{ "cells": [ { "cell_type": "markdown", "id": "d1d92b2e", "metadata": {}, "source": [ "[![image](https://raw.githubusercontent.com/visual-layer/visuallayer/main/imgs/vl_horizontal_logo.png)](https://www.visual-layer.com)" ] }, { "cell_type": "markdown", "id": "731484b5", "metadata": {}, "source": [ "# Analyzing Hugging Face Datasets\n", "\n", "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/analyzing-hf-datasets.ipynb)\n", "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/analyzing-hf-datasets.ipynb)\n", "\n", "This notebook shows how you can use fastdup to analyze any dataset from [Hugging Face Datasets](https://huggingface.co/docs/datasets/index).\n", "\n", "We will analyze an image classification dataset for:\n", "\n", "+ Duplicates / near-duplicates\n", "+ Outliers\n", "+ Wrong labels" ] }, { "cell_type": "markdown", "id": "34d4d2db", "metadata": {}, "source": [ "## Installation" ] }, { "cell_type": "code", "execution_count": null, "id": "7176a4bc", "metadata": {}, "outputs": [], "source": [ "!pip install -Uq fastdup datasets" ] }, { "cell_type": "markdown", "id": "4dea523f", "metadata": {}, "source": [ "Now, test the installation. If there's no error message, we are ready to go." ] }, { "cell_type": "code", "execution_count": 16, "id": "655330c1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1.33'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import fastdup\n", "fastdup.__version__" ] }, { "cell_type": "markdown", "id": "40145087", "metadata": {}, "source": [ "## Load Dataset\n", "\n", "In this example we load the Tiny ImageNet dataset from [Hugging Face Datasets](https://huggingface.co/datasets)..\n", "\n", "Tiny ImageNet contains 100,000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images, and 50 test images.\n", "\n", "Let's load the dataset into our local directory." ] }, { "cell_type": "code", "execution_count": 17, "id": "d455b739", "metadata": {}, "outputs": [], "source": [ "import datasets\n", "dataset = datasets.load_dataset(\"zh-plus/tiny-imagenet\", split=\"train\", cache_dir='images_dir')" ] }, { "cell_type": "markdown", "id": "be18cac4", "metadata": {}, "source": [ "We can inspect the `dataset` object." ] }, { "cell_type": "code", "execution_count": 18, "id": "85ea7e08", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Dataset({\n", " features: ['image', 'label'],\n", " num_rows: 100000\n", "})" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset" ] }, { "cell_type": "code", "execution_count": 19, "id": "3e05ba85", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'image': ,\n", " 'label': 0}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[0]" ] }, { "cell_type": "code", "execution_count": 20, "id": "e1078a54", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[0]['image']" ] }, { "cell_type": "code", "execution_count": 21, "id": "07daca49", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[0]['label']" ] }, { "cell_type": "markdown", "id": "61b315c3", "metadata": {}, "source": [ "## Get Labels Mapping\n", "\n", "Tiny ImageNet follows the original ImageNet class names. Let's download the class mappings `classes.py`." ] }, { "cell_type": "code", "execution_count": 22, "id": "eb3c000c", "metadata": {}, "outputs": [], "source": [ "!wget -q https://huggingface.co/datasets/zh-plus/tiny-imagenet/raw/main/classes.py" ] }, { "cell_type": "markdown", "id": "90212bfd", "metadata": {}, "source": [ "Here's the top 10 lines of `classes.py`." ] }, { "cell_type": "code", "execution_count": 23, "id": "79569736", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "i2d = {\n", " \"n00001740\": \"entity\",\n", " \"n00001930\": \"physical entity\",\n", " \"n00002137\": \"abstraction, abstract entity\",\n", " \"n00002452\": \"thing\",\n", " \"n00002684\": \"object, physical object\",\n", " \"n00003553\": \"whole, unit\",\n", " \"n00003993\": \"congener\",\n", " \"n00004258\": \"living thing, animate thing\",\n", " \"n00004475\": \"organism, being\",\n" ] } ], "source": [ "!head -n 10 classes.py" ] }, { "cell_type": "markdown", "id": "edb6463d", "metadata": {}, "source": [ "Now we can get the class names by providing the class ID. For example:" ] }, { "cell_type": "code", "execution_count": 24, "id": "ac4fcdb4", "metadata": {}, "outputs": [], "source": [ "from classes import i2d" ] }, { "cell_type": "code", "execution_count": 25, "id": "9b000a73", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'entity'" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "i2d[\"n00001740\"]" ] }, { "cell_type": "markdown", "id": "2f6c5990", "metadata": {}, "source": [ "## Save Images to Disk" ] }, { "cell_type": "markdown", "id": "69319cf7", "metadata": {}, "source": [ "The images are downloaded in a parquet format. Let's save them into the local disk." ] }, { "cell_type": "code", "execution_count": 36, "id": "2913137d", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3a10f560e9f243e097ed3ca951e7b535", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/100000 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
filenamelabelsplit
0images_dir/images/swimming-trunks_bathing-trunks/73748.jpgswimming-trunks_bathing-trunkstrain
1images_dir/images/swimming-trunks_bathing-trunks/73509.jpgswimming-trunks_bathing-trunkstrain
2images_dir/images/swimming-trunks_bathing-trunks/73946.jpgswimming-trunks_bathing-trunkstrain
3images_dir/images/swimming-trunks_bathing-trunks/73669.jpgswimming-trunks_bathing-trunkstrain
4images_dir/images/swimming-trunks_bathing-trunks/73733.jpgswimming-trunks_bathing-trunkstrain
............
