{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"
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\n\n

Google Universal Image Embedding

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CREATED BY: DARIEN SCHETTLER
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\n\n---\n\n
\n\n
\n πŸ›‘   WARNING:

THIS IS A WORK IN PROGRESS
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\n\n\n
\n πŸ‘   IF YOU FORK THIS OR FIND THIS HELPFUL   πŸ‘

PLEASE UPVOTE!

This was a lot of work for me and while it may seem silly, it makes me feel appreciated when others like my work. πŸ˜…\n
\n\n\n","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"

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\n\n

TABLE OF CONTENTS

\n\n---\n\n

0    IMPORTS

\n\n---\n\n

1    BACKGROUND INFORMATION

\n\n---\n\n

2    SETUP

\n\n---\n\n

3    HELPER FUNCTIONS

\n\n---\n\n

4    DATASET EXPLORATION

\n\n---\n\n

5    MODELLING

\n\n---","metadata":{}},{"cell_type":"markdown","source":"
\n\n\n\n

0  IMPORTS    ⤒

","metadata":{}},{"cell_type":"code","source":"print(\"\\n... IMPORTS STARTING ...\\n\")\n\nprint(\"\\n\\tVERSION INFORMATION\")\n# Machine Learning and Data Science Imports\nimport tensorflow as tf; print(f\"\\t\\t– TENSORFLOW VERSION: {tf.__version__}\");\nimport tensorflow_hub as tfhub; print(f\"\\t\\t– TENSORFLOW HUB VERSION: {tfhub.__version__}\");\nimport tensorflow_addons as tfa; print(f\"\\t\\t– TENSORFLOW ADDONS VERSION: {tfa.__version__}\");\nimport tensorflow_io as tfio; print(f\"\\t\\t– TENSORFLOW I/O VERSION: {tfio.__version__}\");\nimport pandas as pd; pd.options.mode.chained_assignment = None;\nimport numpy as np; print(f\"\\t\\t– NUMPY VERSION: {np.__version__}\");\nimport sklearn; print(f\"\\t\\t– SKLEARN VERSION: {sklearn.__version__}\");\nfrom sklearn.preprocessing import RobustScaler, PolynomialFeatures\nfrom pandarallel import pandarallel; pandarallel.initialize();\nfrom sklearn.model_selection import GroupKFold, StratifiedKFold\nfrom scipy.spatial import cKDTree\n\n# RAPIDS\n# import cudf, cupy, cuml\n# from cuml.neighbors import NearestNeighbors\n# from cuml.manifold import TSNE, UMAP\n# from cuml import PCA\n\n# Built In Imports\nfrom kaggle_datasets import KaggleDatasets\nfrom collections import Counter\nfrom datetime import datetime\nfrom zipfile import ZipFile\nfrom glob import glob\nimport openslide\nimport warnings\nimport requests\nimport hashlib\nimport imageio\nimport IPython\nimport sklearn\nimport urllib\nimport zipfile\nimport pickle\nimport random\nimport shutil\nimport string\nimport json\nimport math\nimport time\nimport gzip\nimport ast\nimport sys\nimport io\nimport os\nimport gc\nimport re\n\n# Visualization Imports\nfrom matplotlib.colors import ListedColormap\nfrom matplotlib.patches import Rectangle\nimport matplotlib.patches as patches\nimport plotly.graph_objects as go\nimport matplotlib.pyplot as plt\nfrom tqdm.notebook import tqdm; tqdm.pandas();\nimport plotly.express as px\nimport tifffile as tif\nimport seaborn as sns\nfrom PIL import Image, ImageEnhance; Image.MAX_IMAGE_PIXELS = 5_000_000_000;\nimport matplotlib; print(f\"\\t\\t– MATPLOTLIB VERSION: {matplotlib.__version__}\");\nfrom matplotlib import animation, rc; rc('animation', html='jshtml')\nimport plotly\nimport PIL\nimport cv2\n\nimport plotly.io as pio\nprint(pio.renderers)\n\ndef seed_it_all(seed=7):\n \"\"\" Attempt to be Reproducible \"\"\"\n os.environ['PYTHONHASHSEED'] = str(seed)\n random.seed(seed)\n np.random.seed(seed)\n tf.random.set_seed(seed)\n\n \nprint(\"\\n\\n... IMPORTS COMPLETE ...\\n\")","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:22:34.921199Z","iopub.execute_input":"2023-04-19T02:22:34.92232Z","iopub.status.idle":"2023-04-19T02:22:47.449632Z","shell.execute_reply.started":"2023-04-19T02:22:34.922276Z","shell.execute_reply":"2023-04-19T02:22:47.448484Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"\n... IMPORTS STARTING ...\n\n\n\tVERSION INFORMATION\n\t\t– TENSORFLOW VERSION: 2.11.0\n\t\t– TENSORFLOW HUB VERSION: 0.12.0\n\t\t– TENSORFLOW ADDONS VERSION: 0.19.0\n\t\t– TENSORFLOW I/O VERSION: 0.29.0\n\t\t– NUMPY VERSION: 1.21.6\n\t\t– SKLEARN VERSION: 1.0.2\nINFO: Pandarallel will run on 2 workers.\nINFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n\t\t– MATPLOTLIB VERSION: 3.5.3\nRenderers configuration\n-----------------------\n Default renderer: 'kaggle'\n Available renderers:\n ['plotly_mimetype', 'jupyterlab', 'nteract', 'vscode',\n 'notebook', 'notebook_connected', 'kaggle', 'azure', 'colab',\n 'cocalc', 'databricks', 'json', 'png', 'jpeg', 'jpg', 'svg',\n 'pdf', 'browser', 'firefox', 'chrome', 'chromium', 'iframe',\n 'iframe_connected', 'sphinx_gallery', 'sphinx_gallery_png']\n\n\n\n... IMPORTS COMPLETE ...\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"
\n\n\n\n

1  BACKGROUND INFORMATION    ⤒

\n\n---\n\nThis notebook will create a dataset that spans all the required categories. It will also add in various miscellaneous datasets.\n\nThe list of datasets being used are as follows:","metadata":{}},{"cell_type":"markdown","source":"
\n\n\n\n

2  SETUP    ⤒

\n\n---\n","metadata":{}},{"cell_type":"markdown","source":"
\n\n

2.1 ACCELERATOR DETECTION

\n\n---\n\nIn order to use **`TPU`**, we use **`TPUClusterResolver`** for the initialization which is necessary to connect to the remote cluster and initialize cloud TPUs. Let's go over two important points\n\n1. When using TPU on Kaggle, you don't need to specify arguments for **`TPUClusterResolver`**\n2. However, on **G**oogle **C**ompute **E**ngine (**GCE**), you will need to do the following:\n\n
\n\n```python\n# The name you gave to the TPU to use\nTPU_WORKER = 'my-tpu-name'\n\n# or you can also specify the grpc path directly\n# TPU_WORKER = 'grpc://xxx.xxx.xxx.xxx:8470'\n\n# The zone you chose when you created the TPU to use on GCP.\nZONE = 'us-east1-b'\n\n# The name of the GCP project where you created the TPU to use on GCP.\nPROJECT = 'my-tpu-project'\n\ntpu = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=TPU_WORKER, zone=ZONE, project=PROJECT)\n```\n\n
\n πŸ›‘   WARNING:

- Although the Tensorflow documentation says it is the project name that should be provided for the argument `project`, it is actually the Project ID, that you should provide. This can be found on the GCP project dashboard page.
\n
\n\n
\n πŸ“–   REFERENCES:

\n - Guide - Use TPUs
\n - Doc - TPUClusterResolver
\n\n
","metadata":{}},{"cell_type":"code","source":"print(f\"\\n... ACCELERATOR SETUP STARTING ...\\n\")\n\n# Detect hardware, return appropriate distribution strategy\ntry:\n # TPU detection. No parameters necessary if TPU_NAME environment variable is set. On Kaggle this is always the case.\n TPU = tf.distribute.cluster_resolver.TPUClusterResolver() \nexcept ValueError:\n TPU = None\n\nif TPU:\n print(f\"\\n... RUNNING ON TPU - {TPU.master()}...\")\n tf.config.experimental_connect_to_cluster(TPU)\n tf.tpu.experimental.initialize_tpu_system(TPU)\n strategy = tf.distribute.experimental.TPUStrategy(TPU)\nelse:\n print(f\"\\n... RUNNING ON CPU/GPU ...\")\n # Yield the default distribution strategy in Tensorflow\n # --> Works on CPU and single GPU.\n strategy = tf.distribute.get_strategy()\n # tf.config.experimental.set_memory_growth(tf.config.list_physical_devices('GPU')[0], True)\n\n# What Is a Replica?\n# --> A single Cloud TPU device consists of FOUR chips, each of which has TWO TPU cores. \n# --> Therefore, for efficient utilization of Cloud TPU, a program should make use of each of the EIGHT (4x2) cores. \n# --> Each replica is essentially a copy of the training graph that is run on each core and \n# trains a mini-batch containing 1/8th of the overall batch size\nN_REPLICAS = strategy.num_replicas_in_sync\n \nprint(f\"... # OF REPLICAS: {N_REPLICAS} ...\\n\")\n\nprint(f\"\\n... ACCELERATOR SETUP COMPLTED ...\\n\")","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:22:47.451608Z","iopub.execute_input":"2023-04-19T02:22:47.452007Z","iopub.status.idle":"2023-04-19T02:22:47.467348Z","shell.execute_reply.started":"2023-04-19T02:22:47.451976Z","shell.execute_reply":"2023-04-19T02:22:47.465935Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"\n... ACCELERATOR SETUP STARTING ...\n\n\n... RUNNING ON CPU/GPU ...\n... # OF REPLICAS: 1 ...\n\n\n... ACCELERATOR SETUP COMPLTED ...\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"
\n\n

2.2 COMPETITION DATA ACCESS

\n\n---\n\nTPUs read data must be read directly from **G**oogle **C**loud **S**torage **(GCS)**. Kaggle provides a utility library – **`KaggleDatasets`** – which has a utility function **`.get_gcs_path`** that will allow us to access the location of our input datasets within **GCS**.

\n\n
\n πŸ“Œ   TIPS:

- If you have multiple datasets attached to the notebook, you should pass the name of a specific dataset to the `get_gcs_path()` function. In our case, the name of the dataset is the name of the directory the dataset is mounted within.

\n
","metadata":{}},{"cell_type":"code","source":"print(\"\\n... DATA ACCESS SETUP STARTED ...\\n\")\n\n# Root folder definitions\nINPUT_DIR = \"/kaggle/input\"\nWORKING_DIR = \"/kaggle/working\"\n\nif TPU:\n save_locally = tf.saved_model.SaveOptions(experimental_io_device='/job:localhost')\n load_locally = tf.saved_model.LoadOptions(experimental_io_device='/job:localhost')\nelse:\n save_locally = None\n load_locally = None\n\nprint(\"\\n\\n... DATASETS LOADED ...\")\nfor x in os.listdir(INPUT_DIR): print(f\"\\t--> {x}\")\n \nprint(\"\\n\\n... DATA ACCESS SETUP COMPLETED ...\\n\")","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:22:47.468738Z","iopub.execute_input":"2023-04-19T02:22:47.469101Z","iopub.status.idle":"2023-04-19T02:22:47.478327Z","shell.execute_reply.started":"2023-04-19T02:22:47.469059Z","shell.execute_reply":"2023-04-19T02:22:47.47697Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"\n... DATA ACCESS SETUP STARTED ...\n\n\n\n... DATASETS LOADED ...\n\t--> memotion-dataset-7k\n\t--> imagenetsketch\n\t--> pixiv-popular-illustrations\n\t--> caltech256\n\t--> toy-car-dataset-lear\n\t--> objectnet-1-of-10\n\t--> imagenetmini-1000\n\t--> food-recognition-2022\n\t--> the-met-dataset\n\t--> shopee-product-matching\n\t--> google-universal-image-embedding\n\t--> landmark-recognition-2021\n\t--> best-artworks-of-all-time\n\t--> imaterialist-fashion-2020-fgvc7\n\t--> guie-toys-dataset\n\t--> furniture-images-dataset\n\t--> the-car-connection-picture-dataset\n\n\n... DATA ACCESS SETUP COMPLETED ...\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"
\n\n

2.3 LEVERAGING XLA OPTIMIZATIONS

\n\n---\n\n\n**XLA** (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes. **The results are improvements in speed and memory usage**.\n\n
\n\nWhen a TensorFlow program is run, all of the operations are executed individually by the TensorFlow executor. Each TensorFlow operation has a precompiled GPU/TPU kernel implementation that the executor dispatches to.\n\nXLA provides us with an alternative mode of running models: it compiles the TensorFlow graph into a sequence of computation kernels generated specifically for the given model. Because these kernels are unique to the model, they can exploit model-specific information for optimization.

\n\n
\n πŸ›‘   WARNING:

- XLA can not currently compile functions where dimensions are not inferrable: that is, if it's not possible to infer the dimensions of all tensors without running the entire computation
\n
\n\n
\n πŸ“Œ   NOTE:

- XLA compilation is only applied to code that is compiled into a graph (in TF2 that's only a code inside tf.function).
- The jit_compile API has must-compile semantics, i.e. either the entire function is compiled with XLA, or an errors.InvalidArgumentError exception is thrown)\n
\n\n
\n πŸ“–   REFERENCE:

- XLA: Optimizing Compiler for Machine Learning
\n
","metadata":{}},{"cell_type":"code","source":"print(f\"\\n... XLA OPTIMIZATIONS STARTING ...\\n\")\n\nprint(f\"\\n... CONFIGURE JIT (JUST IN TIME) COMPILATION ...\\n\")\n# enable XLA optmizations (10% speedup when using @tf.function calls)\ntf.config.optimizer.set_jit(True)\n\nprint(f\"\\n... XLA OPTIMIZATIONS COMPLETED ...\\n\")","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:22:47.481816Z","iopub.execute_input":"2023-04-19T02:22:47.482235Z","iopub.status.idle":"2023-04-19T02:22:47.49138Z","shell.execute_reply.started":"2023-04-19T02:22:47.4822Z","shell.execute_reply":"2023-04-19T02:22:47.490115Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"\n... XLA OPTIMIZATIONS STARTING ...\n\n\n... CONFIGURE JIT (JUST IN TIME) COMPILATION ...\n\n\n... XLA OPTIMIZATIONS COMPLETED ...\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"
\n\n

