "plt.grid()\n", "plt.legend(loc=\"lower right\", fontsize=\"x-large\")\n", "plt.xlabel(\"# labeled samples\", fontsize=\"x-large\")\n", "plt.ylabel(\"accuracy\", fontsize=\"x-large\")\n", "plt.show()"]}], "metadata": {"kernelspec": {"display_name": "scikit-activeml", "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", "version": "3.12.11"}, "nbsphinx": {"orphan": true}}, "nbformat": 4, "nbformat_minor": 5}