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"cells": [ { "cell_type": "markdown", "metadata": { "id": "ApFglcc3TASu" }, "source": [ "# HuggingPics 🤗🖼️\n", "\n", "Fine-tune Vision Transformers for **anything** using images found on the web." ] }, { "cell_type": "code", "metadata": { "id": "XVyU3ScYZ3Hc" }, "source": [ "%%capture\n", "\n", "! pip install transformers pytorch-lightning --quiet\n", "! sudo apt -qq install git-lfs\n", "! git config --global credential.helper store" ], "execution_count": 1, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ihFempth1zK0" }, "source": [ "import requests\n", "import math\n", "import matplotlib.pyplot as plt\n", "import shutil\n", "from getpass import getpass\n", "from PIL import Image, UnidentifiedImageError\n", "from requests.exceptions import HTTPError\n", "from io import BytesIO\n", "from pathlib import Path\n", "import torch\n", "import pytorch_lightning as pl\n", "from huggingface_hub import HfApi, HfFolder, Repository, notebook_login\n", "from torch.utils.data import DataLoader\n", "from torchmetrics import Accuracy\n", "from torchvision.datasets import ImageFolder\n", "from transformers import ViTFeatureExtractor, ViTForImageClassification" ], "execution_count": 2, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Utxw4wj-RS94" }, "source": [ "## Defining your search terms\n", "\n", "Simply replace the terms in the widget below with whatever you want to classify, and we'll download ~150 images for each class for you.\n", "\n", "You can define up to 5 classes. For < 5 classes, just leave the remaining text boxes blank.\n", "\n", "### Examples\n", "\n", "💡 If you need some inspiration, take a look at these examples:\n", "\n", "| | [nateraw/rare-puppers](https://huggingface.co/nateraw/rare-puppers) | [nateraw/pasta-pizza-ravioli](https://huggingface.co/nateraw/pasta-pizza-ravioli) | [nateraw/baseball-stadium-foods](https://huggingface.co/nateraw/baseball-stadium-foods) | [nateraw/denver-nyc-paris](https://huggingface.co/nateraw/denver-nyc-paris) |\n", "| ---------- | ------------------------------------------------------------------- | --------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | --------------------------------------------------------------------------- |\n", "| **term_1** | samoyed | pizza | cotton candy | denver |\n", "| **term_2** | shiba inu | pasta | hamburger | new york city |\n", "| **term_3** | corgi | ravioli | hot dog | paris |\n", "| **term_4** | | | nachos | |\n", "| **term_5** | | | popcorn | |\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "JGyijnL08Cge", "cellView": "form" }, "source": [ "term_1 = \"samoyed\" #@param {type:\"string\"}\n", "term_2 = \"shiba inu\" #@param {type:\"string\"}\n", "term_3 = \"corgi\" #@param {type:\"string\"}\n", "term_4 = \"\" #@param {type:\"string\"}\n", "term_5 = \"\" #@param {type:\"string\"}\n", "\n", "search_terms = sorted([\n", " term_1,\n", " term_2,\n", " term_3,\n", " term_4,\n", " term_5\n", "])\n", "\n", "search_terms = [x for x in search_terms if x.strip() != '']" ], "execution_count": 3, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "8O-Z8-k3DByn" }, "source": [ "## Get Images From The Web\n", "\n", "Here, we loop over your selected search terms and collect ~150 related images for each.