2.4 FILE PATH AND METADATA

\n\n---\n","metadata":{}},{"cell_type":"code","source":"print(\"\\n... BASIC DATA SETUP STARTING ...\\n\\n\")\ndataset_dir_map = {_ds:os.path.join(INPUT_DIR, _ds) for _ds in os.listdir(INPUT_DIR) if \"universal-image\" not in _ds}\nfor k,v in dataset_dir_map.items(): print(f\"{k:>40} --> {v}\")\n\n\nprint(\"\\n... BASIC DATA SETUP FINISHED ...\\n\\n\")","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:22:47.492837Z","iopub.execute_input":"2023-04-19T02:22:47.493906Z","iopub.status.idle":"2023-04-19T02:22:47.502948Z","shell.execute_reply.started":"2023-04-19T02:22:47.49386Z","shell.execute_reply":"2023-04-19T02:22:47.501981Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"\n... BASIC DATA SETUP STARTING ...\n\n\n memotion-dataset-7k --> /kaggle/input/memotion-dataset-7k\n imagenetsketch --> /kaggle/input/imagenetsketch\n pixiv-popular-illustrations --> /kaggle/input/pixiv-popular-illustrations\n caltech256 --> /kaggle/input/caltech256\n toy-car-dataset-lear --> /kaggle/input/toy-car-dataset-lear\n objectnet-1-of-10 --> /kaggle/input/objectnet-1-of-10\n imagenetmini-1000 --> /kaggle/input/imagenetmini-1000\n food-recognition-2022 --> /kaggle/input/food-recognition-2022\n the-met-dataset --> /kaggle/input/the-met-dataset\n shopee-product-matching --> /kaggle/input/shopee-product-matching\n landmark-recognition-2021 --> /kaggle/input/landmark-recognition-2021\n best-artworks-of-all-time --> /kaggle/input/best-artworks-of-all-time\n imaterialist-fashion-2020-fgvc7 --> /kaggle/input/imaterialist-fashion-2020-fgvc7\n guie-toys-dataset --> /kaggle/input/guie-toys-dataset\n furniture-images-dataset --> /kaggle/input/furniture-images-dataset\n the-car-connection-picture-dataset --> /kaggle/input/the-car-connection-picture-dataset\n\n... BASIC DATA SETUP FINISHED ...\n\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**HELPERS**","metadata":{}},{"cell_type":"code","source":"def load_json_to_dict(json_path):\n \"\"\" tbd \"\"\"\n with open(json_path) as json_file:\n data = json.load(json_file)\n return data\n\ndef flatten_l_o_l(nested_list):\n \"\"\" Flatten a list of lists \"\"\"\n return [item for sublist in nested_list for item in sublist]\n\ndef print_ln(symbol=\"-\", line_len=110):\n print(symbol*line_len)","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:22:47.504883Z","iopub.execute_input":"2023-04-19T02:22:47.505225Z","iopub.status.idle":"2023-04-19T02:22:47.513424Z","shell.execute_reply.started":"2023-04-19T02:22:47.505193Z","shell.execute_reply":"2023-04-19T02:22:47.512616Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"
\n\n**INITIALIZE MASTER DATAFRAME**","metadata":{}},{"cell_type":"code","source":"master_df = pd.DataFrame({\n \"img_path\":[],\n \"supercls\":[],\n \"cls\":[],\n \"dataset\":[],\n \"val_only\":[],\n})\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:22:47.51486Z","iopub.execute_input":"2023-04-19T02:22:47.515482Z","iopub.status.idle":"2023-04-19T02:22:47.548348Z","shell.execute_reply.started":"2023-04-19T02:22:47.515449Z","shell.execute_reply":"2023-04-19T02:22:47.547203Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"Empty DataFrame\nColumns: [img_path, supercls, cls, dataset, val_only]\nIndex: []","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n
img_pathsuperclsclsdatasetval_only
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"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**FASHION FROM IMATERIALIST 2020**\n* Take 5000","metadata":{}},{"cell_type":"code","source":"# Class mapping\nimaterialist_fashion_2020_class_map = load_json_to_dict(\"../input/imaterialist-fashion-2020-fgvc7/label_descriptions.json\")\nimaterialist_fashion_2020_class_map = {_cls[\"id\"]:_cls[\"name\"] for _cls in imaterialist_fashion_2020_class_map[\"categories\"]}\n\n# How many to take\nimaterialist_n = 5_000\n\n# Make temporary dataframe\ntmp_df = pd.read_csv(os.path.join(INPUT_DIR, \"imaterialist-fashion-2020-fgvc7\", \"train.csv\"))\ntmp_df[\"img_path\"] = \"../input/imaterialist-fashion-2020-fgvc7/train/\"+tmp_df[\"ImageId\"]+\".jpg\"\ntmp_df[\"cls\"] = tmp_df[\"ClassId\"].map(imaterialist_fashion_2020_class_map)\n\ntmp_df[\"cls_cnt\"] = tmp_df.groupby(\"cls\")[\"cls\"].transform(\"count\")\ntmp_df = tmp_df.sort_values(by=\"cls_cnt\", ascending=False).reset_index(drop=True).iloc[:100000]\ntmp_df = tmp_df.sample(imaterialist_n).reset_index(drop=True)\n\ntmp_df = tmp_df[[\"img_path\", \"cls\"]]\ntmp_df[\"supercls\"] = \"apparel & accessories\"\ntmp_df[\"dataset\"] = \"imaterialist-fashion-2020-fgvc7\"\ntmp_df[\"val_only\"] = False\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:22:47.550067Z","iopub.execute_input":"2023-04-19T02:22:47.550594Z","iopub.status.idle":"2023-04-19T02:23:19.746524Z","shell.execute_reply.started":"2023-04-19T02:22:47.550546Z","shell.execute_reply":"2023-04-19T02:23:19.745234Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":" img_path \\\n0 ../input/imaterialist-fashion-2020-fgvc7/train... \n1 ../input/imaterialist-fashion-2020-fgvc7/train... \n2 ../input/imaterialist-fashion-2020-fgvc7/train... \n3 ../input/imaterialist-fashion-2020-fgvc7/train... \n4 ../input/imaterialist-fashion-2020-fgvc7/train... \n... ... \n4995 ../input/imaterialist-fashion-2020-fgvc7/train... \n4996 ../input/imaterialist-fashion-2020-fgvc7/train... \n4997 ../input/imaterialist-fashion-2020-fgvc7/train... \n4998 ../input/imaterialist-fashion-2020-fgvc7/train... \n4999 ../input/imaterialist-fashion-2020-fgvc7/train... \n\n supercls cls dataset val_only \n0 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 0.0 \n1 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 0.0 \n2 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 0.0 \n3 apparel & accessories shoe imaterialist-fashion-2020-fgvc7 0.0 \n4 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 0.0 \n... ... ... ... ... \n4995 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 0.0 \n4996 apparel & accessories shoe imaterialist-fashion-2020-fgvc7 0.0 \n4997 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 0.0 \n4998 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 0.0 \n4999 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 0.0 \n\n[5000 rows x 5 columns]","text/html":"
\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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
img_pathsuperclsclsdatasetval_only
0../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
1../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
2../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
3../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
..................
4995../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
4996../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4997../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
4998../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
4999../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
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5000 rows Γ— 5 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**FURNITURE IMAGE DATASET**\n* Take 5000","metadata":{}},{"cell_type":"code","source":"# How many to take\nfurniture_n = 5_000\n\n# Make temporary dataframe\ntmp_df = pd.read_csv(os.path.join(INPUT_DIR, \"furniture-images-dataset\", \"furniture_data_img.csv\"))\ntmp_df[\"img_path\"] = \"/kaggle/input/furniture-images-dataset/furniture_images\"+ tmp_df[\"Image_File\"]\ntmp_df[\"cls\"] = tmp_df[\"Furniture_Type\"]\ntmp_df[\"cls_cnt\"] = tmp_df.groupby(\"cls\")[\"cls\"].transform(\"count\")\ntmp_df = tmp_df.sort_values(by=\"cls_cnt\", ascending=False)\ntmp_df = tmp_df.head(furniture_n).sample(imaterialist_n).reset_index(drop=True)\n\ntmp_df = tmp_df[[\"img_path\", \"cls\"]]\ntmp_df[\"supercls\"] = \"furniture\"\ntmp_df[\"dataset\"] = \"furniture-images-dataset\"\ntmp_df[\"val_only\"] = False\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:23:19.74795Z","iopub.execute_input":"2023-04-19T02:23:19.748623Z","iopub.status.idle":"2023-04-19T02:23:19.817056Z","shell.execute_reply.started":"2023-04-19T02:23:19.748587Z","shell.execute_reply":"2023-04-19T02:23:19.816172Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":" img_path \\\n0 ../input/imaterialist-fashion-2020-fgvc7/train... \n1 ../input/imaterialist-fashion-2020-fgvc7/train... \n2 ../input/imaterialist-fashion-2020-fgvc7/train... \n3 ../input/imaterialist-fashion-2020-fgvc7/train... \n4 ../input/imaterialist-fashion-2020-fgvc7/train... \n... ... \n9995 /kaggle/input/furniture-images-dataset/furnitu... \n9996 /kaggle/input/furniture-images-dataset/furnitu... \n9997 /kaggle/input/furniture-images-dataset/furnitu... \n9998 /kaggle/input/furniture-images-dataset/furnitu... \n9999 /kaggle/input/furniture-images-dataset/furnitu... \n\n supercls cls \\\n0 apparel & accessories sleeve \n1 apparel & accessories sleeve \n2 apparel & accessories sleeve \n3 apparel & accessories shoe \n4 apparel & accessories sleeve \n... ... ... \n9995 furniture Bed / bedroom item \n9996 furniture Table / chair \n9997 furniture Table / chair \n9998 furniture Table / chair \n9999 furniture Table / chair \n\n dataset val_only \n0 imaterialist-fashion-2020-fgvc7 0.0 \n1 imaterialist-fashion-2020-fgvc7 0.0 \n2 imaterialist-fashion-2020-fgvc7 0.0 \n3 imaterialist-fashion-2020-fgvc7 0.0 \n4 imaterialist-fashion-2020-fgvc7 0.0 \n... ... ... \n9995 furniture-images-dataset 0.0 \n9996 furniture-images-dataset 0.0 \n9997 furniture-images-dataset 0.0 \n9998 furniture-images-dataset 0.0 \n9999 furniture-images-dataset 0.0 \n\n[10000 rows x 5 columns]","text/html":"
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img_pathsuperclsclsdatasetval_only
0../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
1../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
2../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
3../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
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9995/kaggle/input/furniture-images-dataset/furnitu...furnitureBed / bedroom itemfurniture-images-dataset0.0
9996/kaggle/input/furniture-images-dataset/furnitu...furnitureTable / chairfurniture-images-dataset0.0
9997/kaggle/input/furniture-images-dataset/furnitu...furnitureTable / chairfurniture-images-dataset0.0
9998/kaggle/input/furniture-images-dataset/furnitu...furnitureTable / chairfurniture-images-dataset0.0
9999/kaggle/input/furniture-images-dataset/furnitu...furnitureTable / chairfurniture-images-dataset0.0
\n

10000 rows Γ— 5 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**SHOPEE PRODUCT DATASET**\n* Take 2500","metadata":{}},{"cell_type":"code","source":"# How many to take\nshopee_n = 2_500\n\n# Make temporary dataframe\ntmp_df = pd.read_csv(os.path.join(INPUT_DIR, \"shopee-product-matching\", \"train.csv\"))\ncnt_map = tmp_df.label_group.value_counts().to_dict()\ntmp_df[\"lbl_group_cnt\"] = tmp_df.label_group.map(cnt_map)\ntmp_df = tmp_df.sort_values(by=[\"lbl_group_cnt\", \"label_group\"], ascending=False).reset_index(drop=True).head(shopee_n)\ntmp_df[\"img_path\"] = \"/kaggle/input/shopee-product-matching/train_images/\"+ tmp_df[\"image\"]\n\ntmp_df[\"cls\"] = tmp_df[\"label_group\"]\ntmp_df = tmp_df[[\"img_path\", \"cls\"]]\ntmp_df[\"supercls\"] = \"packaged goods\"\ntmp_df[\"dataset\"] = \"shopee-product-matching\"\ntmp_df[\"val_only\"] = False\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:23:19.820765Z","iopub.execute_input":"2023-04-19T02:23:19.821453Z","iopub.status.idle":"2023-04-19T02:23:20.062405Z","shell.execute_reply.started":"2023-04-19T02:23:19.821414Z","shell.execute_reply":"2023-04-19T02:23:20.061294Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":" img_path \\\n0 ../input/imaterialist-fashion-2020-fgvc7/train... \n1 ../input/imaterialist-fashion-2020-fgvc7/train... \n2 ../input/imaterialist-fashion-2020-fgvc7/train... \n3 ../input/imaterialist-fashion-2020-fgvc7/train... \n4 ../input/imaterialist-fashion-2020-fgvc7/train... \n... ... \n12495 /kaggle/input/shopee-product-matching/train_im... \n12496 /kaggle/input/shopee-product-matching/train_im... \n12497 /kaggle/input/shopee-product-matching/train_im... \n12498 /kaggle/input/shopee-product-matching/train_im... \n12499 /kaggle/input/shopee-product-matching/train_im... \n\n supercls cls dataset \\\n0 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n1 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n2 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n3 apparel & accessories shoe imaterialist-fashion-2020-fgvc7 \n4 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n... ... ... ... \n12495 packaged goods 2315452163 shopee-product-matching \n12496 packaged goods 2315452163 shopee-product-matching \n12497 packaged goods 2315452163 shopee-product-matching \n12498 packaged goods 2315452163 shopee-product-matching \n12499 packaged goods 2315452163 shopee-product-matching \n\n val_only \n0 0.0 \n1 0.0 \n2 0.0 \n3 0.0 \n4 0.0 \n... ... \n12495 0.0 \n12496 0.0 \n12497 0.0 \n12498 0.0 \n12499 0.0 \n\n[12500 rows x 5 columns]","text/html":"
\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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
img_pathsuperclsclsdatasetval_only
0../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
1../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
2../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
3../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
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12495/kaggle/input/shopee-product-matching/train_im...packaged goods2315452163shopee-product-matching0.0
12496/kaggle/input/shopee-product-matching/train_im...packaged goods2315452163shopee-product-matching0.0
12497/kaggle/input/shopee-product-matching/train_im...packaged goods2315452163shopee-product-matching0.0
12498/kaggle/input/shopee-product-matching/train_im...packaged goods2315452163shopee-product-matching0.0
12499/kaggle/input/shopee-product-matching/train_im...packaged goods2315452163shopee-product-matching0.0
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12500 rows Γ— 5 columns

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"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**CAR CONNECTION DATASET**\n* Take 5000","metadata":{}},{"cell_type":"code","source":"n_cc = 5000\n\ntmp_df = pd.DataFrame({\"img_path\":glob(\"/kaggle/input/the-car-connection-picture-dataset/*.jpg\")})\ntmp_df[\"cls\"] = tmp_df[\"img_path\"].progress_apply(lambda x: \"_\".join(x.rsplit(\"/\", 1)[-1].split(\"_\",2)[:2]))\n\ntmp_df[\"cls_cnt\"] = tmp_df.groupby(\"cls\")[\"cls\"].transform(\"count\")\ntmp_df = tmp_df.sort_values(by=\"cls_cnt\", ascending=False)\ntmp_df = tmp_df.head(n_cc).sample(n_cc).reset_index(drop=True)\ntmp_df = tmp_df[[\"img_path\", \"cls\"]]\n\ntmp_df[\"supercls\"] = \"cars\"\ntmp_df[\"dataset\"] = \"the-car-connectino-picture-dataset\"\ntmp_df[\"val_only\"] = False\ntmp_df\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:23:20.063916Z","iopub.execute_input":"2023-04-19T02:23:20.064362Z","iopub.status.idle":"2023-04-19T02:23:29.834811Z","shell.execute_reply.started":"2023-04-19T02:23:20.064317Z","shell.execute_reply":"2023-04-19T02:23:29.833369Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":" 0%| | 0/64467 [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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
img_pathsuperclsclsdatasetval_only
0../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
1../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
2../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
3../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
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17495/kaggle/input/the-car-connection-picture-datas...carsFord_Rangerthe-car-connectino-picture-dataset0.0
17496/kaggle/input/the-car-connection-picture-datas...carsPorsche_911the-car-connectino-picture-dataset0.0
17497/kaggle/input/the-car-connection-picture-datas...carsMINI_Cooperthe-car-connectino-picture-dataset0.0
17498/kaggle/input/the-car-connection-picture-datas...carsMINI_Cooperthe-car-connectino-picture-dataset0.0
17499/kaggle/input/the-car-connection-picture-datas...carsFord_Rangerthe-car-connectino-picture-dataset0.0
\n

