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Colab](https://img.shields.io/badge/Open%20in%20Colab-blue?style=for-the-badge&logo=data:image/png;base64,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&labelColor=gray)](https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/analyzing-torchvision-datasets.ipynb)\n", "[![Kaggle](https://img.shields.io/badge/Open%20in%20Kaggle-blue?style=for-the-badge&logo=data:image/png;base64,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&labelColor=gray)](https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/analyzing-torchvision-datasets.ipynb)\n", "[![Explore the Docs](https://img.shields.io/badge/Explore%20the%20Docs-blue?style=for-the-badge&labelColor=gray&logo=read-the-docs)](https://visual-layer.readme.io/docs/analyzing-torchvision-datasets)\n", "\n", "This notebook shows how you can analyze [Torchvision Datasets](https://pytorch.org/vision/main/datasets.html) for issues using fastdup." ] }, { "cell_type": "markdown", "id": "55b99f27-269c-49d6-8f51-b2af6d2019bb", "metadata": {}, "source": [ "## Installation\n", "\n", "First, let's install the necessary packages." ] }, { "cell_type": "code", "execution_count": null, "id": "9b81d6ca-a91f-46c5-bc91-7bc3b36b01b5", "metadata": { "tags": [] }, "outputs": [], "source": [ "!pip install -Uq fastdup torchvision" ] }, { "cell_type": "markdown", "id": "e6722adf-0f74-4aae-8e67-76107456a91b", "metadata": {}, "source": [ "Now, test the installation. If there's no error message, we are ready to go." ] }, { "cell_type": "code", "execution_count": 1, "id": "efc6af00-4688-454d-b84b-05e15c95fb86", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "'1.43'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import fastdup\n", "fastdup.__version__" ] }, { "cell_type": "markdown", "id": "390f9cd9-08cf-42bb-947b-546f7a520e5b", "metadata": {}, "source": [ "## Download Dataset\n", "Torchvision provides many built-in datasets in the `torchvision.datasets` module. The datasets span across various tasks such as image classification, object detection, and segmentation to name a few.\n", "\n", "Let's download the [Caltech 256](https://data.caltech.edu/records/nyy15-4j048) dataset to our local directory.\n", "\n", "Caltech 256 dataset consists of 256 object categories containing a total of 30607 images for image classification." ] }, { "cell_type": "code", "execution_count": 2, "id": "958fba0b-9be7-40e5-8d3f-ad5829fc2883", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Files already downloaded and verified\n" ] } ], "source": [ "from torchvision.datasets import Caltech256\n", "caltech256 = Caltech256(root='./', download=True)" ] }, { "cell_type": "markdown", "id": "cf8168e5-611b-42bc-b4f2-1b271360fd53", "metadata": {}, "source": [ "The datasets is downloaded into the `caltech256` folder in the root directory." ] }, { "cell_type": "code", "execution_count": 3, "id": "dbc97b80-8213-4cd6-8ee6-d352344ef34d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'./caltech256'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "caltech256.root" ] }, { "cell_type": "markdown", "id": "7c99aa34", "metadata": {}, "source": [ "## Construct Annotation DataFrame\n", "Although you can run fasdup without the annotations, specifying the labels lets us do more analysis with fastdup such as inspecting mislabels.\n", "Since the dataset is labeled, let's make use of the labels and feed them into fastdup.\n", "\n", "fastdup expects the labels to be formatted into a Pandas `DataFrame` with the columns `filename` and `label`.\n", "Let's loop over the directory recursively search for the filenames and labels, and format them into a DataFrame." ] }, { "cell_type": "code", "execution_count": 4, "id": "01890591", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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filenamelabel
0caltech256/256_ObjectCategories/199.spoon/199_0092.jpg199.spoon
1caltech256/256_ObjectCategories/199.spoon/199_0006.jpg199.spoon
2caltech256/256_ObjectCategories/199.spoon/199_0063.jpg199.spoon
3caltech256/256_ObjectCategories/199.spoon/199_0073.jpg199.spoon
4caltech256/256_ObjectCategories/199.spoon/199_0021.jpg199.spoon
.........