\n", "\n", "We'll save them to a new folder named `images/` that is structured to work with [`torchvision.datasets.ImageFolder`](https://pytorch.org/vision/stable/datasets.html#torchvision.datasets.ImageFolder)." ] }, { "cell_type": "code", "metadata": { "id": "jJEH5b3rsdId" }, "source": [ "SEARCH_URL = \"https://huggingface.co/api/experimental/images/search\"\n", "\n", "def get_image_urls_by_term(search_term: str, count=150):\n", " params = {\"q\": search_term, \"license\": \"public\", \"imageType\": \"photo\", \"count\": count}\n", " response = requests.get(SEARCH_URL, params=params)\n", " response.raise_for_status()\n", " response_data = response.json()\n", " image_urls = [img['thumbnailUrl'] for img in response_data['value']]\n", " return image_urls\n", "\n", "\n", "def gen_images_from_urls(urls):\n", " num_skipped = 0\n", " for url in urls:\n", " response = requests.get(url)\n", " if not response.status_code == 200:\n", " num_skipped += 1\n", " try:\n", " img = Image.open(BytesIO(response.content))\n", " yield img\n", " except UnidentifiedImageError:\n", " num_skipped +=1\n", "\n", " print(f\"Retrieved {len(urls) - num_skipped} images. Skipped {num_skipped}.\")\n", "\n", "\n", "def urls_to_image_folder(urls, save_directory):\n", " for i, image in enumerate(gen_images_from_urls(urls)):\n", " image.save(save_directory / f'{i}.jpg')" ], "execution_count": 4, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "PKBb34N9qnNS" }, "source": [ "# data_dir = Path('images')\n", "\n", "# if data_dir.exists():\n", "# shutil.rmtree(data_dir)\n", "\n", "# for search_term in search_terms:\n", "# search_term_dir = data_dir / search_term\n", "# search_term_dir.mkdir(exist_ok=True, parents=True)\n", "# urls = get_image_urls_by_term(search_term)\n", "# print(f\"Saving images of {search_term} to {str(search_term_dir)}...\")\n", "# urls_to_image_folder(urls, search_term_dir)" ], "execution_count": 6, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "v_r5KO0aMIsn" }, "source": [ "## Init Dataset and Split into Training and Validation Sets\n" ] }, { "cell_type": "markdown", "source": [ "Import torch, matplotlib, numpy and torchvision." ], "metadata": { "id": "hjbh8ce3cCgX" } }, { "cell_type": "code", "source": [ "import torch\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import torch.nn.functional as F\n", "from torch import nn\n", "import os\n", "import requests\n", "import shutil\n", "from torchvision import datasets, transforms, models\n", "from pathlib import Path\n", "import tarfile\n", "import scipy.io\n", "import cv2 as cv\n", "import random\n", "import re\n", "import csv" ], "metadata": { "id": "DVVsEvOPcCVG" }, "execution_count": 49, "outputs": [] }, { "cell_type": "markdown", "source": [ "Set the device type variable to cuda or cpu." ], "metadata": { "id": "CSnPOfkQcCMk" } }, { "cell_type": "code", "source": [ "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", "print(f'device = {device}')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rVJxIemEcCFB", "outputId": "66c5e0f7-3f57-45a2-ce5f-f112a1683fef" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "device = cuda:0\n" ] } ] }, { "cell_type": "markdown", "source": [ "Get the Training, Validation & Testing dataset.\n", "\n", "http://ai.stanford.edu/~jkrause/cars/car_dataset.html\n", "\n", "The Cars dataset contains 16,185 images of 196 classes of cars. The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe." ], "metadata": { "id": "HgG2nyARcB6A" } }, { "cell_type": "code", "source": [ "def download_tar_gz_files(url, destination_path, destination_tar_file_name='downloaded_file.tgz'):\n", " # Delete the destination path if it already exist in the current environment.