17500 rows Γ— 5 columns

\n"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**CALTECH-256**\n* Take 10_000","metadata":{}},{"cell_type":"code","source":"n_caltech = 10_000\ntmp_df = pd.DataFrame({\"img_path\":tf.io.gfile.glob(os.path.join(INPUT_DIR, \"caltech256\", \"**\", \"**\", \"*.jpg\"))})\ntmp_df[\"cls\"] = tmp_df[\"img_path\"].apply(lambda x: x.rsplit(\"/\", 2)[1][4:])\n\ntmp_df[\"cls_cnt\"] = tmp_df.groupby(\"cls\")[\"cls\"].transform(\"count\")\ntmp_df = tmp_df.sort_values(by=\"cls_cnt\", ascending=False)\ntmp_df = tmp_df.head(n_caltech).sample(n_caltech).reset_index(drop=True)\ntmp_df = tmp_df[[\"img_path\", \"cls\"]]\n\ntmp_df[\"supercls\"] = \"other\"\ntmp_df[\"dataset\"] = \"caltech256\"\ntmp_df[\"val_only\"] = False\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:23:29.836428Z","iopub.execute_input":"2023-04-19T02:23:29.836917Z","iopub.status.idle":"2023-04-19T02:23:33.876911Z","shell.execute_reply.started":"2023-04-19T02:23:29.836885Z","shell.execute_reply":"2023-04-19T02:23:33.87561Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":" img_path \\\n0 ../input/imaterialist-fashion-2020-fgvc7/train... \n1 ../input/imaterialist-fashion-2020-fgvc7/train... \n2 ../input/imaterialist-fashion-2020-fgvc7/train... \n3 ../input/imaterialist-fashion-2020-fgvc7/train... \n4 ../input/imaterialist-fashion-2020-fgvc7/train... \n... ... \n27495 /kaggle/input/caltech256/256_ObjectCategories/... \n27496 /kaggle/input/caltech256/256_ObjectCategories/... \n27497 /kaggle/input/caltech256/256_ObjectCategories/... \n27498 /kaggle/input/caltech256/256_ObjectCategories/... \n27499 /kaggle/input/caltech256/256_ObjectCategories/... \n\n supercls cls dataset \\\n0 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n1 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n2 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n3 apparel & accessories shoe imaterialist-fashion-2020-fgvc7 \n4 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n... ... ... ... \n27495 other faces-easy-101 caltech256 \n27496 other minaret caltech256 \n27497 other gorilla caltech256 \n27498 other horse caltech256 \n27499 other bonsai-101 caltech256 \n\n val_only \n0 0.0 \n1 0.0 \n2 0.0 \n3 0.0 \n4 0.0 \n... ... \n27495 0.0 \n27496 0.0 \n27497 0.0 \n27498 0.0 \n27499 0.0 \n\n[27500 rows x 5 columns]","text/html":"
\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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
img_pathsuperclsclsdatasetval_only
0../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
1../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
2../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
3../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
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27495/kaggle/input/caltech256/256_ObjectCategories/...otherfaces-easy-101caltech2560.0
27496/kaggle/input/caltech256/256_ObjectCategories/...otherminaretcaltech2560.0
27497/kaggle/input/caltech256/256_ObjectCategories/...othergorillacaltech2560.0
27498/kaggle/input/caltech256/256_ObjectCategories/...otherhorsecaltech2560.0
27499/kaggle/input/caltech256/256_ObjectCategories/...otherbonsai-101caltech2560.0
\n

27500 rows Γ— 5 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**Food Recognition 2022**\n* Take 5000","metadata":{}},{"cell_type":"code","source":"n_food = 5000\nfood_rec_meta = load_json_to_dict(\"../input/food-recognition-2022/raw_data/public_training_set_release_2.0/annotations.json\")\nfood_cat_map = {x[\"id\"]:x[\"name\"] for x in food_rec_meta[\"categories\"]}\nimage_id_map = {x[\"image_id\"]:food_cat_map[x[\"category_id\"]] for x in food_rec_meta[\"annotations\"]}\n\ntmp_df = pd.DataFrame({\"img_path\":tf.io.gfile.glob(os.path.join(INPUT_DIR, \"food-recognition-2022\", \"raw_data\", \"public_training_set_release_2.0\", \"images\", \"*.jpg\"))})\ntmp_df[\"cls\"]=tmp_df.img_path.progress_apply(lambda x: image_id_map[int(x.rsplit(\"/\", 1)[-1][:-4])])\n\ntmp_df[\"cls_cnt\"] = tmp_df.groupby(\"cls\")[\"cls\"].transform(\"count\")\ntmp_df = tmp_df.sort_values(by=\"cls_cnt\", ascending=False)\ntmp_df = tmp_df.head(n_food*2).sample(n_food).reset_index(drop=True)\ntmp_df = tmp_df[[\"img_path\", \"cls\"]]\n\ntmp_df[\"supercls\"] = \"dishes\"\ntmp_df[\"dataset\"] = \"food-recognition-2022\"\ntmp_df[\"val_only\"] = False\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:23:33.878717Z","iopub.execute_input":"2023-04-19T02:23:33.880141Z","iopub.status.idle":"2023-04-19T02:23:55.462238Z","shell.execute_reply.started":"2023-04-19T02:23:33.879978Z","shell.execute_reply":"2023-04-19T02:23:55.46067Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":" 0%| | 0/39962 [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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
img_pathsuperclsclsdatasetval_only
0../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
1../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
2../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
3../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
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32495/kaggle/input/food-recognition-2022/raw_data/p...disheswine-redfood-recognition-20220.0
32496/kaggle/input/food-recognition-2022/raw_data/p...disheswine-redfood-recognition-20220.0
32497/kaggle/input/food-recognition-2022/raw_data/p...dishesbutterfood-recognition-20220.0
32498/kaggle/input/food-recognition-2022/raw_data/p...disheswaterfood-recognition-20220.0
32499/kaggle/input/food-recognition-2022/raw_data/p...dishestomato-rawfood-recognition-20220.0
\n

32500 rows Γ— 5 columns

\n"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**Google Landmarks Dataset**\n* Take 5000","metadata":{}},{"cell_type":"code","source":"n_landmark = 5_000\ntmp_df = pd.read_csv(os.path.join(INPUT_DIR, \"landmark-recognition-2021\", \"train.csv\"))\ntmp_df[\"landmark_cnt\"] = tmp_df.groupby(\"landmark_id\")[\"landmark_id\"].transform(\"count\")\ntmp_df = tmp_df.sort_values(by=\"landmark_cnt\", ascending=False)\ntmp_df = tmp_df.reset_index(drop=True)\ntmp_df = tmp_df.head(100_000)\ntmp_df = tmp_df.sample(n_landmark).reset_index(drop=True)\ntmp_df[\"img_path\"] = \"/kaggle/input/landmark-recognition-2021/train/\"+\\\n tmp_df[\"id\"].str[0]+\"/\"+tmp_df[\"id\"].str[1]+\"/\"+tmp_df[\"id\"].str[2]\\\n +\"/\"+tmp_df[\"id\"]+\".jpg\"\ntmp_df[\"cls\"] = \"landmark_\"+tmp_df[\"landmark_id\"].astype(str)\ntmp_df = tmp_df[[\"img_path\", \"cls\"]]\ntmp_df[\"supercls\"] = \"landmarks\"\ntmp_df[\"dataset\"] = \"landmark-recognition-2021\"\ntmp_df[\"val_only\"] = False\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:23:55.464082Z","iopub.execute_input":"2023-04-19T02:23:55.464758Z","iopub.status.idle":"2023-04-19T02:23:57.564671Z","shell.execute_reply.started":"2023-04-19T02:23:55.464715Z","shell.execute_reply":"2023-04-19T02:23:57.563452Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":" img_path \\\n0 ../input/imaterialist-fashion-2020-fgvc7/train... \n1 ../input/imaterialist-fashion-2020-fgvc7/train... \n2 ../input/imaterialist-fashion-2020-fgvc7/train... \n3 ../input/imaterialist-fashion-2020-fgvc7/train... \n4 ../input/imaterialist-fashion-2020-fgvc7/train... \n... ... \n37495 /kaggle/input/landmark-recognition-2021/train/... \n37496 /kaggle/input/landmark-recognition-2021/train/... \n37497 /kaggle/input/landmark-recognition-2021/train/... \n37498 /kaggle/input/landmark-recognition-2021/train/... \n37499 /kaggle/input/landmark-recognition-2021/train/... \n\n supercls cls \\\n0 apparel & accessories sleeve \n1 apparel & accessories sleeve \n2 apparel & accessories sleeve \n3 apparel & accessories shoe \n4 apparel & accessories sleeve \n... ... ... \n37495 landmarks landmark_177870 \n37496 landmarks landmark_102850 \n37497 landmarks landmark_168098 \n37498 landmarks landmark_102850 \n37499 landmarks landmark_138982 \n\n dataset val_only \n0 imaterialist-fashion-2020-fgvc7 0.0 \n1 imaterialist-fashion-2020-fgvc7 0.0 \n2 imaterialist-fashion-2020-fgvc7 0.0 \n3 imaterialist-fashion-2020-fgvc7 0.0 \n4 imaterialist-fashion-2020-fgvc7 0.0 \n... ... ... \n37495 landmark-recognition-2021 0.0 \n37496 landmark-recognition-2021 0.0 \n37497 landmark-recognition-2021 0.0 \n37498 landmark-recognition-2021 0.0 \n37499 landmark-recognition-2021 0.0 \n\n[37500 rows x 5 columns]","text/html":"
\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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
img_pathsuperclsclsdatasetval_only
0../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
1../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
2../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
3../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
..................
37495/kaggle/input/landmark-recognition-2021/train/...landmarkslandmark_177870landmark-recognition-20210.0
37496/kaggle/input/landmark-recognition-2021/train/...landmarkslandmark_102850landmark-recognition-20210.0
37497/kaggle/input/landmark-recognition-2021/train/...landmarkslandmark_168098landmark-recognition-20210.0
37498/kaggle/input/landmark-recognition-2021/train/...landmarkslandmark_102850landmark-recognition-20210.0
37499/kaggle/input/landmark-recognition-2021/train/...landmarkslandmark_138982landmark-recognition-20210.0
\n