30602caltech256/256_ObjectCategories/206.sushi/206_0074.jpg206.sushi
30603caltech256/256_ObjectCategories/206.sushi/206_0088.jpg206.sushi
30604caltech256/256_ObjectCategories/206.sushi/206_0043.jpg206.sushi
30605caltech256/256_ObjectCategories/206.sushi/206_0023.jpg206.sushi
30606caltech256/256_ObjectCategories/206.sushi/206_0025.jpg206.sushi
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30607 rows × 2 columns

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" ], "text/plain": [ " filename label\n", "0 caltech256/256_ObjectCategories/199.spoon/199_0092.jpg 199.spoon\n", "1 caltech256/256_ObjectCategories/199.spoon/199_0006.jpg 199.spoon\n", "2 caltech256/256_ObjectCategories/199.spoon/199_0063.jpg 199.spoon\n", "3 caltech256/256_ObjectCategories/199.spoon/199_0073.jpg 199.spoon\n", "4 caltech256/256_ObjectCategories/199.spoon/199_0021.jpg 199.spoon\n", "... ... ...\n", "30602 caltech256/256_ObjectCategories/206.sushi/206_0074.jpg 206.sushi\n", "30603 caltech256/256_ObjectCategories/206.sushi/206_0088.jpg 206.sushi\n", "30604 caltech256/256_ObjectCategories/206.sushi/206_0043.jpg 206.sushi\n", "30605 caltech256/256_ObjectCategories/206.sushi/206_0023.jpg 206.sushi\n", "30606 caltech256/256_ObjectCategories/206.sushi/206_0025.jpg 206.sushi\n", "\n", "[30607 rows x 2 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import glob\n", "import os\n", "import pandas as pd\n", "\n", "# Define the path\n", "path = \"caltech256/\"\n", "\n", "# Define patterns for tif image found in the dataset\n", "patterns = ['*jpg', '*jpeg']\n", "\n", "# Use glob to get all image filenames for both extensions\n", "filenames = [f for pattern in patterns for f in glob.glob(path + '**/' + pattern, recursive=True)]\n", "\n", "# Extract the parent folder name for each filename\n", "label = [os.path.basename(os.path.dirname(filename)) for filename in filenames]\n", "\n", "# Convert to a pandas DataFrame and add the title label column\n", "df = pd.DataFrame({\n", " 'filename': filenames,\n", " 'label': label\n", "})\n", "\n", "df" ] }, { "cell_type": "markdown", "id": "1766b743-51bc-4be6-8827-5777fa085a93", "metadata": {}, "source": [ "## Run fastdup\n", "One the dataset download completes, analyze the image folder where the dataset is stored.\n", "\n", "Point `input_dir` to the directory where the images are stored." ] }, { "cell_type": "code", "execution_count": 5, "id": "c3d775d7-4e2a-4574-b794-2f6180453781", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: fastdup create() without work_dir argument, output is stored in a folder named work_dir in your current working path.\n", "FastDup Software, (C) copyright 2022 Dr. Amir Alush and Dr. Danny Bickson.\n", "\n", "\n", " \n", " ad88 88 \n", " d8\" ,d 88 \n", " 88 88 88 \n", "MM88MMM ,adPPYYba, ,adPPYba, MM88MMM ,adPPYb,88 88 88 8b,dPPYba, \n", " 88 \"\" `Y8 I8[ \"\" 88 a8\" `Y88 88 88 88P' \"8a \n", " 88 ,adPPPPP88 `\"Y8ba, 88 8b 88 88 88 88 d8 \n", " 88 88, ,88 aa ]8I 88, \"8a, ,d88 \"8a, ,a88 88b, ,a8\" \n", " 88 `\"8bbdP\"Y8 `\"YbbdP\"' \"Y888 `\"8bbdP\"Y8 `\"YbbdP'Y8 88`YbbdP\"' \n", " 88 \n", " 88 \n", "\n", "\n", "2023-09-26 14:16:12 [INFO] Going to loop over dir /tmp/tmpe_pnff4j.csv\n", "2023-09-26 14:16:12 [INFO] Found total 30607 images to run on, 30607 train, 0 test, name list 30607, counter 30607 \n", "2023-09-26 14:17:14 [INFO] Found total 30607 images to run onimated: 0 Minutes\n", "Finished histogram 8.575\n", "Finished bucket sort 8.638\n", "2023-09-26 14:17:16 [INFO] 2184) Finished write_index() NN model\n", "2023-09-26 14:17:16 [INFO] Stored nn model index file work_dir/nnf.index\n", "2023-09-26 14:17:17 [INFO] Total time took 65661 ms\n", "2023-09-26 14:17:17 [INFO] Found a total of 180 fully identical images (d>0.990), which are 0.29 % of total graph edges\n", "2023-09-26 14:17:17 [INFO] Found a total of 86 nearly identical images(d>0.980), which are 0.14 % of total graph edges\n", "2023-09-26 14:17:17 [INFO] Found a total of 4480 above threshold images (d>0.900), which are 7.32 % of total graph edges\n", "2023-09-26 14:17:17 [INFO] Found a total of 3061 outlier images (d<0.050), which are 5.00 % of total graph edges\n", "2023-09-26 14:17:17 [INFO] Min distance found 0.457 max distance 1.000\n", "2023-09-26 14:17:17 [INFO] Running connected components for ccthreshold 0.960000 \n", ".0\n", " ########################################################################################\n", "\n", "Dataset Analysis Summary: \n", "\n", " Dataset contains 30607 images\n", " Valid images are 100.00% (30,607) of the data, invalid are 0.00% (0) of the data\n", " Similarity: 1.77% (543) belong to 9 similarity clusters (components).\n", " 98.23% (30,064) images do not belong to any similarity cluster.\n", " Largest cluster has 60 (0.20%) images.