99995images_dir/images/Yorkshire-terrier/11304.jpgYorkshire-terriertrain
99996images_dir/images/Yorkshire-terrier/11244.jpgYorkshire-terriertrain
99997images_dir/images/Yorkshire-terrier/11439.jpgYorkshire-terriertrain
99998images_dir/images/Yorkshire-terrier/11290.jpgYorkshire-terriertrain
99999images_dir/images/Yorkshire-terrier/11490.jpgYorkshire-terriertrain
\n", "

100000 rows × 3 columns

\n", "" ], "text/plain": [ " filename label split\n", "0 images_dir/images/swimming-trunks_bathing-trunks/73748.jpg swimming-trunks_bathing-trunks train\n", "1 images_dir/images/swimming-trunks_bathing-trunks/73509.jpg swimming-trunks_bathing-trunks train\n", "2 images_dir/images/swimming-trunks_bathing-trunks/73946.jpg swimming-trunks_bathing-trunks train\n", "3 images_dir/images/swimming-trunks_bathing-trunks/73669.jpg swimming-trunks_bathing-trunks train\n", "4 images_dir/images/swimming-trunks_bathing-trunks/73733.jpg swimming-trunks_bathing-trunks train\n", "... ... ... ...\n", "99995 images_dir/images/Yorkshire-terrier/11304.jpg Yorkshire-terrier train\n", "99996 images_dir/images/Yorkshire-terrier/11244.jpg Yorkshire-terrier train\n", "99997 images_dir/images/Yorkshire-terrier/11439.jpg Yorkshire-terrier train\n", "99998 images_dir/images/Yorkshire-terrier/11290.jpg Yorkshire-terrier train\n", "99999 images_dir/images/Yorkshire-terrier/11490.jpg Yorkshire-terrier train\n", "\n", "[100000 rows x 3 columns]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "import pandas as pd\n", "\n", "path = 'images_dir/images'\n", "\n", "filenames = []\n", "labels = []\n", "\n", "for label in os.listdir(path):\n", " label_path = os.path.join(path, label)\n", " if os.path.isdir(label_path):\n", " for filename in os.listdir(label_path):\n", " filenames.append(os.path.join(label_path, filename))\n", " labels.append(label)\n", "\n", "df = pd.DataFrame({\n", " 'filename': filenames,\n", " 'label': labels\n", "})\n", "\n", "df[\"split\"] = \"train\"\n", "\n", "df\n", "\n" ] }, { "cell_type": "markdown", "id": "6aac94ea", "metadata": {}, "source": [ "## Run fastdup" ] }, { "cell_type": "code", "execution_count": 38, "id": "8e90af72", "metadata": {}, "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", "2023-08-07 20:18:12 [INFO] Going to loop over dir /tmp/tmphta0o4og.csv\n", "2023-08-07 20:18:12 [INFO] Found total 100000 images to run on, 100000 train, 0 test, name list 100000, counter 100000 \n", "2023-08-07 20:21:51 [INFO] Found total 100000 images to run onmated: 0 Minutes\n", "Finished histogram 24.316\n", "Finished bucket sort 24.500\n", "2023-08-07 20:22:11 [INFO] 20350) Finished write_index() NN model\n", "2023-08-07 20:22:11 [INFO] Stored nn model index file work_dir/nnf.index\n", "2023-08-07 20:22:22 [INFO] Total time took 249230 ms\n", "2023-08-07 20:22:22 [INFO] Found a total of 40 fully identical images (d>0.990), which are 0.02 %\n", "2023-08-07 20:22:22 [INFO] Found a total of 0 nearly identical images(d>0.980), which are 0.00 %\n", "2023-08-07 20:22:22 [INFO] Found a total of 10829 above threshold images (d>0.900), which are 5.41 %\n", "2023-08-07 20:22:22 [INFO] Found a total of 10001 outlier images (d<0.050), which are 5.00 %\n", "2023-08-07 20:22:22 [INFO] Min distance found 0.594 max distance 1.000\n", "2023-08-07 20:22:22 [INFO] Running connected components for ccthreshold 0.960000 \n", ".0\n", " ########################################################################################\n", "\n", "Dataset Analysis Summary: \n", "\n", " Dataset contains 100000 images\n", " Valid images are 88.00% (100,000) of the data, invalid are 0.00% (0) of the data\n", " Similarity: 0.04% (46) belong to 1 similarity clusters (components).\n", " 87.96% (99,954) images do not belong to any similarity cluster.\n", " Largest cluster has 4 (0.00%) 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: 5.58% (6,345) 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": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fd = fastdup.create(input_dir='images_dir/')\n", "fd.run(annotations=df, overwrite=True)" ] }, { "cell_type": "markdown", "id": "676d9175", "metadata": {}, "source": [ "## Inspect Issues" ] }, { "cell_type": "markdown", "id": "1017106b", "metadata": {}, "source": [ "There are several methods we can use to inspect the issues found:\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": "code", "execution_count": 39, "id": "8f558b89", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/dnth/anaconda3/envs/datapull/lib/python3.10/site-packages/fastdup/galleries.py:106: 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/datapull/lib/python3.10/site-packages/fastdup/galleries.py:106: 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", "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 360.31it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Stored similarity visual view in work_dir/galleries/duplicates.html\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/html": [ " \n", " \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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Distance1.0