37500 rows Γ— 5 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**ImageNetMini 1000**\n* Take 5000","metadata":{}},{"cell_type":"code","source":"n_imagenetmini = 5000\nimagenet_cls_map = {'n02119789': 'kit fox, Vulpes macrotis', 'n02100735': 'English setter', 'n02096294': 'Australian terrier', 'n02066245': 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus', 'n02509815': 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens', 'n02124075': 'Egyptian cat', 'n02417914': 'ibex, Capra ibex', 'n02123394': 'Persian cat', 'n02125311': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor', 'n02423022': 'gazelle', 'n02346627': 'porcupine, hedgehog', 'n02077923': 'sea lion', 'n02447366': 'badger', 'n02109047': 'Great Dane', 'n02092002': 'Scottish deerhound, deerhound', 'n02071294': 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca', 'n02442845': 'mink', 'n02504458': 'African elephant, Loxodonta africana', 'n02114712': 'red wolf, maned wolf, Canis rufus, Canis niger', 'n02128925': 'jaguar, panther, Panthera onca, Felis onca', 'n02117135': 'hyena, hyaena', 'n02493509': 'titi, titi monkey', 'n02457408': 'three-toed sloth, ai, Bradypus tridactylus', 'n02389026': 'sorrel', 'n02443484': 'black-footed ferret, ferret, Mustela nigripes', 'n02110341': 'dalmatian, coach dog, carriage dog', 'n02093256': 'Staffordshire bullterrier, Staffordshire bull terrier', 'n02106382': 'Bouvier des Flandres, Bouviers des Flandres', 'n02441942': 'weasel', 'n02113712': 'miniature poodle', 'n02415577': 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis', 'n02356798': 'fox squirrel, eastern fox squirrel, Sciurus niger', 'n02488702': 'colobus, colobus monkey', 'n02123159': 'tiger cat', 'n02422699': 'impala, Aepyceros melampus', 'n02114855': 'coyote, prairie wolf, brush wolf, Canis latrans', 'n02094433': 'Yorkshire terrier', 'n02111277': 'Newfoundland, Newfoundland dog', 'n02119022': 'red fox, Vulpes vulpes', 'n02422106': 'hartebeest', 'n02120505': 'grey fox, gray fox, Urocyon cinereoargenteus', 'n02086079': 'Pekinese, Pekingese, Peke', 'n02484975': 'guenon, guenon monkey', 'n02137549': 'mongoose', 'n02500267': 'indri, indris, Indri indri, Indri brevicaudatus', 'n02129604': 'tiger, Panthera tigris', 'n02396427': 'wild boar, boar, Sus scrofa', 'n02391049': 'zebra', 'n02412080': 'ram, tup', 'n02480495': 'orangutan, orang, orangutang, Pongo pygmaeus', 'n02110806': 'basenji', 'n02128385': 'leopard, Panthera pardus', 'n02100583': 'vizsla, Hungarian pointer', 'n02494079': 'squirrel monkey, Saimiri sciureus', 'n02123597': 'Siamese cat, Siamese', 'n02481823': 'chimpanzee, chimp, Pan troglodytes', 'n02105505': 'komondor', 'n02489166': 'proboscis monkey, Nasalis larvatus', 'n02364673': 'guinea pig, Cavia cobaya', 'n02114548': 'white wolf, Arctic wolf, Canis lupus tundrarum', 'n02134084': 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus', 'n02480855': 'gorilla, Gorilla gorilla', 'n02403003': 'ox', 'n02108551': 'Tibetan mastiff', 'n02493793': 'spider monkey, Ateles geoffroyi', 'n02107142': 'Doberman, Doberman pinscher', 'n02397096': 'warthog', 'n02437312': 'Arabian camel, dromedary, Camelus dromedarius', 'n02483708': 'siamang, Hylobates syndactylus, Symphalangus syndactylus', 'n02099601': 'golden retriever', 'n02106166': 'Border collie', 'n02326432': 'hare', 'n02108089': 'boxer', 'n02486261': 'patas, hussar monkey, Erythrocebus patas', 'n02486410': 'baboon', 'n02487347': 'macaque', 'n02492035': 'capuchin, ringtail, Cebus capucinus', 'n02099267': 'flat-coated retriever', 'n02395406': 'hog, pig, grunter, squealer, Sus scrofa', 'n02109961': 'Eskimo dog, husky', 'n02101388': 'Brittany spaniel', 'n03187595': 'dial telephone, dial phone', 'n03733281': 'maze, labyrinth', 'n02101006': 'Gordon setter', 'n02115641': 'dingo, warrigal, warragal, Canis dingo', 'n02342885': 'hamster', 'n02120079': 'Arctic fox, white fox, Alopex lagopus', 'n02408429': 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis', 'n02133161': 'American black bear, black bear, Ursus americanus, Euarctos americanus', 'n02328150': 'Angora, Angora rabbit', 'n02410509': 'bison', 'n02492660': 'howler monkey, howler', 'n02398521': 'hippopotamus, hippo, river horse, Hippopotamus amphibius', 'n02510455': 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca', 'n02123045': 'tabby, tabby cat', 'n02490219': 'marmoset', 'n02109525': 'Saint Bernard, St Bernard', 'n02454379': 'armadillo', 'n02090379': 'redbone', 'n02443114': 'polecat, fitch, foulmart, foumart, Mustela putorius', 'n02361337': 'marmot', 'n02483362': 'gibbon, Hylobates lar', 'n02437616': 'llama', 'n02325366': 'wood rabbit, cottontail, cottontail rabbit', 'n02129165': 'lion, king of beasts, Panthera leo', 'n02100877': 'Irish setter, red setter', 'n02074367': 'dugong, Dugong dugon', 'n02504013': 'Indian elephant, Elephas maximus', 'n02363005': 'beaver', 'n02497673': 'Madagascar cat, ring-tailed lemur, Lemur catta', 'n02087394': 'Rhodesian ridgeback', 'n02127052': 'lynx, catamount', 'n02116738': 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus', 'n02488291': 'langur', 'n02114367': 'timber wolf, grey wolf, gray wolf, Canis lupus', 'n02130308': 'cheetah, chetah, Acinonyx jubatus', 'n02134418': 'sloth bear, Melursus ursinus, Ursus ursinus', 'n02106662': 'German shepherd, German shepherd dog, German police dog, alsatian', 'n02444819': 'otter', 'n01882714': 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus', 'n01871265': 'tusker', 'n01872401': 'echidna, spiny anteater, anteater', 'n01877812': 'wallaby, brush kangaroo', 'n01873310': 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus', 'n01883070': 'wombat', 'n04086273': 'revolver, six-gun, six-shooter', 'n04507155': 'umbrella', 'n04147183': 'schooner', 'n04254680': 'soccer ball', 'n02672831': 'accordion, piano accordion, squeeze box', 'n02219486': 'ant, emmet, pismire', 'n02317335': 'starfish, sea star', 'n01968897': 'chambered nautilus, pearly nautilus, nautilus', 'n03452741': 'grand piano, grand', 'n03642806': 'laptop, laptop computer', 'n07745940': 'strawberry', 'n02690373': 'airliner', 'n04552348': 'warplane, military plane', 'n02692877': 'airship, dirigible', 'n02782093': 'balloon', 'n04266014': 'space shuttle', 'n03344393': 'fireboat', 'n03447447': 'gondola', 'n04273569': 'speedboat', 'n03662601': 'lifeboat', 'n02951358': 'canoe', 'n04612504': 'yawl', 'n02981792': 'catamaran', 'n04483307': 'trimaran', 'n03095699': 'container ship, containership, container vessel', 'n03673027': 'liner, ocean liner', 'n03947888': 'pirate, pirate ship', 'n02687172': 'aircraft carrier, carrier, flattop, attack aircraft carrier', 'n04347754': 'submarine, pigboat, sub, U-boat', 'n04606251': 'wreck', 'n03478589': 'half track', 'n04389033': 'tank, army tank, armored combat vehicle, armoured combat vehicle', 'n03773504': 'missile', 'n02860847': 'bobsled, bobsleigh, bob', 'n03218198': 'dogsled, dog sled, dog sleigh', 'n02835271': 'bicycle-built-for-two, tandem bicycle, tandem', 'n03792782': 'mountain bike, all-terrain bike, off-roader', 'n03393912': 'freight car', 'n03895866': 'passenger car, coach, carriage', 'n02797295': 'barrow, garden cart, lawn cart, wheelbarrow', 'n04204347': 'shopping cart', 'n03791053': 'motor scooter, scooter', 'n03384352': 'forklift', 'n03272562': 'electric locomotive', 'n04310018': 'steam locomotive', 'n02704792': 'amphibian, amphibious vehicle', 'n02701002': 'ambulance', 'n02814533': 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon', 'n02930766': 'cab, hack, taxi, taxicab', 'n03100240': 'convertible', 'n03594945': 'jeep, landrover', 'n03670208': 'limousine, limo', 'n03770679': 'minivan', 'n03777568': 'Model T', 'n04037443': 'racer, race car, racing car', 'n04285008': 'sports car, sport car', 'n03444034': 'go-kart', 'n03445924': 'golfcart, golf cart', 'n03785016': 'moped', 'n04252225': 'snowplow, snowplough', 'n03345487': 'fire engine, fire truck', 'n03417042': 'garbage truck, dustcart', 'n03930630': 'pickup, pickup truck', 'n04461696': 'tow truck, tow car, wrecker', 'n04467665': 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi', 'n03796401': 'moving van', 'n03977966': 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria', 'n04065272': 'recreational vehicle, RV, R.V.', 'n04335435': 'streetcar, tram, tramcar, trolley, trolley car', 'n04252077': 'snowmobile', 'n04465501': 'tractor', 'n03776460': 'mobile home, manufactured home', 'n04482393': 'tricycle, trike, velocipede', 'n04509417': 'unicycle, monocycle', 'n03538406': 'horse cart, horse-cart', 'n03788365': 'mosquito net', 'n03868242': 'oxcart', 'n02804414': 'bassinet', 'n03125729': 'cradle', 'n03131574': 'crib, cot', 'n03388549': 'four-poster', 'n02870880': 'bookcase', 'n03018349': 'china cabinet, china closet', 'n03742115': 'medicine chest, medicine cabinet', 'n03016953': 'chiffonier, commode', 'n04380533': 'table lamp', 'n03337140': 'file, file cabinet, filing cabinet', 'n03902125': 'pay-phone, pay-station', 'n03891251': 'park bench', 'n02791124': 'barber chair', 'n04429376': 'throne', 'n03376595': 'folding chair', 'n04099969': 'rocking chair, rocker', 'n04344873': 'studio couch, day bed', 'n04447861': 'toilet seat', 'n03179701': 'desk', 'n03982430': 'pool table, billiard table, snooker table', 'n03201208': 'dining table, board', 'n03290653': 'entertainment center', 'n04550184': 'wardrobe, closet, press', 'n07742313': 'Granny Smith', 'n07747607': 'orange', 'n07749582': 'lemon', 'n07753113': 'fig', 'n07753275': 'pineapple, ananas', 'n07753592': 'banana', 'n07754684': 'jackfruit, jak, jack', 'n07760859': 'custard apple', 'n07768694': 'pomegranate', 'n12267677': 'acorn', 'n12620546': 'hip, rose hip, rosehip', 'n13133613': 'ear, spike, capitulum', 'n11879895': 'rapeseed', 'n12144580': 'corn', 'n12768682': 'buckeye, horse chestnut, conker', 'n03854065': 'organ, pipe organ', 'n04515003': 'upright, upright piano', 'n03017168': 'chime, bell, gong', 'n03249569': 'drum, membranophone, tympan', 'n03447721': 'gong, tam-tam', 'n03720891': 'maraca', 'n03721384': 'marimba, xylophone', 'n04311174': 'steel drum', 'n02787622': 'banjo', 'n02992211': 'cello, violoncello', 'n03637318': 'lampshade, lamp shade', 'n03495258': 'harp', 'n02676566': 'acoustic guitar', 'n03272010': 'electric guitar', 'n03110669': 'cornet, horn, trumpet, trump', 'n03394916': 'French horn, horn', 'n04487394': 'trombone', 'n03494278': 'harmonica, mouth organ, harp, mouth harp', 'n03840681': 'ocarina, sweet potato', 'n03884397': 'panpipe, pandean pipe, syrinx', 'n02804610': 'bassoon', 'n04141076': 'sax, saxophone', 'n03372029': 'flute, transverse flute', 'n11939491': 'daisy', 'n12057211': \"yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum\", 'n09246464': 'cliff, drop, drop-off', 'n09468604': 'valley, vale', 'n09193705': 'alp', 'n09472597': 'volcano', 'n09399592': 'promontory, headland, head, foreland', 'n09421951': 'sandbar, sand bar', 'n09256479': 'coral reef', 'n09332890': 'lakeside, lakeshore', 'n09428293': 'seashore, coast, seacoast, sea-coast', 'n09288635': 'geyser', 'n03498962': 'hatchet', 'n03041632': 'cleaver, meat cleaver, chopper', 'n03658185': 'letter opener, paper knife, paperknife', 'n03954731': \"plane, carpenter's plane, woodworking plane\", 'n03995372': 'power drill', 'n03649909': 'lawn mower, mower', 'n03481172': 'hammer', 'n03109150': 'corkscrew, bottle screw', 'n02951585': 'can opener, tin opener', 'n03970156': \"plunger, plumber's helper\", 'n04154565': 'screwdriver', 'n04208210': 'shovel', 'n03967562': 'plow, plough', 'n03000684': 'chain saw, chainsaw', 'n01514668': 'cock', 'n01514859': 'hen', 'n01518878': 'ostrich, Struthio camelus', 'n01530575': 'brambling, Fringilla montifringilla', 'n01531178': 'goldfinch, Carduelis carduelis', 'n01532829': 'house finch, linnet, Carpodacus mexicanus', 'n01534433': 'junco, snowbird', 'n01537544': 'indigo bunting, indigo finch, indigo bird, Passerina cyanea', 'n01558993': 'robin, American robin, Turdus migratorius', 'n01560419': 'bulbul', 'n01580077': 'jay', 'n01582220': 'magpie', 'n01592084': 'chickadee', 'n01601694': 'water ouzel, dipper', 'n01608432': 'kite', 'n01614925': 'bald eagle, American eagle, Haliaeetus leucocephalus', 'n01616318': 'vulture', 'n01622779': 'great grey owl, great gray owl, Strix nebulosa', 'n01795545': 'black grouse', 'n01796340': 'ptarmigan', 'n01797886': 'ruffed grouse, partridge, Bonasa umbellus', 'n01798484': 'prairie chicken, prairie grouse, prairie fowl', 'n01806143': 'peacock', 'n01806567': 'quail', 'n01807496': 'partridge', 'n01817953': 'African grey, African gray, Psittacus erithacus', 'n01818515': 'macaw', 'n01819313': 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita', 'n01820546': 'lorikeet', 'n01824575': 'coucal', 'n01828970': 'bee eater', 'n01829413': 'hornbill', 'n01833805': 'hummingbird', 'n01843065': 'jacamar', 'n01843383': 'toucan', 'n01847000': 'drake', 'n01855032': 'red-breasted merganser, Mergus serrator', 'n01855672': 'goose', 'n01860187': 'black swan, Cygnus atratus', 'n02002556': 'white stork, Ciconia ciconia', 'n02002724': 'black stork, Ciconia nigra', 'n02006656': 'spoonbill', 'n02007558': 'flamingo', 'n02009912': 'American egret, great white heron, Egretta albus', 'n02009229': 'little blue heron, Egretta caerulea', 'n02011460': 'bittern', 'n02012849': 'crane', 'n02013706': 'limpkin, Aramus pictus', 'n02018207': 'American coot, marsh hen, mud hen, water hen, Fulica americana', 'n02018795': 'bustard', 'n02025239': 'ruddy turnstone, Arenaria interpres', 'n02027492': 'red-backed sandpiper, dunlin, Erolia alpina', 'n02028035': 'redshank, Tringa totanus', 'n02033041': 'dowitcher', 'n02037110': 'oystercatcher, oyster catcher', 'n02017213': 'European gallinule, Porphyrio porphyrio', 'n02051845': 'pelican', 'n02056570': 'king penguin, Aptenodytes patagonica', 'n02058221': 'albatross, mollymawk', 'n01484850': 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', 'n01491361': 'tiger shark, Galeocerdo cuvieri', 'n01494475': 'hammerhead, hammerhead shark', 'n01496331': 'electric ray, crampfish, numbfish, torpedo', 'n01498041': 'stingray', 'n02514041': 'barracouta, snoek', 'n02536864': 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch', 'n01440764': 'tench, Tinca tinca', 'n01443537': 'goldfish, Carassius auratus', 'n02526121': 'eel', 'n02606052': 'rock beauty, Holocanthus tricolor', 'n02607072': 'anemone fish', 'n02643566': 'lionfish', 'n02655020': 'puffer, pufferfish, blowfish, globefish', 'n02640242': 'sturgeon', 'n02641379': 'gar, garfish, garpike, billfish, Lepisosteus osseus', 'n01664065': 'loggerhead, loggerhead turtle, Caretta caretta', 'n01667114': 'mud turtle', 'n01667778': 'terrapin', 'n01669191': 'box turtle, box tortoise', 'n01675722': 'banded gecko', 'n01677366': 'common iguana, iguana, Iguana iguana', 'n01682714': 'American chameleon, anole, Anolis carolinensis', 'n01685808': 'whiptail, whiptail lizard', 'n01687978': 'agama', 'n01688243': 'frilled lizard, Chlamydosaurus kingi', 'n01689811': 'alligator lizard', 'n01692333': 'Gila monster, Heloderma suspectum', 'n01693334': 'green lizard, Lacerta viridis', 'n01694178': 'African chameleon, Chamaeleo chamaeleon', 'n01695060': 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis', 'n01704323': 'triceratops', 'n01697457': 'African crocodile, Nile crocodile, Crocodylus niloticus', 'n01698640': 'American alligator, Alligator mississipiensis', 'n01728572': 'thunder snake, worm snake, Carphophis amoenus', 'n01728920': 'ringneck snake, ring-necked snake, ring snake', 'n01729322': 'hognose snake, puff adder, sand viper', 'n01729977': 'green snake, grass snake', 'n01734418': 'king snake, kingsnake', 'n01735189': 'garter snake, grass snake', 'n01737021': 'water snake', 'n01739381': 'vine snake', 'n01740131': 'night snake, Hypsiglena torquata', 'n01742172': 'boa constrictor, Constrictor constrictor', 'n01744401': 'rock python, rock snake, Python sebae', 'n01748264': 'Indian cobra, Naja naja', 'n01749939': 'green mamba', 'n01751748': 'sea snake', 'n01753488': 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus', 'n04326547': 'stone wall', 'n01756291': 'sidewinder, horned rattlesnake, Crotalus cerastes', 'n01629819': 'European fire salamander, Salamandra salamandra', 'n01630670': 'common newt, Triturus vulgaris', 'n01631663': 'eft', 'n01632458': 'spotted salamander, Ambystoma maculatum', 'n01632777': 'axolotl, mud puppy, Ambystoma mexicanum', 'n01641577': 'bullfrog, Rana catesbeiana', 'n01644373': 'tree frog, tree-frog', 'n01644900': 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui', 'n04579432': 'whistle', 'n04592741': 'wing', 'n03876231': 'paintbrush', 'n03868863': 'oxygen mask', 'n04251144': 'snorkel', 'n03691459': 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system', 'n03759954': 'microphone, mike', 'n04152593': 'screen, CRT screen', 'n03793489': 'mouse, computer mouse', 'n03271574': 'electric fan, blower', 'n03843555': 'oil filter', 'n04332243': 'strainer', 'n04265275': 'space heater', 'n04330267': 'stove', 'n03467068': 'guillotine', 'n02794156': 'barometer', 'n04118776': 'rule, ruler', 'n03841143': 'odometer, hodometer, mileometer, milometer', 'n04141975': 'scale, weighing machine', 'n02708093': 'analog clock', 'n03196217': 'digital clock', 'n04548280': 'wall clock', 'n03544143': 'hourglass', 'n04355338': 'sundial', 'n03891332': 'parking meter', 'n04328186': 'stopwatch, stop watch', 'n03197337': 'digital watch', 'n04317175': 'stethoscope', 'n04376876': 'syringe', 'n03706229': 'magnetic compass', 'n02841315': 'binoculars, field glasses, opera glasses', 'n04009552': 'projector', 'n04356056': 'sunglasses, dark glasses, shades', 'n03692522': \"loupe, jeweler's loupe\", 'n04044716': 'radio telescope, radio reflector', 'n02879718': 'bow', 'n02950826': 'cannon', 'n02749479': 'assault rifle, assault gun', 'n04090263': 'rifle', 'n04008634': 'projectile, missile', 'n03085013': 'computer keyboard, keypad', 'n04505470': 'typewriter keyboard', 'n03126707': 'crane', 'n03666591': 'lighter, light, igniter, ignitor', 'n02666196': 'abacus', 'n02977058': 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM', 'n04238763': 'slide rule, slipstick', 'n03180011': 'desktop computer', 'n03485407': 'hand-held computer, hand-held microcomputer', 'n03832673': 'notebook, notebook computer', 'n03874599': 'padlock', 'n03496892': 'harvester, reaper', 'n04428191': 'thresher, thrasher, threshing machine', 'n04004767': 'printer', 'n04243546': 'slot, one-armed bandit', 'n04525305': 'vending machine', 'n04179913': 'sewing machine', 'n03602883': 'joystick', 'n04372370': 'switch, electric switch, electrical switch', 'n03532672': 'hook, claw', 'n02974003': 'car wheel', 'n03874293': 'paddlewheel, paddle wheel', 'n03944341': 'pinwheel', 'n03992509': \"potter's wheel\", 'n03425413': 'gas pump, gasoline pump, petrol pump, island dispenser', 'n02966193': 'carousel, carrousel, merry-go-round, roundabout, whirligig', 'n04371774': 'swing', 'n04067472': 'reel', 'n04040759': 'radiator', 'n04019541': 'puck, hockey puck', 'n03492542': 'hard disc, hard disk, fixed disk', 'n04355933': 'sunglass', 'n03929660': 'pick, plectrum, plectron', 'n02965783': 'car mirror', 'n04258138': 'solar dish, solar collector, solar furnace', 'n04074963': 'remote control, remote', 'n03208938': 'disk brake, disc brake', 'n02910353': 'buckle', 'n03476684': 'hair slide', 'n03627232': 'knot', 'n03075370': 'combination lock', 'n06359193': 'web site, website, internet site, site', 'n03804744': 'nail', 'n04127249': 'safety pin', 'n04153751': 'screw', 'n03803284': 'muzzle', 'n04162706': 'seat belt, seatbelt', 'n04228054': 'ski', 'n02948072': 'candle, taper, wax light', 'n03590841': \"jack-o'-lantern\", 'n04286575': 'spotlight, spot', 'n04456115': 'torch', 'n03814639': 'neck brace', 'n03933933': 'pier', 'n04485082': 'tripod', 'n03733131': 'maypole', 'n03483316': 'hand blower, blow dryer, blow drier, hair dryer, hair drier', 'n03794056': 'mousetrap', 'n04275548': \"spider web, spider's web\", 'n01768244': 'trilobite', 'n01770081': 'harvestman, daddy longlegs, Phalangium opilio', 'n01770393': 'scorpion', 'n01773157': 'black and gold garden spider, Argiope aurantia', 'n01773549': 'barn spider, Araneus cavaticus', 'n01773797': 'garden spider, Aranea diademata', 'n01774384': 'black widow, Latrodectus mactans', 'n01774750': 'tarantula', 'n01775062': 'wolf spider, hunting spider', 'n01776313': 'tick', 'n01784675': 'centipede', 'n01990800': 'isopod', 'n01978287': 'Dungeness crab, Cancer magister', 'n01978455': 'rock crab, Cancer irroratus', 'n01980166': 'fiddler crab', 'n01981276': 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica', 'n01983481': 'American lobster, Northern lobster, Maine lobster, Homarus americanus', 'n01984695': 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish', 'n01985128': 'crayfish, crawfish, crawdad, crawdaddy', 'n01986214': 'hermit crab', 'n02165105': 'tiger beetle', 'n02165456': 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle', 'n02167151': 'ground beetle, carabid beetle', 'n02168699': 'long-horned beetle, longicorn, longicorn beetle', 'n02169497': 'leaf beetle, chrysomelid', 'n02172182': 'dung beetle', 'n02174001': 'rhinoceros beetle', 'n02177972': 'weevil', 'n02190166': 'fly', 'n02206856': 'bee', 'n02226429': 'grasshopper, hopper', 'n02229544': 'cricket', 'n02231487': 'walking stick, walkingstick, stick insect', 'n02233338': 'cockroach, roach', 'n02236044': 'mantis, mantid', 'n02256656': 'cicada, cicala', 'n02259212': 'leafhopper', 'n02264363': 'lacewing, lacewing fly', 'n02268443': \"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk\", 'n02268853': 'damselfly', 'n02276258': 'admiral', 'n02277742': 'ringlet, ringlet butterfly', 'n02279972': 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus', 'n02280649': 'cabbage butterfly', 'n02281406': 'sulphur butterfly, sulfur butterfly', 'n02281787': 'lycaenid, lycaenid butterfly', 'n01910747': 'jellyfish', 'n01914609': 'sea anemone, anemone', 'n01917289': 'brain coral', 'n01924916': 'flatworm, platyhelminth', 'n01930112': 'nematode, nematode worm, roundworm', 'n01943899': 'conch', 'n01944390': 'snail', 'n01945685': 'slug', 'n01950731': 'sea slug, nudibranch', 'n01955084': 'chiton, coat-of-mail shell, sea cradle, polyplacophore', 'n02319095': 'sea urchin', 'n02321529': 'sea cucumber, holothurian', 'n03584829': 'iron, smoothing iron', 'n03297495': 'espresso maker', 'n03761084': 'microwave, microwave oven', 'n03259280': 'Dutch oven', 'n04111531': 'rotisserie', 'n04442312': 'toaster', 'n04542943': 'waffle iron', 'n04517823': 'vacuum, vacuum cleaner', 'n03207941': 'dishwasher, dish washer, dishwashing machine', 'n04070727': 'refrigerator, icebox', 'n04554684': 'washer, automatic washer, washing machine', 'n03133878': 'Crock Pot', 'n03400231': 'frying pan, frypan, skillet', 'n04596742': 'wok', 'n02939185': 'caldron, cauldron', 'n03063689': 'coffeepot', 'n04398044': 'teapot', 'n04270147': 'spatula', 'n02699494': 'altar', 'n04486054': 'triumphal arch', 'n03899768': 'patio, terrace', 'n04311004': 'steel arch bridge', 'n04366367': 'suspension bridge', 'n04532670': 'viaduct', 'n02793495': 'barn', 'n03457902': 'greenhouse, nursery, glasshouse', 'n03877845': 'palace', 'n03781244': 'monastery', 'n03661043': 'library', 'n02727426': 'apiary, bee house', 'n02859443': 'boathouse', 'n03028079': 'church, church building', 'n03788195': 'mosque', 'n04346328': 'stupa, tope', 'n03956157': 'planetarium', 'n04081281': 'restaurant, eating house, eating place, eatery', 'n03032252': 'cinema, movie theater, movie theatre, movie house, picture palace', 'n03529860': 'home theater, home theatre', 'n03697007': 'lumbermill, sawmill', 'n03065424': 'coil, spiral, volute, whorl, helix', 'n03837869': 'obelisk', 'n04458633': 'totem pole', 'n02980441': 'castle', 'n04005630': 'prison, prison house', 'n03461385': 'grocery store, grocery, food market, market', 'n02776631': 'bakery, bakeshop, bakehouse', 'n02791270': 'barbershop', 'n02871525': 'bookshop, bookstore, bookstall', 'n02927161': 'butcher shop, meat market', 'n03089624': 'confectionery, confectionary, candy store', 'n04200800': 'shoe shop, shoe-shop, shoe store', 'n04443257': 'tobacco shop, tobacconist shop, tobacconist', 'n04462240': 'toyshop', 'n03388043': 'fountain', 'n03042490': 'cliff dwelling', 'n04613696': 'yurt', 'n03216828': 'dock, dockage, docking facility', 'n02892201': 'brass, memorial tablet, plaque', 'n03743016': 'megalith, megalithic structure', 'n02788148': 'bannister, banister, balustrade, balusters, handrail', 'n02894605': 'breakwater, groin, groyne, mole, bulwark, seawall, jetty', 'n03160309': 'dam, dike, dyke', 'n03000134': 'chainlink fence', 'n03930313': 'picket fence, paling', 'n04604644': 'worm fence, snake fence, snake-rail fence, Virginia fence', 'n01755581': 'diamondback, diamondback rattlesnake, Crotalus adamanteus', 'n03459775': 'grille, radiator grille', 'n04239074': 'sliding door', 'n04501370': 'turnstile', 'n03792972': 'mountain tent', 'n04149813': 'scoreboard', 'n03530642': 'honeycomb', 'n03961711': 'plate rack', 'n03903868': 'pedestal, plinth, footstall', 'n02814860': 'beacon, lighthouse, beacon light, pharos', 'n01665541': 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea', 'n07711569': 'mashed potato', 'n07720875': 'bell pepper', 'n07714571': 'head cabbage', 'n07714990': 'broccoli', 'n07715103': 'cauliflower', 'n07716358': 'zucchini, courgette', 'n07716906': 'spaghetti squash', 'n07717410': 'acorn squash', 'n07717556': 'butternut squash', 'n07718472': 'cucumber, cuke', 'n07718747': 'artichoke, globe artichoke', 'n07730033': 'cardoon', 'n07734744': 'mushroom', 'n04209239': 'shower curtain', 'n03594734': 'jean, blue jean, denim', 'n02971356': 'carton', 'n03485794': 'handkerchief, hankie, hanky, hankey', 'n04133789': 'sandal', 'n02747177': 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin', 'n04125021': 'safe', 'n07579787': 'plate', 'n03814906': 'necklace', 'n03134739': 'croquet ball', 'n03404251': 'fur coat', 'n04423845': 'thimble', 'n03877472': \"pajama, pyjama, pj's, jammies\", 'n04120489': 'running shoe', 'n03838899': 'oboe, hautboy, hautbois', 'n03062245': 'cocktail shaker', 'n03014705': 'chest', 'n03717622': 'manhole cover', 'n03777754': 'modem', 'n04493381': 'tub, vat', 'n04476259': 'tray', 'n02777292': 'balance beam, beam', 'n07693725': 'bagel, beigel', 'n04536866': 'violin, fiddle', 'n03998194': 'prayer rug, prayer mat', 'n03617480': 'kimono', 'n07590611': 'hot pot, hotpot', 'n04579145': 'whiskey jug', 'n03623198': 'knee pad', 'n07248320': 'book jacket, dust cover, dust jacket, dust wrapper', 'n04277352': 'spindle', 'n04229816': 'ski mask', 'n02823428': 'beer bottle', 'n03127747': 'crash helmet', 'n02877765': 'bottlecap', 'n04435653': 'tile roof', 'n03724870': 'mask', 'n03710637': 'maillot', 'n03920288': 'Petri dish', 'n03379051': 'football helmet', 'n02807133': 'bathing cap, swimming cap', 'n04399382': 'teddy, teddy bear', 'n03527444': 'holster', 'n03983396': 'pop bottle, soda bottle', 'n03924679': 'photocopier', 'n04532106': 'vestment', 'n06785654': 'crossword puzzle, crossword', 'n03445777': 'golf ball', 'n07613480': 'trifle', 'n04350905': 'suit, suit of clothes', 'n04562935': 'water tower', 'n03325584': 'feather boa, boa', 'n03045698': 'cloak', 'n07892512': 'red wine', 'n03250847': 'drumstick', 'n04192698': 'shield, buckler', 'n03026506': 'Christmas stocking', 'n03534580': 'hoopskirt, crinoline', 'n07565083': 'menu', 'n04296562': 'stage', 'n02869837': 'bonnet, poke bonnet', 'n07871810': 'meat loaf, meatloaf', 'n02799071': 'baseball', 'n03314780': 'face powder', 'n04141327': 'scabbard', 'n04357314': 'sunscreen, sunblock, sun blocker', 'n02823750': 'beer glass', 'n13052670': 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa', 'n07583066': 'guacamole', 'n04599235': 'wool, woolen, woollen', 'n07802026': 'hay', 'n02883205': 'bow tie, bow-tie, bowtie', 'n03709823': 'mailbag, postbag', 'n04560804': 'water jug', 'n02909870': 'bucket, pail', 'n03207743': 'dishrag, dishcloth', 'n04263257': 'soup bowl', 'n07932039': 'eggnog', 'n03786901': 'mortar', 'n04479046': 'trench coat', 'n03873416': 'paddle, boat paddle', 'n02999410': 'chain', 'n04367480': 'swab, swob, mop', 'n03775546': 'mixing bowl', 'n07875152': 'potpie', 'n04591713': 'wine bottle', 'n04201297': 'shoji', 'n02916936': 'bulletproof vest', 'n03240683': 'drilling platform, offshore rig', 'n02840245': 'binder, ring-binder', 'n02963159': 'cardigan', 'n04370456': 'sweatshirt', 'n03991062': 'pot, flowerpot', 'n02843684': 'birdhouse', 'n03599486': 'jinrikisha, ricksha, rickshaw', 'n03482405': 'hamper', 'n03942813': 'ping-pong ball', 'n03908618': 'pencil box, pencil case', 'n07584110': 'consomme', 'n02730930': 'apron', 'n04023962': 'punching bag, punch bag, punching ball, punchball', 'n02769748': 'backpack, back pack, knapsack, packsack, rucksack, haversack', 'n10148035': 'groom, bridegroom', 'n02817516': 'bearskin, busby, shako', 'n03908714': 'pencil sharpener', 'n02906734': 'broom', 'n02667093': 'abaya', 'n03787032': 'mortarboard', 'n03980874': 'poncho', 'n03141823': 'crutch', 'n03976467': 'Polaroid camera, Polaroid Land camera', 'n04264628': 'space bar', 'n07930864': 'cup', 'n04039381': 'racket, racquet', 'n06874185': 'traffic light, traffic signal, stoplight', 'n04033901': 'quill, quill pen', 'n04041544': 'radio, wireless', 'n02128757': 'snow leopard, ounce, Panthera uncia', 'n07860988': 'dough', 'n03146219': 'cuirass', 'n03763968': 'military uniform', 'n03676483': 'lipstick, lip rouge', 'n04209133': 'shower cap', 'n03782006': 'monitor', 'n03857828': 'oscilloscope, scope, cathode-ray oscilloscope, CRO', 'n03775071': 'mitten', 'n02892767': 'brassiere, bra, bandeau', 'n07684084': 'French loaf', 'n04522168': 'vase', 'n03764736': 'milk can', 'n04118538': 'rugby ball', 'n03887697': 'paper towel', 'n13044778': 'earthstar', 'n03291819': 'envelope', 'n03770439': 'miniskirt, mini', 'n03124170': 'cowboy hat, ten-gallon hat', 'n04487081': 'trolleybus, trolley coach, trackless trolley', 'n03916031': 'perfume, essence', 'n02808440': 'bathtub, bathing tub, bath, tub', 'n07697537': 'hotdog, hot dog, red hot', 'n12985857': 'coral fungus', 'n02917067': 'bullet train, bullet', 'n03938244': 'pillow', 'n15075141': 'toilet tissue, toilet paper, bathroom tissue', 'n02978881': 'cassette', 'n02966687': \"carpenter's kit, tool kit\", 'n03633091': 'ladle', 'n13040303': 'stinkhorn, carrion fungus', 'n03690938': 'lotion', 'n03476991': 'hair spray', 'n02669723': \"academic gown, academic robe, judge's robe\", 'n03220513': 'dome', 'n03127925': 'crate', 'n04584207': 'wig', 'n07880968': 'burrito', 'n03937543': 'pill bottle', 'n03000247': 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour', 'n04418357': 'theater curtain, theatre curtain', 'n04590129': 'window shade', 'n02795169': 'barrel, cask', 'n04553703': 'washbasin, handbasin, washbowl, lavabo, wash-hand basin', 'n02783161': 'ballpoint, ballpoint pen, ballpen, Biro', 'n02802426': 'basketball', 'n02808304': 'bath towel', 'n03124043': 'cowboy boot', 'n03450230': 'gown', 'n04589890': 'window screen', 'n12998815': 'agaric', 'n02113799': 'standard poodle', 'n02992529': 'cellular telephone, cellular phone, cellphone, cell, mobile phone', 'n03825788': 'nipple', 'n02790996': 'barbell', 'n03710193': 'mailbox, letter box', 'n03630383': 'lab coat, laboratory coat', 'n03347037': 'fire screen, fireguard', 'n03769881': 'minibus', 'n03871628': 'packet', 'n02132136': 'brown bear, bruin, Ursus arctos', 'n03976657': 'pole', 'n03535780': 'horizontal bar, high bar', 'n04259630': 'sombrero', 'n03929855': 'pickelhaube', 'n04049303': 'rain barrel', 'n04548362': 'wallet, billfold, notecase, pocketbook', 'n02979186': 'cassette player', 'n06596364': 'comic book', 'n03935335': 'piggy bank, penny bank', 'n06794110': 'street sign', 'n02825657': 'bell cote, bell cot', 'n03388183': 'fountain pen', 'n04591157': 'Windsor tie', 'n04540053': 'volleyball', 'n03866082': 'overskirt', 'n04136333': 'sarong', 'n04026417': 'purse', 'n02865351': 'bolo tie, bolo, bola tie, bola', 'n02834397': 'bib', 'n03888257': 'parachute, chute', 'n04235860': 'sleeping bag', 'n04404412': 'television, television system', 'n04371430': 'swimming trunks, bathing trunks', 'n03733805': 'measuring cup', 'n07920052': 'espresso', 'n07873807': 'pizza, pizza pie', 'n02895154': 'breastplate, aegis, egis', 'n04204238': 'shopping basket', 'n04597913': 'wooden spoon', 'n04131690': 'saltshaker, salt shaker', 'n07836838': 'chocolate sauce, chocolate syrup', 'n09835506': 'ballplayer, baseball player', 'n03443371': 'goblet', 'n13037406': 'gyromitra', 'n04336792': 'stretcher', 'n04557648': 'water bottle', 'n02445715': 'skunk, polecat, wood pussy', 'n04254120': 'soap dispenser', 'n03595614': 'jersey, T-shirt, tee shirt', 'n04146614': 'school bus', 'n03598930': 'jigsaw puzzle', 'n03958227': 'plastic bag', 'n04069434': 'reflex camera', 'n03188531': 'diaper, nappy, napkin', 'n02786058': 'Band Aid', 'n07615774': 'ice lolly, lolly, lollipop, popsicle', 'n04525038': 'velvet', 'n04409515': 'tennis ball', 'n03424325': 'gasmask, respirator, gas helmet', 'n03223299': 'doormat, welcome mat', 'n03680355': 'Loafer', 'n07614500': 'ice cream, icecream', 'n07695742': 'pretzel', 'n04033995': 'quilt, comforter, comfort, puff', 'n03710721': 'maillot, tank suit', 'n04392985': 'tape player', 'n03047690': 'clog, geta, patten, sabot', 'n03584254': 'iPod', 'n13054560': 'bolete', 'n02138441': 'meerkat, mierkat', 'n10565667': 'scuba diver', 'n03950228': 'pitcher, ewer', 'n03729826': 'matchstick', 'n02837789': 'bikini, two-piece', 'n04254777': 'sock', 'n02988304': 'CD player', 'n03657121': 'lens cap, lens cover', 'n04417672': 'thatch, thatched roof', 'n04523525': 'vault', 'n02815834': 'beaker', 'n09229709': 'bubble', 'n07697313': 'cheeseburger', 'n03888605': 'parallel bars, bars', 'n03355925': 'flagpole, flagstaff', 'n03063599': 'coffee mug', 'n04116512': 'rubber eraser, rubber, pencil eraser', 'n04325704': 'stole', 'n07831146': 'carbonara', 'n03255030': 'dumbbell', 'n02110185': 'Siberian husky', 'n02102040': 'English springer, English springer spaniel', 'n02110063': 'malamute, malemute, Alaskan malamute', 'n02089867': 'Walker hound, Walker foxhound', 'n02102177': 'Welsh springer spaniel', 'n02091134': 'whippet', 'n02092339': 'Weimaraner', 'n02098105': 'soft-coated wheaten terrier', 'n02096437': 'Dandie Dinmont, Dandie Dinmont terrier', 'n02105641': 'Old English sheepdog, bobtail', 'n02091635': 'otterhound, otter hound', 'n02088466': 'bloodhound, sleuthhound', 'n02096051': 'Airedale, Airedale terrier', 'n02097130': 'giant schnauzer', 'n02089078': 'black-and-tan coonhound', 'n02086910': 'papillon', 'n02113978': 'Mexican hairless', 'n02113186': 'Cardigan, Cardigan Welsh corgi', 'n02105162': 'malinois', 'n02098413': 'Lhasa, Lhasa apso', 'n02091467': 'Norwegian elkhound, elkhound', 'n02106550': 'Rottweiler', 'n02091831': 'Saluki, gazelle hound', 'n02104365': 'schipperke', 'n02112706': 'Brabancon griffon', 'n02098286': 'West Highland white terrier', 'n02095889': 'Sealyham terrier, Sealyham', 'n02090721': 'Irish wolfhound', 'n02108000': 'EntleBucher', 'n02108915': 'French bulldog', 'n02107683': 'Bernese mountain dog', 'n02085936': 'Maltese dog, Maltese terrier, Maltese', 'n02094114': 'Norfolk terrier', 'n02087046': 'toy terrier', 'n02096177': 'cairn, cairn terrier', 'n02105056': 'groenendael', 'n02101556': 'clumber, clumber spaniel', 'n02088094': 'Afghan hound, Afghan', 'n02085782': 'Japanese spaniel', 'n02090622': 'borzoi, Russian wolfhound', 'n02113624': 'toy poodle', 'n02093859': 'Kerry blue terrier', 'n02097298': 'Scotch terrier, Scottish terrier, Scottie', 'n02096585': 'Boston bull, Boston terrier', 'n02107574': 'Greater Swiss Mountain dog', 'n02107908': 'Appenzeller', 'n02086240': 'Shih-Tzu', 'n02102973': 'Irish water spaniel', 'n02112018': 'Pomeranian', 'n02093647': 'Bedlington terrier', 'n02097047': 'miniature schnauzer', 'n02106030': 'collie', 'n02093991': 'Irish terrier', 'n02110627': 'affenpinscher, monkey pinscher, monkey dog', 'n02097658': 'silky terrier, Sydney silky', 'n02088364': 'beagle', 'n02111129': 'Leonberg', 'n02100236': 'German short-haired pointer', 'n02115913': 'dhole, Cuon alpinus', 'n02099849': 'Chesapeake Bay retriever', 'n02108422': 'bull mastiff', 'n02104029': 'kuvasz', 'n02110958': 'pug, pug-dog', 'n02099429': 'curly-coated retriever', 'n02094258': 'Norwich terrier', 'n02112350': 'keeshond', 'n02095570': 'Lakeland terrier', 'n02097209': 'standard schnauzer', 'n02097474': 'Tibetan terrier, chrysanthemum dog', 'n02095314': 'wire-haired fox terrier', 'n02088238': 'basset, basset hound', 'n02112137': 'chow, chow chow', 'n02093428': 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier', 'n02105855': 'Shetland sheepdog, Shetland sheep dog, Shetland', 'n02111500': 'Great Pyrenees', 'n02085620': 'Chihuahua', 'n02099712': 'Labrador retriever', 'n02111889': 'Samoyed, Samoyede', 'n02088632': 'bluetick', 'n02105412': 'kelpie', 'n02107312': 'miniature pinscher', 'n02091032': 'Italian greyhound', 'n02102318': 'cocker spaniel, English cocker spaniel, cocker', 'n02102480': 'Sussex spaniel', 'n02113023': 'Pembroke, Pembroke Welsh corgi', 'n02086646': 'Blenheim spaniel', 'n02091244': 'Ibizan hound, Ibizan Podenco', 'n02089973': 'English foxhound', 'n02105251': 'briard', 'n02093754': 'Border terrier'}\ntmp_df = pd.DataFrame({\"img_path\":tf.io.gfile.glob(os.path.join(INPUT_DIR, \"imagenetmini-1000\", \"imagenet-mini\", \"train\", \"**\", \"*.JPEG\"))})\ntmp_df[\"cls\"] = tmp_df[\"img_path\"].apply(lambda x: imagenet_cls_map[x.rsplit(\"/\", 2)[-2]])\ntmp_df[\"cls_cnt\"] = tmp_df.groupby(\"cls\")[\"cls\"].transform(\"count\")\ntmp_df = tmp_df.sort_values(by=\"cls_cnt\", ascending=False)\ntmp_df = tmp_df.head(n_imagenetmini).sample(n_imagenetmini).reset_index(drop=True)\ntmp_df = tmp_df[[\"img_path\", \"cls\"]]\ntmp_df[\"supercls\"] = \"other\"\ntmp_df[\"dataset\"] = \"imagenetmini-1000\"\ntmp_df[\"val_only\"] = False\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:23:57.566982Z","iopub.execute_input":"2023-04-19T02:23:57.567774Z","iopub.status.idle":"2023-04-19T02:24:04.315599Z","shell.execute_reply.started":"2023-04-19T02:23:57.567724Z","shell.execute_reply":"2023-04-19T02:24:04.314184Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":" img_path \\\n0 ../input/imaterialist-fashion-2020-fgvc7/train... \n1 ../input/imaterialist-fashion-2020-fgvc7/train... \n2 ../input/imaterialist-fashion-2020-fgvc7/train... \n3 ../input/imaterialist-fashion-2020-fgvc7/train... \n4 ../input/imaterialist-fashion-2020-fgvc7/train... \n... ... \n42495 /kaggle/input/imagenetmini-1000/imagenet-mini/... \n42496 /kaggle/input/imagenetmini-1000/imagenet-mini/... \n42497 /kaggle/input/imagenetmini-1000/imagenet-mini/... \n42498 /kaggle/input/imagenetmini-1000/imagenet-mini/... \n42499 /kaggle/input/imagenetmini-1000/imagenet-mini/... \n\n supercls cls \\\n0 apparel & accessories sleeve \n1 apparel & accessories sleeve \n2 apparel & accessories sleeve \n3 apparel & accessories shoe \n4 apparel & accessories sleeve \n... ... ... \n42495 other hand-held computer, hand-held microcomputer \n42496 other radio, wireless \n42497 other Windsor tie \n42498 other bulletproof vest \n42499 other dugong, Dugong dugon \n\n dataset val_only \n0 imaterialist-fashion-2020-fgvc7 0.0 \n1 imaterialist-fashion-2020-fgvc7 0.0 \n2 imaterialist-fashion-2020-fgvc7 0.0 \n3 imaterialist-fashion-2020-fgvc7 0.0 \n4 imaterialist-fashion-2020-fgvc7 0.0 \n... ... ... \n42495 imagenetmini-1000 0.0 \n42496 imagenetmini-1000 0.0 \n42497 imagenetmini-1000 0.0 \n42498 imagenetmini-1000 0.0 \n42499 imagenetmini-1000 0.0 \n\n[42500 rows x 5 columns]","text/html":"
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img_pathsuperclsclsdatasetval_only
0../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
1../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
2../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
3../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
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42495/kaggle/input/imagenetmini-1000/imagenet-mini/...otherhand-held computer, hand-held microcomputerimagenetmini-10000.0
42496/kaggle/input/imagenetmini-1000/imagenet-mini/...otherradio, wirelessimagenetmini-10000.0
42497/kaggle/input/imagenetmini-1000/imagenet-mini/...otherWindsor tieimagenetmini-10000.0
42498/kaggle/input/imagenetmini-1000/imagenet-mini/...otherbulletproof vestimagenetmini-10000.0
42499/kaggle/input/imagenetmini-1000/imagenet-mini/...otherdugong, Dugong dugonimagenetmini-10000.0
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42500 rows Γ— 5 columns