\n", " For a detailed analysis, use `.connected_components()`\n", "(similarity threshold used is 0.9, connected component threshold used is 0.96).\n", "\n", " Outliers: 6.48% (1,983) of images are possible outliers, and fall in the bottom 5.00% of similarity values.\n", " For a detailed list of outliers, use `.outliers()`.\n", "\n", "########################################################################################\n", "Would you like to see awesome visualizations for some of the most popular academic datasets?\n", "Click here to see and learn more: https://app.visual-layer.com/vl-datasets?utm_source=fastdup\n", "########################################################################################\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fd = fastdup.create(input_dir=\"caltech256\")\n", "fd.run(annotations=df)" ] }, { "cell_type": "markdown", "id": "3caa9e1a-5cb5-47d3-baa0-948d879b78b3", "metadata": {}, "source": [ "## View Galleries\n", "\n", "You can use all of fastdup gallery methods to view duplicates, clusters, etc.\n", "\n", "```python\n", "fd.vis.duplicates_gallery() # create a visual gallery of duplicates\n", "fd.vis.outliers_gallery() # create a visual gallery of anomalies\n", "fd.vis.component_gallery() # create a visualization of connected components\n", "fd.vis.stats_gallery() # create a visualization of images statistics (e.g. blur)\n", "fd.vis.similarity_gallery() # create a gallery of similar images\n", "```" ] }, { "cell_type": "markdown", "id": "3a23642e-4fe0-4962-a819-43547f12fb33", "metadata": {}, "source": [ "Lets view some of the image clusters in the dataset." ] }, { "cell_type": "code", "execution_count": 6, "id": "0dbea899-8560-4e0d-9c25-7ba882dd06e0", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "049.cormorant\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8b4033f8ebb64c42afc3cac5a44d2cdf", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating gallery: 0%| | 0/20 [00:00\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Components Report\n", " \n", " \n", "\n", "\n", "\n", "
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\n", " \n", " \"logo\"\n", " \n", "
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Components Report

Showing groups of similar images

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Info
component19826
num_images16
mean_distance0.9606
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Label
224.touring-bike13
146.mountain-bike3
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Info
component15180
num_images10
mean_distance0.9618
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Label
145.motorbikes-10110
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Info
component15058
num_images6
mean_distance0.9689
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Label
145.motorbikes-1016
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Info
component29096
num_images5
mean_distance0.9605
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Label
146.mountain-bike5
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Info
component20580
num_images5
mean_distance0.9662
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Label
137.mars5
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Info
component15100
num_images5
mean_distance0.9643
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Label
145.motorbikes-1015
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Info
component20587
num_images4
mean_distance0.9638
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Label
137.mars4
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Info
component20605
num_images4
mean_distance0.961
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Label
137.mars4
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Info
component1690
num_images4
mean_distance0.9609
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Label
253.faces-easy-1014
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Info
component15495
num_images3
mean_distance0.9653
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Label
145.motorbikes-1013
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Info
component15234
num_images3
mean_distance0.9607
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Label
145.motorbikes-1013
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Info
component22944
num_images3
mean_distance0.9684
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Label
021.breadmaker3
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Info
component15476
num_images3
mean_distance0.9649
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Label
145.motorbikes-1013
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Info
component20627
num_images3
mean_distance0.9777
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Label