\n", " if os.path.exists(destination_path):\n", " shutil.rmtree(destination_path)\n", " \n", " # Create a new destination directory path.\n", " os.mkdir(destination_path)\n", " \n", " # Download the url to the destination - downloaded_file.tgz.\n", " response = requests.get(url, stream=True)\n", " if response.status_code == 200:\n", " with open(os.path.join(destination_path, destination_tar_file_name), 'wb') as f:\n", " f.write(response.raw.read())\n", "\n", " # Untar the downloaded file to the destination path.\n", " tar_file = tarfile.open(os.path.join(destination_path, destination_tar_file_name))\n", " tar_file.extractall(destination_path) # specify which folder to extract to\n", " tar_file.close()\n", "\n", " # Print the total number of files in the tar file.\n", " print(destination_path)\n", " pathlist = destination_path.glob('**/*.jpg')\n", " image_path_list = []\n", " for path in pathlist:\n", " image_path_list.append(str(path))\n", " print(f\"There are {len(image_path_list)} images in this path.\") " ], "metadata": { "id": "58cLvsCecBye" }, "execution_count": 7, "outputs": [] }, { "cell_type": "markdown", "source": [ "Download training data - http://imagenet.stanford.edu/internal/car196/cars_train.tgz" ], "metadata": { "id": "vsntWxGfcBrb" } }, { "cell_type": "code", "source": [ "training_url = 'http://ai.stanford.edu/~jkrause/car196/cars_train.tgz'\n", "cars_training_path = Path('./cars_training') # Create a path object and pass in the name of the directory (\"cars_training\") where you will be downloading the dataset.\n", "download_tar_gz_files(training_url, cars_training_path)\n", "cars_training_path = Path('./cars_training/cars_train') # set the cars training path to the inner sub directory." ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KTGoe1dDcBjw", "outputId": "b44834be-9293-46e1-da56-16752754bdb6" }, "execution_count": 23, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "cars_training\n", "There are 8144 images in this path.\n" ] } ] }, { "cell_type": "markdown", "source": [ "Download testing data - http://imagenet.stanford.edu/internal/car196/cars_test.tgz" ], "metadata": { "id": "44sYDGCmcBcd" } }, { "cell_type": "code", "source": [ "testing_url = 'http://ai.stanford.edu/~jkrause/car196/cars_test.tgz'\n", "cars_testing_path = Path('./cars_testing')\n", "download_tar_gz_files(testing_url, cars_testing_path)\n", "cars_testing_path = Path('./cars_testing/cars_test')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Fc3m51_lcBVK", "outputId": "07b76d7e-e802-4af4-a603-867baaf60bea" }, "execution_count": 51, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "cars_testing\n", "There are 8041 images in this path.\n" ] } ] }, { "cell_type": "markdown", "source": [ "A devkit, including class labels for training images and bounding boxes for all images, can be downloaded here - http://ai.stanford.edu/~jkrause/cars/car_devkit.tgz" ], "metadata": { "id": "YT7NoPnWcBNL" } }, { "cell_type": "code", "source": [ "annotations_url = 'http://ai.stanford.edu/~jkrause/cars/car_devkit.tgz'\n", "cars_annotations_path = Path('./cars_annotations')\n", "download_tar_gz_files(annotations_url, cars_annotations_path)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_IUYArQQcBFN", "outputId": "25d90f7f-066e-4741-8c5a-e8524822ce21" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "cars_annotations\n", "There are 0 images in this path.