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Info
Distance1.0
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Info
Distance1.0
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Distance1.0
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Distance1.0
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Distance1.0
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Distance1.0
From/images/coral-reef/95198.jpg
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Distance1.0
From/images/lemon/88852.jpg
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From_Labellemon
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Distance1.0
From/images/banana/89263.jpg
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From_Labelbanana
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Distance1.0
From/images/orange/99746.jpg
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Distance1.0
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Distance1.0
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Outliers Report

Showing image outliers, one per row

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Distance0.59868
Path/images/slug/99254.jpg
labelslug
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Distance0.630931
Path/images/fountain/47232.jpg
labelfountain
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Distance0.640042
Path/images/jellyfish/6152.jpg
labeljellyfish
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Distance0.654982
Path/images/walking-stick_walkingstick_stick-insect/17626.jpg
labelwalking-stick_walkingstick_stick-insect
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Distance0.658417
Path/images/remote-control_remote/65026.jpg
labelremote-control_remote
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Distance0.665128
Path/images/pretzel/86745.jpg
labelpretzel
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Distance0.665253
Path/images/centipede/5052.jpg
labelcentipede
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Distance0.665985
Path/images/centipede/5240.jpg
labelcentipede
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Distance0.666785
Path/images/chain/98818.jpg
labelchain
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Distance0.67015
Path/images/nail/98207.jpg
labelnail
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Distance0.6707
Path/images/abacus/27129.jpg
labelabacus
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Distance0.674768
Path/images/volleyball/80147.jpg
labelvolleyball
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Distance0.675084
Path/images/abacus/27267.jpg
labelabacus
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Distance0.67606
Path/images/chain/98911.jpg
labelchain
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Distance0.676764
Path/images/nail/98021.jpg
labelnail
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Distance0.677318
Path/images/crane/43785.jpg
labelcrane
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Path/images/fountain/47006.jpg
labelfountain
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Path/images/space-heater/70208.jpg
labelspace-heater
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Distance0.679209
Path/images/nail/98027.jpg
labelnail
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Path/images/barbershop/29697.jpg
labelbarbershop
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Info From
labelAfrican-elephant_Loxodonta-africana
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0.900586/images/German-shepherd_German-shepherd-dog_German-police-dog_alsatian/12798.jpgGerman-shepherd_German-shepherd-dog_German-police-dog_alsatian
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Info From
labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26046.jpg
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0.90894/images/military-uniform/55737.jpgmilitary-uniform
0.906594/images/cliff_drop_drop-off/94750.jpgcliff_drop_drop-off
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Info From
labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26049.jpg
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0.905895/images/cliff-dwelling/41561.jpgcliff-dwelling