137.mars3
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Info
component15511
num_images3
mean_distance0.9615
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Label
145.motorbikes-1013
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Info
component20582
num_images3
mean_distance0.9686
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Label
137.mars3
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Info
component18589
num_images3
mean_distance0.9651
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Label
214.teepee3
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Info
component18656
num_images3
mean_distance1.0
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Label
214.teepee3
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Info
component15202
num_images3
mean_distance0.9603
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Label
145.motorbikes-1013
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Info
component15322
num_images3
mean_distance0.9636
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Label
145.motorbikes-1013
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\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fd.vis.component_gallery()" ] }, { "cell_type": "markdown", "id": "6c8c654c-d68d-45d5-9f85-baba1ed9f61d", "metadata": {}, "source": [ "And also inspect duplicates." ] }, { "cell_type": "code", "execution_count": 7, "id": "10651ae0-7067-4b37-901e-e538fd74738e", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/dnth/anaconda3/envs/fastdup/lib/python3.10/site-packages/fastdup/galleries.py:100: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " df[out_col] = df[in_col].apply(lambda x: get_label_func.get(x, MISSING_LABEL))\n", "/home/dnth/anaconda3/envs/fastdup/lib/python3.10/site-packages/fastdup/galleries.py:100: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " df[out_col] = df[in_col].apply(lambda x: get_label_func.get(x, MISSING_LABEL))\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "700e376402a44048b2f73d86316d1057", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating gallery: 0%| | 0/20 [00:00\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Duplicates Report\n", " \n", " \n", "\n", "\n", "\n", "
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Duplicates Report

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Info
Distance1.0
From/256_ObjectCategories/214.teepee/214_0023.jpg
To/256_ObjectCategories/214.teepee/214_0004.jpg
From_Label214.teepee
To_Label214.teepee
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Info
Distance1.0
From/256_ObjectCategories/049.cormorant/049_0032.jpg
To/256_ObjectCategories/049.cormorant/049_0013.jpg
From_Label049.cormorant
To_Label049.cormorant
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Info
Distance1.0
From/256_ObjectCategories/224.touring-bike/224_0095.jpg
To/256_ObjectCategories/224.touring-bike/224_0004.jpg
From_Label224.touring-bike
To_Label224.touring-bike
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Info
Distance1.0
From/256_ObjectCategories/214.teepee/214_0030.jpg
To/256_ObjectCategories/214.teepee/214_0014.jpg
From_Label214.teepee
To_Label214.teepee
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Info
Distance1.0
From/256_ObjectCategories/214.teepee/214_0098.jpg
To/256_ObjectCategories/214.teepee/214_0037.jpg
From_Label214.teepee
To_Label214.teepee
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Info
Distance1.0
From/256_ObjectCategories/137.mars/137_0123.jpg
To/256_ObjectCategories/137.mars/137_0114.jpg
From_Label137.mars
To_Label137.mars
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Info
Distance1.0
From/256_ObjectCategories/214.teepee/214_0057.jpg
To/256_ObjectCategories/214.teepee/214_0004.jpg
From_Label214.teepee
To_Label214.teepee
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Info
Distance1.0
From/256_ObjectCategories/186.skunk/186_0007.jpg
To/256_ObjectCategories/186.skunk/186_0001.jpg
From_Label186.skunk
To_Label186.skunk
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Info
Distance1.0
From/256_ObjectCategories/228.triceratops/228_0055.jpg
To/256_ObjectCategories/228.triceratops/228_0001.jpg
From_Label228.triceratops
To_Label228.triceratops
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Info
Distance1.0
From/256_ObjectCategories/090.gorilla/090_0003.jpg
To/256_ObjectCategories/090.gorilla/090_0154.jpg
From_Label090.gorilla
To_Label090.gorilla
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Info
Distance1.0
From/256_ObjectCategories/249.yo-yo/249_0063.jpg
To/256_ObjectCategories/216.tennis-ball/216_0015.jpg
From_Label249.yo-yo
To_Label216.tennis-ball
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Info
Distance1.0
From/256_ObjectCategories/214.teepee/214_0028.jpg