\n" ] } ] }, { "cell_type": "markdown", "source": [ "Download test annotations with labels - http://imagenet.stanford.edu/internal/car196/cars_test_annos_withlabels.mat\n", "\n" ], "metadata": { "id": "f6UK3iTocA9K" } }, { "cell_type": "code", "source": [ "annotations_test_url = 'http://ai.stanford.edu/~jkrause/car196/cars_test_annos_withlabels.mat'\n", "cars_test_annotations_path = Path('./cars_annotations/devkit/cars_test_annos_withlabels.mat')\n", "# Download the url to the destination - downloaded_file.tgz.\n", "response = requests.get(annotations_test_url, stream=True)\n", "if response.status_code == 200:\n", " with open(cars_test_annotations_path, 'wb') as f:\n", " f.write(response.raw.read())" ], "metadata": { "id": "CYAqxRdwcA1o" }, "execution_count": 13, "outputs": [] }, { "cell_type": "markdown", "source": [ "Save the annotations to variables." ], "metadata": { "id": "wV0IQpCOcAtl" } }, { "cell_type": "code", "source": [ "cars_test_annos = 'cars_annotations/devkit/cars_test_annos_withlabels.mat'\n", "car_train_annos = 'cars_annotations/devkit/cars_train_annos.mat'\n", "car_meta_annos = 'cars_annotations/devkit/cars_meta.mat'" ], "metadata": { "id": "YsUVdngScAl7" }, "execution_count": 14, "outputs": [] }, { "cell_type": "markdown", "source": [ "Install console progress bar to observe the progression of preparing training and testing data based upon the annotations." ], "metadata": { "id": "x30vZAZKcAdq" } }, { "cell_type": "code", "source": [ "!pip install console_progressbar\n", "from console_progressbar import ProgressBar" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uJ0Ax0VrcAU2", "outputId": "8c501e9f-5fc0-41f2-92e7-f50171b13140" }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting console_progressbar\n", " Downloading console_progressbar-1.1.2.tar.gz (3.6 kB)\n", "Building wheels for collected packages: console-progressbar\n", " Building wheel for console-progressbar (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for console-progressbar: filename=console_progressbar-1.1.2-py3-none-any.whl size=4156 sha256=7f4d0165b9987d8d6602b73b7b39680cff18e4c4085b47b86ad04ce88e38c96f\n", " Stored in directory: /root/.cache/pip/wheels/2b/5f/7d/97976c8b046302df5781c0cbb18fd04b9d58467edae9942bfa\n", "Successfully built console-progressbar\n", "Installing collected packages: console-progressbar\n", "Successfully installed console-progressbar-1.1.2\n" ] } ] }, { "cell_type": "markdown", "source": [ "Load the class name into a list. Sanitize the class names by replacing whitespaces with _ which will be used to create sub directories under test and train output. Example: \"Ram C/V Cargo Van Minivan 2012\" -> Ram_C_V_Cargo_Van_Minivan_2012\n", "\n", "Use scipy.io.loadmat to load the annotations which is in .mat format. Get all the cars_name_list by replacing white spaces with '_' symbol." ], "metadata": { "id": "DPFtkyUScALG" } }, { "cell_type": "code", "source": [ "cars_name_list = []\n", "print(f\"Processing annotations metadata {car_meta_annos}\")\n", "cars_meta = scipy.io.loadmat(car_meta_annos)\n", "class_names = cars_meta['class_names'] # shape=(1, 196)\n", "class_names = np.transpose(class_names)\n", "print('class_names.shape: ' + str(class_names.shape))\n", "print(f'type of class_names = {type(class_names)}')\n", "print(f'size of class_names = {class_names.size}')\n", "for index in range(class_names.size):\n", " # Replace white space and / with _. Example: \"Ram C/V Cargo Van Minivan 2012\" -> Ram_C_V_Cargo_Van_Minivan_2012\n", " cars_name_list.append(re.sub(r\"(/|\\s+)\", r'_', class_names[index][0][0]))\n", "\n", "print('length of cars_name_list = {}, Sample class_name: [{}]'.format(len(cars_name_list), cars_name_list[3]))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HxCNjWnjb__v", "outputId": "ec026190-d523-4b82-d435-580bb70b06c7" }, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Processing annotations metadata cars_annotations/devkit/cars_meta.mat\n", "class_names.shape: (196, 1)\n", "type of class_names = \n", "size of class_names = 196\n", "length of cars_name_list = 196, Sample class_name: [Acura_TL_Type-S_2008]\n" ] } ] }, { "cell_type": "markdown", "source": [ "For each of the class name, create a sub directory in cars_testing_output directory and cars_training_output directory." ], "metadata": { "id": "VZVidNTad79a" } }, { "cell_type": "code", "source": [ "cars_training_output_path = Path('./cars_training_output')\n", "cars_testing_output_path = Path('./cars_testing_output')" ], "metadata": { "id": "sPI5fMqFd7zV" }, "execution_count": 34, "outputs": [] }, { "cell_type": "markdown", "source": [ "Process the train data and classify them into the cars_testing_output and cars_training_output directory with the correct class name as the sub directory name." ], "metadata": { "id": "RtTW9F6Nd7ri" } }, { "cell_type": "code", "source": [ "def create_destination_directory_with_names(dest, name_list):\n", " # Delete the training output dir.\n", " if os.path.exists(dest):\n", " shutil.rmtree(dest)\n", " if not dest.exists():\n", " dest.mkdir()\n", " #Create sub directories with the name of the car names in the destination.\n", " print(\"creating sub directories in {} with names found in {}.\".format(\n", " dest, name_list))\n", " for o in name_list:\n", " final_dest = (dest/o)\n", " final_dest.mkdir(exist_ok=True)\n", "\n", "\n", "create_destination_directory_with_names(cars_training_output_path, cars_name_list)\n", "create_destination_directory_with_names(cars_testing_output_path, cars_name_list)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vkbpDxFid7kZ", "outputId": "80419ad6-fcb7-43e4-848d-45a47ceb9382" }, "execution_count": 35, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "creating sub directories in cars_training_output with names found in ['AM_General_Hummer_SUV_2000', 'Acura_RL_Sedan_2012', 'Acura_TL_Sedan_2012', 'Acura_TL_Type-S_2008', 'Acura_TSX_Sedan_2012', 'Acura_Integra_Type_R_2001', 'Acura_ZDX_Hatchback_2012', 'Aston_Martin_V8_Vantage_Convertible_2012', 'Aston_Martin_V8_Vantage_Coupe_2012', 'Aston_Martin_Virage_Convertible_2012', 'Aston_Martin_Virage_Coupe_2012', 'Audi_RS_4_Convertible_2008', 'Audi_A5_Coupe_2012', 'Audi_TTS_Coupe_2012', 'Audi_R8_Coupe_2012', 'Audi_V8_Sedan_1994', 'Audi_100_Sedan_1994', 'Audi_100_Wagon_1994', 'Audi_TT_Hatchback_2011', 'Audi_S6_Sedan_2011', 'Audi_S5_Convertible_2012', 'Audi_S5_Coupe_2012', 'Audi_S4_Sedan_2012', 'Audi_S4_Sedan_2007', 'Audi_TT_RS_Coupe_2012', 'BMW_ActiveHybrid_5_Sedan_2012', 'BMW_1_Series_Convertible_2012', 'BMW_1_Series_Coupe_2012', 'BMW_3_Series_Sedan_2012', 'BMW_3_Series_Wagon_2012', 'BMW_6_Series_Convertible_2007', 'BMW_X5_SUV_2007', 'BMW_X6_SUV_2012', 'BMW_M3_Coupe_2012', 'BMW_M5_Sedan_2010', 'BMW_M6_Convertible_2010', 'BMW_X3_SUV_2012', 'BMW_Z4_Convertible_2012', 'Bentley_Continental_Supersports_Conv._Convertible_2012', 'Bentley_Arnage_Sedan_2009', 