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Info From
labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26083.jpg
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0.911342/images/trilobite/3492.jpgtrilobite
0.904064/images/cliff_drop_drop-off/94863.jpgcliff_drop_drop-off
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labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26114.jpg
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0.929913/images/mushroom/97992.jpgmushroom
0.92827/images/suspension-bridge/73142.jpgsuspension-bridge
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Info From
labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26164.jpg
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0.910503/images/bison/22886.jpgbison
0.901101/images/cliff_drop_drop-off/94749.jpgcliff_drop_drop-off
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labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26203.jpg
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0.919515/images/spider-web_spiders-web/70711.jpgspider-web_spiders-web
0.906872/images/American-alligator_Alligator-mississipiensis/2277.jpgAmerican-alligator_Alligator-mississipiensis
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labelAfrican-elephant_Loxodonta-africana
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0.912121/images/koala_koala-bear_kangaroo-bear_native-bear_Phascolarctos-cinereus/5984.jpgkoala_koala-bear_kangaroo-bear_native-bear_Phascolarctos-cinereus
0.907932/images/Arabian-camel_dromedary_Camelus-dromedarius/24191.jpgArabian-camel_dromedary_Camelus-dromedarius
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labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26260.jpg
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0.919056/images/cliff-dwelling/41511.jpgcliff-dwelling
0.910586/images/bighorn_bighorn-sheep_cimarron_Rocky-Mountain-bighorn_Rocky-Mountain-sheep_Ovis-canadensis/23409.jpgbighorn_bighorn-sheep_cimarron_Rocky-Mountain-bighorn_Rocky-Mountain-sheep_Ovis-canadensis
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Query Image
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Info From
labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26303.jpg
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Info To
0.903905/images/cliff-dwelling/41936.jpgcliff-dwelling
0.903871/images/cougar_puma_catamount_mountain-lion_painter_panther_Felis-concolor/15392.jpgcougar_puma_catamount_mountain-lion_painter_panther_Felis-concolor
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Query Image
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Info From
labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26308.jpg
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Info To
0.92572/images/bison/22662.jpgbison
0.908881/images/baboon/25895.jpgbaboon
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Query Image
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Info From
labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26344.jpg
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Info To
0.936067/images/American-alligator_Alligator-mississipiensis/2277.jpgAmerican-alligator_Alligator-mississipiensis
0.932738/images/cliff-dwelling/41788.jpgcliff-dwelling
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Query Image
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Info From
labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26363.jpg
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Info To
0.904164/images/cliff-dwelling/41534.jpgcliff-dwelling
0.90095/images/ox/22268.jpgox
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Query Image
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Info From
labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26398.jpg
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Info To
0.918615/images/bison/22767.jpgbison
0.911312/images/bighorn_bighorn-sheep_cimarron_Rocky-Mountain-bighorn_Rocky-Mountain-sheep_Ovis-canadensis/23192.jpgbighorn_bighorn-sheep_cimarron_Rocky-Mountain-bighorn_Rocky-Mountain-sheep_Ovis-canadensis
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Query Image
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Info From
labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26410.jpg
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Info To
0.912708/images/koala_koala-bear_kangaroo-bear_native-bear_Phascolarctos-cinereus/5690.jpgkoala_koala-bear_kangaroo-bear_native-bear_Phascolarctos-cinereus
0.911665/images/American-alligator_Alligator-mississipiensis/2468.jpgAmerican-alligator_Alligator-mississipiensis