To/256_ObjectCategories/214.teepee/214_0012.jpg
From_Label214.teepee
To_Label214.teepee
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Info
Distance1.0
From/256_ObjectCategories/043.coin/043_0116.jpg
To/256_ObjectCategories/043.coin/043_0115.jpg
From_Label043.coin
To_Label043.coin
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\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fd.vis.duplicates_gallery()" ] }, { "cell_type": "markdown", "id": "419c008f-4bb7-47a2-8e05-44f93843f5fd", "metadata": {}, "source": [ "You can also see potential mislabels." ] }, { "cell_type": "code", "execution_count": 8, "id": "a90abea4-aa64-493d-bbc8-d8d7c7b6422a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "92d4a6ce0e344b0880dd53d567c967b1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating gallery: 0%| | 0/2912 [00:00\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Similarity Report, label_score\n", " \n", " \n", "\n", "\n", "\n", "
\n", "
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\n", " \n", " \"logo\"\n", " \n", "
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Similarity Report, label_score

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Info From
label018.bowling-pin
from/256_ObjectCategories/018.bowling-pin/018_0017.jpg
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Info To
0.924003/256_ObjectCategories/232.t-shirt/232_0244.jpg232.t-shirt
0.912907/256_ObjectCategories/232.t-shirt/232_0003.jpg232.t-shirt
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Query Image
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Similar
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Info From
label039.chopsticks
from/256_ObjectCategories/039.chopsticks/039_0012.jpg
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Info To
0.913836/256_ObjectCategories/004.baseball-bat/004_0111.jpg004.baseball-bat
0.90769/256_ObjectCategories/004.baseball-bat/004_0031.jpg004.baseball-bat
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Query Image
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Similar
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Info From
label082.galaxy
from/256_ObjectCategories/082.galaxy/082_0073.jpg
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Info To
0.938969/256_ObjectCategories/044.comet/044_0036.jpg044.comet
0.931574/256_ObjectCategories/044.comet/044_0013.jpg044.comet
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Query Image
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Similar
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Info From
label091.grand-piano-101
from/256_ObjectCategories/091.grand-piano-101/091_0067.jpg
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Info To
0.915802/256_ObjectCategories/099.harpsichord/099_0080.jpg099.harpsichord
0.910628/256_ObjectCategories/099.harpsichord/099_0070.jpg099.harpsichord
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Query Image
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Similar
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Info From
label146.mountain-bike
from/256_ObjectCategories/146.mountain-bike/146_0041.jpg
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Info To
0.963142/256_ObjectCategories/224.touring-bike/224_0002.jpg224.touring-bike
0.960591/256_ObjectCategories/224.touring-bike/224_0092.jpg224.touring-bike
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Query Image
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Similar
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Info From
label146.mountain-bike
from/256_ObjectCategories/146.mountain-bike/146_0042.jpg
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Info To
0.906792/256_ObjectCategories/224.touring-bike/224_0013.jpg224.touring-bike
0.906261/256_ObjectCategories/224.touring-bike/224_0104.jpg224.touring-bike
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Query Image
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Similar
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Info From
label146.mountain-bike
from/256_ObjectCategories/146.mountain-bike/146_0069.jpg
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Info To
0.956316/256_ObjectCategories/224.touring-bike/224_0002.jpg224.touring-bike
0.953598/256_ObjectCategories/224.touring-bike/224_0096.jpg224.touring-bike
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Query Image
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Similar
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Info From
label224.touring-bike
from/256_ObjectCategories/224.touring-bike/224_0020.jpg
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Info To
0.958791/256_ObjectCategories/146.mountain-bike/146_0019.jpg146.mountain-bike
0.951026/256_ObjectCategories/146.mountain-bike/146_0055.jpg146.mountain-bike