'Bentley_Mulsanne_Sedan_2011', 'Bentley_Continental_GT_Coupe_2012', 'Bentley_Continental_GT_Coupe_2007', 'Bentley_Continental_Flying_Spur_Sedan_2007', 'Bugatti_Veyron_16.4_Convertible_2009', 'Bugatti_Veyron_16.4_Coupe_2009', 'Buick_Regal_GS_2012', 'Buick_Rainier_SUV_2007', 'Buick_Verano_Sedan_2012', 'Buick_Enclave_SUV_2012', 'Cadillac_CTS-V_Sedan_2012', 'Cadillac_SRX_SUV_2012', 'Cadillac_Escalade_EXT_Crew_Cab_2007', 'Chevrolet_Silverado_1500_Hybrid_Crew_Cab_2012', 'Chevrolet_Corvette_Convertible_2012', 'Chevrolet_Corvette_ZR1_2012', 'Chevrolet_Corvette_Ron_Fellows_Edition_Z06_2007', 'Chevrolet_Traverse_SUV_2012', 'Chevrolet_Camaro_Convertible_2012', 'Chevrolet_HHR_SS_2010', 'Chevrolet_Impala_Sedan_2007', 'Chevrolet_Tahoe_Hybrid_SUV_2012', 'Chevrolet_Sonic_Sedan_2012', 'Chevrolet_Express_Cargo_Van_2007', 'Chevrolet_Avalanche_Crew_Cab_2012', 'Chevrolet_Cobalt_SS_2010', 'Chevrolet_Malibu_Hybrid_Sedan_2010', 'Chevrolet_TrailBlazer_SS_2009', 'Chevrolet_Silverado_2500HD_Regular_Cab_2012', 'Chevrolet_Silverado_1500_Classic_Extended_Cab_2007', 'Chevrolet_Express_Van_2007', 'Chevrolet_Monte_Carlo_Coupe_2007', 'Chevrolet_Malibu_Sedan_2007', 'Chevrolet_Silverado_1500_Extended_Cab_2012', 'Chevrolet_Silverado_1500_Regular_Cab_2012', 'Chrysler_Aspen_SUV_2009', 'Chrysler_Sebring_Convertible_2010', 'Chrysler_Town_and_Country_Minivan_2012', 'Chrysler_300_SRT-8_2010', 'Chrysler_Crossfire_Convertible_2008', 'Chrysler_PT_Cruiser_Convertible_2008', 'Daewoo_Nubira_Wagon_2002', 'Dodge_Caliber_Wagon_2012', 'Dodge_Caliber_Wagon_2007', 'Dodge_Caravan_Minivan_1997', 'Dodge_Ram_Pickup_3500_Crew_Cab_2010', 'Dodge_Ram_Pickup_3500_Quad_Cab_2009', 'Dodge_Sprinter_Cargo_Van_2009', 'Dodge_Journey_SUV_2012', 'Dodge_Dakota_Crew_Cab_2010', 'Dodge_Dakota_Club_Cab_2007', 'Dodge_Magnum_Wagon_2008', 'Dodge_Challenger_SRT8_2011', 'Dodge_Durango_SUV_2012', 'Dodge_Durango_SUV_2007', 'Dodge_Charger_Sedan_2012', 'Dodge_Charger_SRT-8_2009', 'Eagle_Talon_Hatchback_1998', 'FIAT_500_Abarth_2012', 'FIAT_500_Convertible_2012', 'Ferrari_FF_Coupe_2012', 'Ferrari_California_Convertible_2012', 'Ferrari_458_Italia_Convertible_2012', 'Ferrari_458_Italia_Coupe_2012', 'Fisker_Karma_Sedan_2012', 'Ford_F-450_Super_Duty_Crew_Cab_2012', 'Ford_Mustang_Convertible_2007', 'Ford_Freestar_Minivan_2007', 'Ford_Expedition_EL_SUV_2009', 'Ford_Edge_SUV_2012', 'Ford_Ranger_SuperCab_2011', 'Ford_GT_Coupe_2006', 'Ford_F-150_Regular_Cab_2012', 'Ford_F-150_Regular_Cab_2007', 'Ford_Focus_Sedan_2007', 'Ford_E-Series_Wagon_Van_2012', 'Ford_Fiesta_Sedan_2012', 'GMC_Terrain_SUV_2012', 'GMC_Savana_Van_2012', 'GMC_Yukon_Hybrid_SUV_2012', 'GMC_Acadia_SUV_2012', 'GMC_Canyon_Extended_Cab_2012', 'Geo_Metro_Convertible_1993', 'HUMMER_H3T_Crew_Cab_2010', 'HUMMER_H2_SUT_Crew_Cab_2009', 'Honda_Odyssey_Minivan_2012', 'Honda_Odyssey_Minivan_2007', 'Honda_Accord_Coupe_2012', 'Honda_Accord_Sedan_2012', 'Hyundai_Veloster_Hatchback_2012', 'Hyundai_Santa_Fe_SUV_2012', 'Hyundai_Tucson_SUV_2012', 'Hyundai_Veracruz_SUV_2012', 'Hyundai_Sonata_Hybrid_Sedan_2012', 'Hyundai_Elantra_Sedan_2007', 'Hyundai_Accent_Sedan_2012', 'Hyundai_Genesis_Sedan_2012', 'Hyundai_Sonata_Sedan_2012', 'Hyundai_Elantra_Touring_Hatchback_2012', 'Hyundai_Azera_Sedan_2012', 'Infiniti_G_Coupe_IPL_2012', 'Infiniti_QX56_SUV_2011', 'Isuzu_Ascender_SUV_2008', 'Jaguar_XK_XKR_2012', 'Jeep_Patriot_SUV_2012', 'Jeep_Wrangler_SUV_2012', 'Jeep_Liberty_SUV_2012', 'Jeep_Grand_Cherokee_SUV_2012', 'Jeep_Compass_SUV_2012', 'Lamborghini_Reventon_Coupe_2008', 