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Query Image
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Info From
labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26412.jpg
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0.90663/images/cougar_puma_catamount_mountain-lion_painter_panther_Felis-concolor/15355.jpgcougar_puma_catamount_mountain-lion_painter_panther_Felis-concolor
0.903551/images/bighorn_bighorn-sheep_cimarron_Rocky-Mountain-bighorn_Rocky-Mountain-sheep_Ovis-canadensis/23465.jpgbighorn_bighorn-sheep_cimarron_Rocky-Mountain-bighorn_Rocky-Mountain-sheep_Ovis-canadensis
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Query Image
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Info From
labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26420.jpg
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Info To
0.947837/images/cliff_drop_drop-off/94962.jpgcliff_drop_drop-off
0.941967/images/bighorn_bighorn-sheep_cimarron_Rocky-Mountain-bighorn_Rocky-Mountain-sheep_Ovis-canadensis/23069.jpgbighorn_bighorn-sheep_cimarron_Rocky-Mountain-bighorn_Rocky-Mountain-sheep_Ovis-canadensis
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Query Image
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Info From
labelAfrican-elephant_Loxodonta-africana
from/images/African-elephant_Loxodonta-africana/26498.jpg
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Info To
0.901463/images/baboon/25566.jpgbaboon
0.900453/images/cliff-dwelling/41861.jpgcliff-dwelling
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Query Image
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0.932808/images/bullfrog_Rana-catesbeiana/1027.jpgbullfrog_Rana-catesbeiana
0.924776/images/European-fire-salamander_Salamandra-salamandra/697.jpgEuropean-fire-salamander_Salamandra-salamandra
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0.929694/images/bighorn_bighorn-sheep_cimarron_Rocky-Mountain-bighorn_Rocky-Mountain-sheep_Ovis-canadensis/23144.jpgbighorn_bighorn-sheep_cimarron_Rocky-Mountain-bighorn_Rocky-Mountain-sheep_Ovis-canadensis
0.924442/images/spider-web_spiders-web/70818.jpgspider-web_spiders-web
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fromtolabellabel2distancescorelength
1images_dir/images/African-elephant_Loxodonta-africana/26003.jpg[images_dir/images/German-shepherd_German-shepherd-dog_German-police-dog_alsatian/12798.jpg, images_dir/images/boa-constrictor_Constrictor-constrictor/2954.jpg][African-elephant_Loxodonta-africana, African-elephant_Loxodonta-africana][German-shepherd_German-shepherd-dog_German-police-dog_alsatian, boa-constrictor_Constrictor-constrictor][0.900586, 0.905542]0.02
5images_dir/images/African-elephant_Loxodonta-africana/26046.jpg[images_dir/images/cliff_drop_drop-off/94750.jpg, images_dir/images/military-uniform/55737.jpg][African-elephant_Loxodonta-africana, African-elephant_Loxodonta-africana][cliff_drop_drop-off, military-uniform][0.906594, 0.90894]0.02
6images_dir/images/African-elephant_Loxodonta-africana/26049.jpg[images_dir/images/cliff-dwelling/41561.jpg, images_dir/images/boa-constrictor_Constrictor-constrictor/2593.jpg][African-elephant_Loxodonta-africana, African-elephant_Loxodonta-africana][cliff-dwelling, boa-constrictor_Constrictor-constrictor][0.905895, 0.906849]0.02
7images_dir/images/African-elephant_Loxodonta-africana/26083.jpg[images_dir/images/cliff_drop_drop-off/94863.jpg, images_dir/images/trilobite/3492.jpg][African-elephant_Loxodonta-africana, African-elephant_Loxodonta-africana][cliff_drop_drop-off, trilobite][0.904064, 0.911342]0.02
13images_dir/images/African-elephant_Loxodonta-africana/26114.jpg[images_dir/images/suspension-bridge/73142.jpg, images_dir/images/mushroom/97992.jpg][African-elephant_Loxodonta-africana, African-elephant_Loxodonta-africana][suspension-bridge, mushroom][0.92827, 0.929913]0.02
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7097images_dir/images/water-tower/81086.jpg[images_dir/images/water-tower/81095.jpg, images_dir/images/water-tower/81128.jpg][water-tower, water-tower][water-tower, water-tower][0.90285, 0.905127]100.02
7099images_dir/images/water-tower/81102.jpg[images_dir/images/water-tower/81331.jpg, images_dir/images/water-tower/81308.jpg][water-tower, water-tower][water-tower, water-tower][0.903888, 0.91205]100.02
7100images_dir/images/water-tower/81128.jpg[images_dir/images/water-tower/81208.jpg, images_dir/images/water-tower/81086.jpg][water-tower, water-tower][water-tower, water-tower][0.900657, 0.905127]100.02
7106images_dir/images/water-tower/81308.jpg[images_dir/images/water-tower/81082.jpg, images_dir/images/water-tower/81102.jpg][water-tower, water-tower][water-tower, water-tower][0.904132, 0.91205]100.02