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"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**ImageNetSketch**\n* Take 5000","metadata":{}},{"cell_type":"code","source":"n_imagenetsketch=5000\ntmp_df = pd.DataFrame({\"img_path\":tf.io.gfile.glob(os.path.join(INPUT_DIR, \"imagenetsketch\", \"sketch\", \"**\", \"*.JPEG\"))})\ntmp_df[\"cls\"] = tmp_df[\"img_path\"].apply(lambda x: imagenet_cls_map[x.rsplit(\"/\", 2)[-2]])\ntmp_df[\"cls_cnt\"] = tmp_df.groupby(\"cls\")[\"cls\"].transform(\"count\")\ntmp_df = tmp_df.sort_values(by=\"cls_cnt\", ascending=False)\ntmp_df = tmp_df.head(n_imagenetsketch).sample(n_imagenetsketch).reset_index(drop=True)\ntmp_df = tmp_df[[\"img_path\", \"cls\"]]\n\ntmp_df[\"supercls\"] = \"illustrations\"\ntmp_df[\"dataset\"] = \"imagenetsketch\"\ntmp_df[\"val_only\"] = False\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:24:04.316918Z","iopub.execute_input":"2023-04-19T02:24:04.317259Z","iopub.status.idle":"2023-04-19T02:24:16.274572Z","shell.execute_reply.started":"2023-04-19T02:24:04.317229Z","shell.execute_reply":"2023-04-19T02:24:16.273339Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":" img_path \\\n0 ../input/imaterialist-fashion-2020-fgvc7/train... \n1 ../input/imaterialist-fashion-2020-fgvc7/train... \n2 ../input/imaterialist-fashion-2020-fgvc7/train... \n3 ../input/imaterialist-fashion-2020-fgvc7/train... \n4 ../input/imaterialist-fashion-2020-fgvc7/train... \n... ... \n47495 /kaggle/input/imagenetsketch/sketch/n01580077/... \n47496 /kaggle/input/imagenetsketch/sketch/n03950228/... \n47497 /kaggle/input/imagenetsketch/sketch/n04409515/... \n47498 /kaggle/input/imagenetsketch/sketch/n01629819/... \n47499 /kaggle/input/imagenetsketch/sketch/n02410509/... \n\n supercls cls \\\n0 apparel & accessories sleeve \n1 apparel & accessories sleeve \n2 apparel & accessories sleeve \n3 apparel & accessories shoe \n4 apparel & accessories sleeve \n... ... ... \n47495 illustrations jay \n47496 illustrations pitcher, ewer \n47497 illustrations tennis ball \n47498 illustrations European fire salamander, Salamandra salamandra \n47499 illustrations bison \n\n dataset val_only \n0 imaterialist-fashion-2020-fgvc7 0.0 \n1 imaterialist-fashion-2020-fgvc7 0.0 \n2 imaterialist-fashion-2020-fgvc7 0.0 \n3 imaterialist-fashion-2020-fgvc7 0.0 \n4 imaterialist-fashion-2020-fgvc7 0.0 \n... ... ... \n47495 imagenetsketch 0.0 \n47496 imagenetsketch 0.0 \n47497 imagenetsketch 0.0 \n47498 imagenetsketch 0.0 \n47499 imagenetsketch 0.0 \n\n[47500 rows x 5 columns]","text/html":"
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img_pathsuperclsclsdatasetval_only
0../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
1../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
2../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
3../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
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47495/kaggle/input/imagenetsketch/sketch/n01580077/...illustrationsjayimagenetsketch0.0
47496/kaggle/input/imagenetsketch/sketch/n03950228/...illustrationspitcher, ewerimagenetsketch0.0
47497/kaggle/input/imagenetsketch/sketch/n04409515/...illustrationstennis ballimagenetsketch0.0
47498/kaggle/input/imagenetsketch/sketch/n01629819/...illustrationsEuropean fire salamander, Salamandra salamandraimagenetsketch0.0
47499/kaggle/input/imagenetsketch/sketch/n02410509/...illustrationsbisonimagenetsketch0.0
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47500 rows Γ— 5 columns