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Query Image
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Info From
label224.touring-bike
from/256_ObjectCategories/224.touring-bike/224_0071.jpg
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Info To
0.956974/256_ObjectCategories/146.mountain-bike/146_0041.jpg146.mountain-bike
0.942855/256_ObjectCategories/146.mountain-bike/146_0037.jpg146.mountain-bike
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Query Image
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Similar
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Info From
label224.touring-bike
from/256_ObjectCategories/224.touring-bike/224_0074.jpg
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Info To
0.963531/256_ObjectCategories/146.mountain-bike/146_0071.jpg146.mountain-bike
0.955398/256_ObjectCategories/146.mountain-bike/146_0065.jpg146.mountain-bike
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Query Image
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Similar
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Info From
label239.washing-machine
from/256_ObjectCategories/239.washing-machine/239_0038.jpg
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Info To
0.914972/256_ObjectCategories/021.breadmaker/021_0058.jpg021.breadmaker
0.90993/256_ObjectCategories/021.breadmaker/021_0063.jpg021.breadmaker
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Query Image
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Info From
label004.baseball-bat
from/256_ObjectCategories/004.baseball-bat/004_0031.jpg
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Info To
0.90769/256_ObjectCategories/039.chopsticks/039_0012.jpg039.chopsticks
0.905357/256_ObjectCategories/004.baseball-bat/004_0051.jpg004.baseball-bat
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Query Image
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Similar
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Info From
label021.breadmaker
from/256_ObjectCategories/021.breadmaker/021_0063.jpg
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Info To
0.929278/256_ObjectCategories/021.breadmaker/021_0058.jpg021.breadmaker
0.90993/256_ObjectCategories/239.washing-machine/239_0038.jpg239.washing-machine
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Query Image
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Similar
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Info From
label021.breadmaker
from/256_ObjectCategories/021.breadmaker/021_0080.jpg
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Info To
0.923446/256_ObjectCategories/021.breadmaker/021_0058.jpg021.breadmaker
0.905769/256_ObjectCategories/239.washing-machine/239_0038.jpg239.washing-machine
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Query Image
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Similar
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Info From
label044.comet
from/256_ObjectCategories/044.comet/044_0011.jpg
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Info To
0.991649/256_ObjectCategories/044.comet/044_0013.jpg044.comet
0.924587/256_ObjectCategories/082.galaxy/082_0073.jpg082.galaxy
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Query Image
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Similar
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Info From
label044.comet
from/256_ObjectCategories/044.comet/044_0013.jpg
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Info To
0.991649/256_ObjectCategories/044.comet/044_0011.jpg044.comet
0.931574/256_ObjectCategories/082.galaxy/082_0073.jpg082.galaxy
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Query Image
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Info From
label044.comet
from/256_ObjectCategories/044.comet/044_0021.jpg
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Info To
0.919978/256_ObjectCategories/082.galaxy/082_0073.jpg082.galaxy
0.903337/256_ObjectCategories/044.comet/044_0036.jpg044.comet
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Query Image
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Info From
label044.comet
from/256_ObjectCategories/044.comet/044_0036.jpg
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0.921088/256_ObjectCategories/044.comet/044_0013.jpg044.comet
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0.900378/256_ObjectCategories/091.grand-piano-101/091_0067.jpg091.grand-piano-101
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0.910355/256_ObjectCategories/099.harpsichord/099_0067.jpg099.harpsichord
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274caltech256/256_ObjectCategories/039.chopsticks/039_0012.jpg[caltech256/256_ObjectCategories/004.baseball-bat/004_0031.jpg, caltech256/256_ObjectCategories/004.baseball-bat/004_0111.jpg][039.chopsticks, 039.chopsticks][004.baseball-bat, 004.baseball-bat][0.90769, 0.913836]0.02