7112images_dir/images/water-tower/81429.jpg[images_dir/images/water-tower/81316.jpg, images_dir/images/water-tower/81441.jpg][water-tower, water-tower][water-tower, water-tower][0.900633, 0.911426]100.02
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3683 rows × 7 columns

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" ], "text/plain": [ " from to label label2 distance score length\n", "1 images_dir/images/African-elephant_Loxodonta-africana/26003.jpg [images_dir/images/German-shepherd_German-shepherd-dog_German-police-dog_alsatian/12798.jpg, images_dir/images/boa-constrictor_Constrictor-constrictor/2954.jpg] [African-elephant_Loxodonta-africana, African-elephant_Loxodonta-africana] [German-shepherd_German-shepherd-dog_German-police-dog_alsatian, boa-constrictor_Constrictor-constrictor] [0.900586, 0.905542] 0.0 2\n", "5 images_dir/images/African-elephant_Loxodonta-africana/26046.jpg [images_dir/images/cliff_drop_drop-off/94750.jpg, images_dir/images/military-uniform/55737.jpg] [African-elephant_Loxodonta-africana, African-elephant_Loxodonta-africana] [cliff_drop_drop-off, military-uniform] [0.906594, 0.90894] 0.0 2\n", "6 images_dir/images/African-elephant_Loxodonta-africana/26049.jpg [images_dir/images/cliff-dwelling/41561.jpg, images_dir/images/boa-constrictor_Constrictor-constrictor/2593.jpg] [African-elephant_Loxodonta-africana, African-elephant_Loxodonta-africana] [cliff-dwelling, boa-constrictor_Constrictor-constrictor] [0.905895, 0.906849] 0.0 2\n", "7 images_dir/images/African-elephant_Loxodonta-africana/26083.jpg [images_dir/images/cliff_drop_drop-off/94863.jpg, images_dir/images/trilobite/3492.jpg] [African-elephant_Loxodonta-africana, African-elephant_Loxodonta-africana] [cliff_drop_drop-off, trilobite] [0.904064, 0.911342] 0.0 2\n", "13 images_dir/images/African-elephant_Loxodonta-africana/26114.jpg [images_dir/images/suspension-bridge/73142.jpg, images_dir/images/mushroom/97992.jpg] [African-elephant_Loxodonta-africana, African-elephant_Loxodonta-africana] [suspension-bridge, mushroom] [0.92827, 0.929913] 0.0 2\n", "... ... ... ... ... ... ... ...\n", "7097 images_dir/images/water-tower/81086.jpg [images_dir/images/water-tower/81095.jpg, images_dir/images/water-tower/81128.jpg] [water-tower, water-tower] [water-tower, water-tower] [0.90285, 0.905127] 100.0 2\n", "7099 images_dir/images/water-tower/81102.jpg [images_dir/images/water-tower/81331.jpg, images_dir/images/water-tower/81308.jpg] [water-tower, water-tower] [water-tower, water-tower] [0.903888, 0.91205] 100.0 2\n", "7100 images_dir/images/water-tower/81128.jpg [images_dir/images/water-tower/81208.jpg, images_dir/images/water-tower/81086.jpg] [water-tower, water-tower] [water-tower, water-tower] [0.900657, 0.905127] 100.0 2\n", "7106 images_dir/images/water-tower/81308.jpg [images_dir/images/water-tower/81082.jpg, images_dir/images/water-tower/81102.jpg] [water-tower, water-tower] [water-tower, water-tower] [0.904132, 0.91205] 100.0 2\n", "7112 images_dir/images/water-tower/81429.jpg [images_dir/images/water-tower/81316.jpg, images_dir/images/water-tower/81441.jpg] [water-tower, water-tower] [water-tower, water-tower] [0.900633, 0.911426] 100.0 2\n", "\n", "[3683 rows x 7 columns]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fd.vis.similarity_gallery(slice='diff')" ] }, { "cell_type": "markdown", "id": "a4eb87fa", "metadata": {}, "source": [ "## Wrap Up\n", "\n", "That's a wrap! In this notebook we showed how you can run fastdup on a Hugging Face Dataset. You can use similar methods to run on other similar datasets on [Huggging Face Datasets](https://huggingface.co/datasets).\n", "\n", "Try it out and let us know what issues you find.\n", "\n", "\n", "Next, feel free to check out other tutorials -\n", "\n", "+ ⚡ [**Quickstart**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/quick-dataset-analysis.ipynb): Learn how to install fastdup, load a dataset and analyze it for potential issues such as duplicates/near-duplicates, broken images, outliers, dark/bright/blurry images, and view visually similar image clusters. 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": "08fd287b", "metadata": {}, "source": [ "\n", "# VL Profiler\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", "[Sign up](https://app.visual-layer.com) now, it's free.\n", "\n", "[![image](https://raw.githubusercontent.com/visual-layer/fastdup/main/gallery/vl_profiler_promo.svg)](https://app.visual-layer.com)\n", "\n", "As usual, feedback is welcome! \n", "\n", "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)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 }