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"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**PixivPopularIllustrations**\n* Take 500 (no classes)","metadata":{}},{"cell_type":"code","source":"n_pixiv=500\ntmp_df = pd.DataFrame({\"img_path\":tf.io.gfile.glob(os.path.join(INPUT_DIR, \"pixiv-popular-illustrations\", \"pixiv_og_11062020\", \"*.jpg\"))})\ntmp_df = tmp_df.sample(n_pixiv).reset_index(drop=True)\ntmp_df[\"cls\"] = \"original_illustration\"\ntmp_df[\"supercls\"] = \"illustrations\"\ntmp_df[\"dataset\"] = \"pixiv-popular-illustrations\"\ntmp_df[\"val_only\"] = False\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:24:16.275954Z","iopub.execute_input":"2023-04-19T02:24:16.276315Z","iopub.status.idle":"2023-04-19T02:24:17.357635Z","shell.execute_reply.started":"2023-04-19T02:24:16.276275Z","shell.execute_reply":"2023-04-19T02:24:17.356443Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":" img_path \\\n0 ../input/imaterialist-fashion-2020-fgvc7/train... \n1 ../input/imaterialist-fashion-2020-fgvc7/train... \n2 ../input/imaterialist-fashion-2020-fgvc7/train... \n3 ../input/imaterialist-fashion-2020-fgvc7/train... \n4 ../input/imaterialist-fashion-2020-fgvc7/train... \n... ... \n47995 /kaggle/input/pixiv-popular-illustrations/pixi... \n47996 /kaggle/input/pixiv-popular-illustrations/pixi... \n47997 /kaggle/input/pixiv-popular-illustrations/pixi... \n47998 /kaggle/input/pixiv-popular-illustrations/pixi... \n47999 /kaggle/input/pixiv-popular-illustrations/pixi... \n\n supercls cls \\\n0 apparel & accessories sleeve \n1 apparel & accessories sleeve \n2 apparel & accessories sleeve \n3 apparel & accessories shoe \n4 apparel & accessories sleeve \n... ... ... \n47995 illustrations original_illustration \n47996 illustrations original_illustration \n47997 illustrations original_illustration \n47998 illustrations original_illustration \n47999 illustrations original_illustration \n\n dataset val_only \n0 imaterialist-fashion-2020-fgvc7 0.0 \n1 imaterialist-fashion-2020-fgvc7 0.0 \n2 imaterialist-fashion-2020-fgvc7 0.0 \n3 imaterialist-fashion-2020-fgvc7 0.0 \n4 imaterialist-fashion-2020-fgvc7 0.0 \n... ... ... \n47995 pixiv-popular-illustrations 0.0 \n47996 pixiv-popular-illustrations 0.0 \n47997 pixiv-popular-illustrations 0.0 \n47998 pixiv-popular-illustrations 0.0 \n47999 pixiv-popular-illustrations 0.0 \n\n[48000 rows x 5 columns]","text/html":"
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img_pathsuperclsclsdatasetval_only
0../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
1../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
2../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
3../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
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47995/kaggle/input/pixiv-popular-illustrations/pixi...illustrationsoriginal_illustrationpixiv-popular-illustrations0.0
47996/kaggle/input/pixiv-popular-illustrations/pixi...illustrationsoriginal_illustrationpixiv-popular-illustrations0.0
47997/kaggle/input/pixiv-popular-illustrations/pixi...illustrationsoriginal_illustrationpixiv-popular-illustrations0.0
47998/kaggle/input/pixiv-popular-illustrations/pixi...illustrationsoriginal_illustrationpixiv-popular-illustrations0.0
47999/kaggle/input/pixiv-popular-illustrations/pixi...illustrationsoriginal_illustrationpixiv-popular-illustrations0.0
\n