511caltech256/256_ObjectCategories/082.galaxy/082_0073.jpg[caltech256/256_ObjectCategories/044.comet/044_0013.jpg, caltech256/256_ObjectCategories/044.comet/044_0036.jpg][082.galaxy, 082.galaxy][044.comet, 044.comet][0.931574, 0.938969]0.02
583caltech256/256_ObjectCategories/091.grand-piano-101/091_0067.jpg[caltech256/256_ObjectCategories/099.harpsichord/099_0070.jpg, caltech256/256_ObjectCategories/099.harpsichord/099_0080.jpg][091.grand-piano-101, 091.grand-piano-101][099.harpsichord, 099.harpsichord][0.910628, 0.915802]0.02
1528caltech256/256_ObjectCategories/146.mountain-bike/146_0041.jpg[caltech256/256_ObjectCategories/224.touring-bike/224_0092.jpg, caltech256/256_ObjectCategories/224.touring-bike/224_0002.jpg][146.mountain-bike, 146.mountain-bike][224.touring-bike, 224.touring-bike][0.960591, 0.963142]0.02
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2896caltech256/256_ObjectCategories/253.faces-easy-101/253_0425.jpg[caltech256/256_ObjectCategories/253.faces-easy-101/253_0420.jpg, caltech256/256_ObjectCategories/253.faces-easy-101/253_0429.jpg][253.faces-easy-101, 253.faces-easy-101][253.faces-easy-101, 253.faces-easy-101][0.901978, 0.910732]100.02
2898caltech256/256_ObjectCategories/253.faces-easy-101/253_0429.jpg[caltech256/256_ObjectCategories/253.faces-easy-101/253_0420.jpg, caltech256/256_ObjectCategories/253.faces-easy-101/253_0425.jpg][253.faces-easy-101, 253.faces-easy-101][253.faces-easy-101, 253.faces-easy-101][0.90975, 0.910732]100.02
2900caltech256/256_ObjectCategories/253.faces-easy-101/253_0432.jpg[caltech256/256_ObjectCategories/253.faces-easy-101/253_0423.jpg, caltech256/256_ObjectCategories/253.faces-easy-101/253_0434.jpg][253.faces-easy-101, 253.faces-easy-101][253.faces-easy-101, 253.faces-easy-101][0.907029, 0.915409]100.02
2901caltech256/256_ObjectCategories/253.faces-easy-101/253_0433.jpg[caltech256/256_ObjectCategories/253.faces-easy-101/253_0428.jpg, caltech256/256_ObjectCategories/253.faces-easy-101/253_0430.jpg][253.faces-easy-101, 253.faces-easy-101][253.faces-easy-101, 253.faces-easy-101][0.914409, 0.920648]100.02
2902caltech256/256_ObjectCategories/253.faces-easy-101/253_0434.jpg[caltech256/256_ObjectCategories/253.faces-easy-101/253_0432.jpg, caltech256/256_ObjectCategories/253.faces-easy-101/253_0423.jpg][253.faces-easy-101, 253.faces-easy-101][253.faces-easy-101, 253.faces-easy-101][0.915409, 0.928949]100.02
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If you're new, start here!\n", "+ 🧹 [**Clean Image Folder**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb): Learn how to analyze and clean a folder of images from potential issues and export a list of problematic files for further action. If you have an unorganized folder of images, this is a good place to start.\n", "+ 🖼 [**Analyze Image Classification Dataset**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/analyzing-image-classification-dataset.ipynb): Learn how to load a labeled image classification dataset and analyze for potential issues. If you have labeled ImageNet-style folder structure, have a go!\n", "+ 🎁 [**Analyze Object Detection Dataset**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/analyzing-object-detection-dataset.ipynb): Learn how to load bounding box annotations for object detection and analyze for potential issues. If you have a COCO-style labeled object detection dataset, give this example a try." ] }, { "cell_type": "markdown", "id": "44acb813-730b-4513-9266-17e0348f8584", "metadata": {}, "source": [ "\n", "## VL Profiler - A faster and easier way to diagnose and visualize dataset issues\n", "\n", "If you prefer a no-code platform to inspect and visualize your dataset, [**try our free cloud product VL Profiler**](https://app.visual-layer.com) - VL Profiler is our first no-code commercial product that lets you visualize and inspect your dataset in your browser. \n", "\n", "VL Profiler is free to get started. Upload up to 1,000,000 images for analysis at zero cost!\n", "\n", "[Sign up](https://app.visual-layer.com) now.\n", "\n", "[![image](https://raw.githubusercontent.com/visual-layer/fastdup/main/gallery/github_banner_profiler.gif)](https://app.visual-layer.com)\n", "\n", "As usual, feedback is welcome! Questions? Drop by our [Slack channel](https://visualdatabase.slack.com/join/shared_invite/zt-19jaydbjn-lNDEDkgvSI1QwbTXSY6dlA#/shared-invite/email) or open an issue on [GitHub](https://github.com/visual-layer/fastdup/issues)." ] }, { "cell_type": "markdown", "id": "75e95b2c-5354-46b6-8f5a-23d3c20e1864", "metadata": {}, "source": [ "
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