48000 rows Γ— 5 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**Memotion Dataset**\n* Take 2500","metadata":{}},{"cell_type":"code","source":"n_meme = 2500\ntmp_df = pd.read_csv(os.path.join(INPUT_DIR, \"memotion-dataset-7k/memotion_dataset_7k\", \"labels.csv\"))\ntmp_df[\"cls\"] = \"meme_\"+tmp_df[\"humour\"]+\"_\"+tmp_df[\"sarcasm\"]+\"_\"+tmp_df[\"offensive\"]+\"_\"+tmp_df[\"motivational\"]\ntmp_df[\"img_path\"] = os.path.join(INPUT_DIR, \"memotion-dataset-7k/memotion_dataset_7k\")+\"/images/\"+tmp_df[\"image_name\"]\ntmp_df[\"cls_cnt\"] = tmp_df.groupby(\"cls\")[\"cls\"].transform(\"count\")\ntmp_df = tmp_df.sort_values(by=\"cls_cnt\", ascending=False)\ntmp_df = tmp_df.head(n_meme).sample(n_meme).reset_index(drop=True)\ntmp_df = tmp_df[[\"img_path\", \"cls\"]]\ntmp_df[\"supercls\"] = \"memes\"\ntmp_df[\"dataset\"] = \"memotion-dataset-7k\"\ntmp_df[\"val_only\"] = False\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:24:17.359298Z","iopub.execute_input":"2023-04-19T02:24:17.363223Z","iopub.status.idle":"2023-04-19T02:24:17.470661Z","shell.execute_reply.started":"2023-04-19T02:24:17.363181Z","shell.execute_reply":"2023-04-19T02:24:17.469308Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":" img_path \\\n0 ../input/imaterialist-fashion-2020-fgvc7/train... \n1 ../input/imaterialist-fashion-2020-fgvc7/train... \n2 ../input/imaterialist-fashion-2020-fgvc7/train... \n3 ../input/imaterialist-fashion-2020-fgvc7/train... \n4 ../input/imaterialist-fashion-2020-fgvc7/train... \n... ... \n50495 /kaggle/input/memotion-dataset-7k/memotion_dat... \n50496 /kaggle/input/memotion-dataset-7k/memotion_dat... \n50497 /kaggle/input/memotion-dataset-7k/memotion_dat... \n50498 /kaggle/input/memotion-dataset-7k/memotion_dat... \n50499 /kaggle/input/memotion-dataset-7k/memotion_dat... \n\n supercls \\\n0 apparel & accessories \n1 apparel & accessories \n2 apparel & accessories \n3 apparel & accessories \n4 apparel & accessories \n... ... \n50495 memes \n50496 memes \n50497 memes \n50498 memes \n50499 memes \n\n cls \\\n0 sleeve \n1 sleeve \n2 sleeve \n3 shoe \n4 sleeve \n... ... \n50495 meme_very_funny_general_slight_not_motivational \n50496 meme_not_funny_not_sarcastic_not_offensive_not... \n50497 meme_not_funny_not_sarcastic_not_offensive_not... \n50498 meme_funny_not_sarcastic_not_offensive_not_mot... \n50499 meme_not_funny_not_sarcastic_not_offensive_not... \n\n dataset val_only \n0 imaterialist-fashion-2020-fgvc7 0.0 \n1 imaterialist-fashion-2020-fgvc7 0.0 \n2 imaterialist-fashion-2020-fgvc7 0.0 \n3 imaterialist-fashion-2020-fgvc7 0.0 \n4 imaterialist-fashion-2020-fgvc7 0.0 \n... ... ... \n50495 memotion-dataset-7k 0.0 \n50496 memotion-dataset-7k 0.0 \n50497 memotion-dataset-7k 0.0 \n50498 memotion-dataset-7k 0.0 \n50499 memotion-dataset-7k 0.0 \n\n[50500 rows x 5 columns]","text/html":"
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img_pathsuperclsclsdatasetval_only
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2../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
3../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
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50495/kaggle/input/memotion-dataset-7k/memotion_dat...memesmeme_very_funny_general_slight_not_motivationalmemotion-dataset-7k0.0
50496/kaggle/input/memotion-dataset-7k/memotion_dat...memesmeme_not_funny_not_sarcastic_not_offensive_not...memotion-dataset-7k0.0
50497/kaggle/input/memotion-dataset-7k/memotion_dat...memesmeme_not_funny_not_sarcastic_not_offensive_not...memotion-dataset-7k0.0
50498/kaggle/input/memotion-dataset-7k/memotion_dat...memesmeme_funny_not_sarcastic_not_offensive_not_mot...memotion-dataset-7k0.0
50499/kaggle/input/memotion-dataset-7k/memotion_dat...memesmeme_not_funny_not_sarcastic_not_offensive_not...memotion-dataset-7k0.0
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50500 rows Γ— 5 columns

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"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**THE MET ART DATASET**\n* 500 artworks","metadata":{}},{"cell_type":"code","source":"n_met=500\n\ntmp_df=pd.DataFrame(load_json_to_dict(\"../input/the-met-dataset/ground_truth/mini_MET_database.json\"))\ntmp_df[\"img_path\"] = os.path.join(INPUT_DIR, \"the-met-dataset\", \"small_MET\")+\"/\"+tmp_df[\"path\"]\ntmp_df[\"cls\"] = \"met_art\"\ntmp_df = tmp_df[[\"img_path\", \"cls\"]].sample(n_met).reset_index(drop=True)\ntmp_df[\"supercls\"] = \"artworks\"\ntmp_df[\"dataset\"] = \"the-met-dataset\"\ntmp_df[\"val_only\"] = False\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:24:17.472222Z","iopub.execute_input":"2023-04-19T02:24:17.472575Z","iopub.status.idle":"2023-04-19T02:24:17.593138Z","shell.execute_reply.started":"2023-04-19T02:24:17.472541Z","shell.execute_reply":"2023-04-19T02:24:17.591615Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":" img_path \\\n0 ../input/imaterialist-fashion-2020-fgvc7/train... \n1 ../input/imaterialist-fashion-2020-fgvc7/train... \n2 ../input/imaterialist-fashion-2020-fgvc7/train... \n3 ../input/imaterialist-fashion-2020-fgvc7/train... \n4 ../input/imaterialist-fashion-2020-fgvc7/train... \n... ... \n50995 /kaggle/input/the-met-dataset/small_MET/MET/20... \n50996 /kaggle/input/the-met-dataset/small_MET/MET/46... \n50997 /kaggle/input/the-met-dataset/small_MET/MET/70... \n50998 /kaggle/input/the-met-dataset/small_MET/MET/56... \n50999 /kaggle/input/the-met-dataset/small_MET/MET/20... \n\n supercls cls dataset \\\n0 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n1 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n2 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n3 apparel & accessories shoe imaterialist-fashion-2020-fgvc7 \n4 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n... ... ... ... \n50995 artworks met_art the-met-dataset \n50996 artworks met_art the-met-dataset \n50997 artworks met_art the-met-dataset \n50998 artworks met_art the-met-dataset \n50999 artworks met_art the-met-dataset \n\n val_only \n0 0.0 \n1 0.0 \n2 0.0 \n3 0.0 \n4 0.0 \n... ... \n50995 0.0 \n50996 0.0 \n50997 0.0 \n50998 0.0 \n50999 0.0 \n\n[51000 rows x 5 columns]","text/html":"
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img_pathsuperclsclsdatasetval_only
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50995/kaggle/input/the-met-dataset/small_MET/MET/20...artworksmet_artthe-met-dataset0.0
50996/kaggle/input/the-met-dataset/small_MET/MET/46...artworksmet_artthe-met-dataset0.0
50997/kaggle/input/the-met-dataset/small_MET/MET/70...artworksmet_artthe-met-dataset0.0
50998/kaggle/input/the-met-dataset/small_MET/MET/56...artworksmet_artthe-met-dataset0.0
50999/kaggle/input/the-met-dataset/small_MET/MET/20...artworksmet_artthe-met-dataset0.0
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51000 rows Γ— 5 columns

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"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**ARTWORK - PAINTING BY ARTIST**","metadata":{}},{"cell_type":"code","source":"n_art = 2500\n\ntmp_df = pd.DataFrame({\"img_path\":tf.io.gfile.glob(os.path.join(INPUT_DIR, \"best-artworks-of-all-time\", \"images\", \"images\", \"**\", \"*.jpg\"))})\ntmp_df[\"cls\"] = tmp_df.img_path.apply(lambda x: x.rsplit(\"/\", 2)[-2])\ntmp_df[\"cls_cnt\"] = tmp_df.groupby(\"cls\")[\"cls\"].transform(\"count\")\ntmp_df = tmp_df.sort_values(by=\"cls_cnt\", ascending=False)\ntmp_df = tmp_df.head(n_art).sample(n_art).reset_index(drop=True)\ntmp_df = tmp_df[[\"img_path\", \"cls\"]]\ntmp_df[\"supercls\"] = \"artworks\"\ntmp_df[\"dataset\"] = \"best-artworks-of-all-time\"\ntmp_df[\"val_only\"] = False\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:24:17.594673Z","iopub.execute_input":"2023-04-19T02:24:17.595002Z","iopub.status.idle":"2023-04-19T02:24:19.841425Z","shell.execute_reply.started":"2023-04-19T02:24:17.594972Z","shell.execute_reply":"2023-04-19T02:24:19.83999Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":" img_path \\\n0 ../input/imaterialist-fashion-2020-fgvc7/train... \n1 ../input/imaterialist-fashion-2020-fgvc7/train... \n2 ../input/imaterialist-fashion-2020-fgvc7/train... \n3 ../input/imaterialist-fashion-2020-fgvc7/train... \n4 ../input/imaterialist-fashion-2020-fgvc7/train... \n... ... \n53495 /kaggle/input/best-artworks-of-all-time/images... \n53496 /kaggle/input/best-artworks-of-all-time/images... \n53497 /kaggle/input/best-artworks-of-all-time/images... \n53498 /kaggle/input/best-artworks-of-all-time/images... \n53499 /kaggle/input/best-artworks-of-all-time/images... \n\n supercls cls \\\n0 apparel & accessories sleeve \n1 apparel & accessories sleeve \n2 apparel & accessories sleeve \n3 apparel & accessories shoe \n4 apparel & accessories sleeve \n... ... ... \n53495 artworks Pablo_Picasso \n53496 artworks Edgar_Degas \n53497 artworks Edgar_Degas \n53498 artworks Edgar_Degas \n53499 artworks Pierre-Auguste_Renoir \n\n dataset val_only \n0 imaterialist-fashion-2020-fgvc7 0.0 \n1 imaterialist-fashion-2020-fgvc7 0.0 \n2 imaterialist-fashion-2020-fgvc7 0.0 \n3 imaterialist-fashion-2020-fgvc7 0.0 \n4 imaterialist-fashion-2020-fgvc7 0.0 \n... ... ... \n53495 best-artworks-of-all-time 0.0 \n53496 best-artworks-of-all-time 0.0 \n53497 best-artworks-of-all-time 0.0 \n53498 best-artworks-of-all-time 0.0 \n53499 best-artworks-of-all-time 0.0 \n\n[53500 rows x 5 columns]","text/html":"
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img_pathsuperclsclsdatasetval_only
0../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
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2../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
3../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
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53495/kaggle/input/best-artworks-of-all-time/images...artworksPablo_Picassobest-artworks-of-all-time0.0
53496/kaggle/input/best-artworks-of-all-time/images...artworksEdgar_Degasbest-artworks-of-all-time0.0
53497/kaggle/input/best-artworks-of-all-time/images...artworksEdgar_Degasbest-artworks-of-all-time0.0
53498/kaggle/input/best-artworks-of-all-time/images...artworksEdgar_Degasbest-artworks-of-all-time0.0
53499/kaggle/input/best-artworks-of-all-time/images...artworksPierre-Auguste_Renoirbest-artworks-of-all-time0.0
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53500 rows Γ— 5 columns

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"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**TOYS**\n\n* 500","metadata":{}},{"cell_type":"code","source":"tmp_df = pd.read_csv(os.path.join(INPUT_DIR, \"guie-toys-dataset\", \"toys.csv\"))\ntmp_df[\"img_path\"] = \"/kaggle/input/guie-toys-dataset/toys/\"+tmp_df[\"toy_name\"]+\".jpg\"\ntmp_df[\"cls\"] = \"misc_toy\"\ntmp_df = tmp_df[[\"img_path\", \"cls\"]].sample(500).reset_index(drop=True)\ntmp_df[\"supercls\"] = \"toys\"\ntmp_df[\"dataset\"] = \"guie-toys-dataset\"\ntmp_df[\"val_only\"] = False\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:24:19.843202Z","iopub.execute_input":"2023-04-19T02:24:19.844414Z","iopub.status.idle":"2023-04-19T02:24:19.884579Z","shell.execute_reply.started":"2023-04-19T02:24:19.844355Z","shell.execute_reply":"2023-04-19T02:24:19.88321Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":" img_path \\\n0 ../input/imaterialist-fashion-2020-fgvc7/train... \n1 ../input/imaterialist-fashion-2020-fgvc7/train... \n2 ../input/imaterialist-fashion-2020-fgvc7/train... \n3 ../input/imaterialist-fashion-2020-fgvc7/train... \n4 ../input/imaterialist-fashion-2020-fgvc7/train... \n... ... \n53995 /kaggle/input/guie-toys-dataset/toys/GI Joe Ma... \n53996 /kaggle/input/guie-toys-dataset/toys/Fistful O... \n53997 /kaggle/input/guie-toys-dataset/toys/Doodle Be... \n53998 /kaggle/input/guie-toys-dataset/toys/Inchworm ... \n53999 /kaggle/input/guie-toys-dataset/toys/Tamagotch... \n\n supercls cls dataset \\\n0 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n1 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n2 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n3 apparel & accessories shoe imaterialist-fashion-2020-fgvc7 \n4 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n... ... ... ... \n53995 toys misc_toy guie-toys-dataset \n53996 toys misc_toy guie-toys-dataset \n53997 toys misc_toy guie-toys-dataset \n53998 toys misc_toy guie-toys-dataset \n53999 toys misc_toy guie-toys-dataset \n\n val_only \n0 0.0 \n1 0.0 \n2 0.0 \n3 0.0 \n4 0.0 \n... ... \n53995 0.0 \n53996 0.0 \n53997 0.0 \n53998 0.0 \n53999 0.0 \n\n[54000 rows x 5 columns]","text/html":"
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img_pathsuperclsclsdatasetval_only
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1../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
2../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
3../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
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53995/kaggle/input/guie-toys-dataset/toys/GI Joe Ma...toysmisc_toyguie-toys-dataset0.0
53996/kaggle/input/guie-toys-dataset/toys/Fistful O...toysmisc_toyguie-toys-dataset0.0
53997/kaggle/input/guie-toys-dataset/toys/Doodle Be...toysmisc_toyguie-toys-dataset0.0
53998/kaggle/input/guie-toys-dataset/toys/Inchworm ...toysmisc_toyguie-toys-dataset0.0
53999/kaggle/input/guie-toys-dataset/toys/Tamagotch...toysmisc_toyguie-toys-dataset0.0
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54000 rows Γ— 5 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"n_toy_cars = 150\n\ntmp_df = pd.DataFrame({\"img_path\":tf.io.gfile.glob(os.path.join(INPUT_DIR, \"toy-car-dataset-lear\", \"toy_car_lear\", \"imstdsize\", \"*.png\"))})\ntmp_df = tmp_df.sample(n_toy_cars).reset_index(drop=True)\ntmp_df[\"cls\"] = \"toy_car\"\ntmp_df[\"supercls\"] = \"toys\"\ntmp_df[\"dataset\"] = \"toy-car-dataset-lear\"\ntmp_df[\"val_only\"] = False\n\n# Update master dataframe\nmaster_df = pd.concat([master_df, tmp_df], ignore_index=True)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:24:19.886013Z","iopub.execute_input":"2023-04-19T02:24:19.886391Z","iopub.status.idle":"2023-04-19T02:24:19.9777Z","shell.execute_reply.started":"2023-04-19T02:24:19.886357Z","shell.execute_reply":"2023-04-19T02:24:19.976238Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":" img_path \\\n0 ../input/imaterialist-fashion-2020-fgvc7/train... \n1 ../input/imaterialist-fashion-2020-fgvc7/train... \n2 ../input/imaterialist-fashion-2020-fgvc7/train... \n3 ../input/imaterialist-fashion-2020-fgvc7/train... \n4 ../input/imaterialist-fashion-2020-fgvc7/train... \n... ... \n54145 /kaggle/input/toy-car-dataset-lear/toy_car_lea... \n54146 /kaggle/input/toy-car-dataset-lear/toy_car_lea... \n54147 /kaggle/input/toy-car-dataset-lear/toy_car_lea... \n54148 /kaggle/input/toy-car-dataset-lear/toy_car_lea... \n54149 /kaggle/input/toy-car-dataset-lear/toy_car_lea... \n\n supercls cls dataset \\\n0 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n1 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n2 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n3 apparel & accessories shoe imaterialist-fashion-2020-fgvc7 \n4 apparel & accessories sleeve imaterialist-fashion-2020-fgvc7 \n... ... ... ... \n54145 toys toy_car toy-car-dataset-lear \n54146 toys toy_car toy-car-dataset-lear \n54147 toys toy_car toy-car-dataset-lear \n54148 toys toy_car toy-car-dataset-lear \n54149 toys toy_car toy-car-dataset-lear \n\n val_only \n0 0.0 \n1 0.0 \n2 0.0 \n3 0.0 \n4 0.0 \n... ... \n54145 0.0 \n54146 0.0 \n54147 0.0 \n54148 0.0 \n54149 0.0 \n\n[54150 rows x 5 columns]","text/html":"
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img_pathsuperclsclsdatasetval_only
0../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
1../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
2../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
3../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriesshoeimaterialist-fashion-2020-fgvc70.0
4../input/imaterialist-fashion-2020-fgvc7/train...apparel & accessoriessleeveimaterialist-fashion-2020-fgvc70.0
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54145/kaggle/input/toy-car-dataset-lear/toy_car_lea...toystoy_cartoy-car-dataset-lear0.0
54146/kaggle/input/toy-car-dataset-lear/toy_car_lea...toystoy_cartoy-car-dataset-lear0.0
54147/kaggle/input/toy-car-dataset-lear/toy_car_lea...toystoy_cartoy-car-dataset-lear0.0
54148/kaggle/input/toy-car-dataset-lear/toy_car_lea...toystoy_cartoy-car-dataset-lear0.0
54149/kaggle/input/toy-car-dataset-lear/toy_car_lea...toystoy_cartoy-car-dataset-lear0.0
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54150 rows Γ— 5 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"master_df[\"supercls\"] = master_df[\"supercls\"].str.replace(\" & \", \"_\").str.replace(\" \", \"_\")\nmaster_df = master_df[~master_df.img_path.str.contains(\"DS_Store\")]\nmaster_df[\"img_size\"] = master_df.img_path.progress_apply(lambda x: Image.open(x).size)\nmaster_df[\"img_width\"] = master_df[\"img_size\"].apply(lambda x: x[0])\nmaster_df[\"img_height\"] = master_df[\"img_size\"].apply(lambda x: x[1])\nmaster_df[\"img_area\"] = master_df[\"img_width\"]*master_df[\"img_height\"]\n\n# remove weird class stuff\nmaster_df[\"cls\"] = master_df[\"cls\"].str.replace(\", \", \"_\").str.replace(\" / \", \"_\").str.replace(\" \", \"_\").str.replace(\"-\", \"_\").str.lower()\n\n# limit to 5 underscores\nmaster_df[\"cls\"] = master_df[\"cls\"].astype(str).apply(lambda x: \"_\".join(x.split(\"_\")[:5]))\n\n# Fix shopee\nmaster_df[\"cls\"] = master_df[\"cls\"].apply(lambda x: \"shopee_product\" if x==\"nan\" else x)\n\n# Drop low occurence classes\nmaster_df = master_df[~master_df.cls.isin([k for k,v in master_df.cls.value_counts().items() if v<15])].reset_index(drop=True)\n\n# Drop VERY small images (all from imagenetmini)\nmaster_df = master_df[master_df.img_area>5000].reset_index(drop=True)\n\nmaster_df.to_csv(\"master.csv\", index=False)\nmaster_df","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:24:19.979336Z","iopub.execute_input":"2023-04-19T02:24:19.980137Z","iopub.status.idle":"2023-04-19T02:31:55.274248Z","shell.execute_reply.started":"2023-04-19T02:24:19.980088Z","shell.execute_reply":"2023-04-19T02:31:55.273019Z"},"trusted":true},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":" 0%| | 0/54150 [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 \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
img_pathsuperclsclsdatasetval_onlyimg_sizeimg_widthimg_heightimg_area
0../input/imaterialist-fashion-2020-fgvc7/train...apparel_accessoriessleeveimaterialist-fashion-2020-fgvc70.0(1000, 1500)100015001500000
1../input/imaterialist-fashion-2020-fgvc7/train...apparel_accessoriessleeveimaterialist-fashion-2020-fgvc70.0(3936, 2624)3936262410328064
2../input/imaterialist-fashion-2020-fgvc7/train...apparel_accessoriessleeveimaterialist-fashion-2020-fgvc70.0(5472, 3648)5472364819961856
3../input/imaterialist-fashion-2020-fgvc7/train...apparel_accessoriesshoeimaterialist-fashion-2020-fgvc70.0(667, 1000)6671000667000
4../input/imaterialist-fashion-2020-fgvc7/train...apparel_accessoriessleeveimaterialist-fashion-2020-fgvc70.0(1941, 3000)194130005823000
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53509/kaggle/input/toy-car-dataset-lear/toy_car_lea...toystoy_cartoy-car-dataset-lear0.0(323, 150)32315048450
53510/kaggle/input/toy-car-dataset-lear/toy_car_lea...toystoy_cartoy-car-dataset-lear0.0(443, 150)44315066450
53511/kaggle/input/toy-car-dataset-lear/toy_car_lea...toystoy_cartoy-car-dataset-lear0.0(378, 150)37815056700
53512/kaggle/input/toy-car-dataset-lear/toy_car_lea...toystoy_cartoy-car-dataset-lear0.0(355, 150)35515053250
53513/kaggle/input/toy-car-dataset-lear/toy_car_lea...toystoy_cartoy-car-dataset-lear0.0(341, 150)34115051150
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53514 rows Γ— 9 columns

\n"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n\n**FUNCTIONS**","metadata":{}},{"cell_type":"code","source":"resize_layer = tf.keras.layers.Resizing(224,224, crop_to_aspect_ratio=True)\ndef resize_like_during_inference(img_path, _to_h=224, _to_w=224, preserve_ar=True):\n img = tf.io.decode_image(tf.io.read_file(img_path))\n return resize_layer(img).numpy().astype(np.uint8)\n\ndef save_row_to_disk(row_kwargs, dataset_dir=\"/kaggle/working/dataset\", image_size=(224,224), preserve_ar=True, **kwargs):\n try:\n resized_square_img = resize_like_during_inference(row_kwargs[\"img_path\"], image_size[1], image_size[0], preserve_ar)\n dst_dir = os.path.join(dataset_dir, row_kwargs[\"supercls\"], row_kwargs[\"cls\"])\n if not os.path.isdir(dst_dir): os.makedirs(dst_dir, exist_ok=True)\n dst_path = os.path.join(dst_dir, row_kwargs[\"img_path\"].rsplit(\"/\", 1)[-1].rsplit(\".\", 1)[0]+\".png\")\n cv2.imwrite(dst_path, resized_square_img[..., ::-1])\n \n if row_kwargs.name%250==0:\n gc.collect(); gc.collect(); tf.keras.backend.clear_session(); gc.collect(); \n except:\n print(row_kwargs.name, row_kwargs.img_path)\n pass","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:31:55.276023Z","iopub.execute_input":"2023-04-19T02:31:55.276494Z","iopub.status.idle":"2023-04-19T02:31:55.297623Z","shell.execute_reply.started":"2023-04-19T02:31:55.276448Z","shell.execute_reply":"2023-04-19T02:31:55.296384Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"markdown","source":"
\n\n**TEST**","metadata":{}},{"cell_type":"code","source":"_ = master_df.progress_apply(save_row_to_disk, axis=1)","metadata":{"execution":{"iopub.status.busy":"2023-04-19T02:31:55.299538Z","iopub.execute_input":"2023-04-19T02:31:55.300207Z","iopub.status.idle":"2023-04-19T02:57:07.940675Z","shell.execute_reply.started":"2023-04-19T02:31:55.300164Z","shell.execute_reply":"2023-04-19T02:57:07.939291Z"},"trusted":true},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":" 0%| | 0/53514 [00:00