'Lamborghini_Aventador_Coupe_2012', 'Lamborghini_Gallardo_LP_570-4_Superleggera_2012', 'Lamborghini_Diablo_Coupe_2001', 'Land_Rover_Range_Rover_SUV_2012', 'Land_Rover_LR2_SUV_2012', 'Lincoln_Town_Car_Sedan_2011', 'MINI_Cooper_Roadster_Convertible_2012', 'Maybach_Landaulet_Convertible_2012', 'Mazda_Tribute_SUV_2011', 'McLaren_MP4-12C_Coupe_2012', 'Mercedes-Benz_300-Class_Convertible_1993', 'Mercedes-Benz_C-Class_Sedan_2012', 'Mercedes-Benz_SL-Class_Coupe_2009', 'Mercedes-Benz_E-Class_Sedan_2012', 'Mercedes-Benz_S-Class_Sedan_2012', 'Mercedes-Benz_Sprinter_Van_2012', 'Mitsubishi_Lancer_Sedan_2012', 'Nissan_Leaf_Hatchback_2012', 'Nissan_NV_Passenger_Van_2012', 'Nissan_Juke_Hatchback_2012', 'Nissan_240SX_Coupe_1998', 'Plymouth_Neon_Coupe_1999', 'Porsche_Panamera_Sedan_2012', 'Ram_C_V_Cargo_Van_Minivan_2012', 'Rolls-Royce_Phantom_Drophead_Coupe_Convertible_2012', 'Rolls-Royce_Ghost_Sedan_2012', 'Rolls-Royce_Phantom_Sedan_2012', 'Scion_xD_Hatchback_2012', 'Spyker_C8_Convertible_2009', 'Spyker_C8_Coupe_2009', 'Suzuki_Aerio_Sedan_2007', 'Suzuki_Kizashi_Sedan_2012', 'Suzuki_SX4_Hatchback_2012', 'Suzuki_SX4_Sedan_2012', 'Tesla_Model_S_Sedan_2012', 'Toyota_Sequoia_SUV_2012', 'Toyota_Camry_Sedan_2012', 'Toyota_Corolla_Sedan_2012', 'Toyota_4Runner_SUV_2012', 'Volkswagen_Golf_Hatchback_2012', 'Volkswagen_Golf_Hatchback_1991', 'Volkswagen_Beetle_Hatchback_2012', 'Volvo_C30_Hatchback_2012', 'Volvo_240_Sedan_1993', 'Volvo_XC90_SUV_2007', 'smart_fortwo_Convertible_2012'].\n", "creating sub directories in cars_testing_output with names found in ['AM_General_Hummer_SUV_2000', 'Acura_RL_Sedan_2012', 'Acura_TL_Sedan_2012', 'Acura_TL_Type-S_2008', 'Acura_TSX_Sedan_2012', 'Acura_Integra_Type_R_2001', 'Acura_ZDX_Hatchback_2012', 'Aston_Martin_V8_Vantage_Convertible_2012', 'Aston_Martin_V8_Vantage_Coupe_2012', 'Aston_Martin_Virage_Convertible_2012', 'Aston_Martin_Virage_Coupe_2012', 'Audi_RS_4_Convertible_2008', 'Audi_A5_Coupe_2012', 'Audi_TTS_Coupe_2012', 'Audi_R8_Coupe_2012', 'Audi_V8_Sedan_1994', 'Audi_100_Sedan_1994', 'Audi_100_Wagon_1994', 'Audi_TT_Hatchback_2011', 'Audi_S6_Sedan_2011', 'Audi_S5_Convertible_2012', 'Audi_S5_Coupe_2012', 'Audi_S4_Sedan_2012', 'Audi_S4_Sedan_2007', 'Audi_TT_RS_Coupe_2012', 'BMW_ActiveHybrid_5_Sedan_2012', 'BMW_1_Series_Convertible_2012', 'BMW_1_Series_Coupe_2012', 'BMW_3_Series_Sedan_2012', 'BMW_3_Series_Wagon_2012', 'BMW_6_Series_Convertible_2007', 'BMW_X5_SUV_2007', 'BMW_X6_SUV_2012', 'BMW_M3_Coupe_2012', 'BMW_M5_Sedan_2010', 'BMW_M6_Convertible_2010', 'BMW_X3_SUV_2012', 'BMW_Z4_Convertible_2012', 'Bentley_Continental_Supersports_Conv._Convertible_2012', 'Bentley_Arnage_Sedan_2009', 'Bentley_Mulsanne_Sedan_2011', 'Bentley_Continental_GT_Coupe_2012', 'Bentley_Continental_GT_Coupe_2007', 'Bentley_Continental_Flying_Spur_Sedan_2007', 'Bugatti_Veyron_16.4_Convertible_2009', 'Bugatti_Veyron_16.4_Coupe_2009', 'Buick_Regal_GS_2012', 'Buick_Rainier_SUV_2007', 'Buick_Verano_Sedan_2012', 'Buick_Enclave_SUV_2012', 'Cadillac_CTS-V_Sedan_2012', 'Cadillac_SRX_SUV_2012', 'Cadillac_Escalade_EXT_Crew_Cab_2007', 'Chevrolet_Silverado_1500_Hybrid_Crew_Cab_2012', 'Chevrolet_Corvette_Convertible_2012', 'Chevrolet_Corvette_ZR1_2012', 'Chevrolet_Corvette_Ron_Fellows_Edition_Z06_2007', 'Chevrolet_Traverse_SUV_2012', 'Chevrolet_Camaro_Convertible_2012', 'Chevrolet_HHR_SS_2010', 'Chevrolet_Impala_Sedan_2007', 'Chevrolet_Tahoe_Hybrid_SUV_2012', 'Chevrolet_Sonic_Sedan_2012', 'Chevrolet_Express_Cargo_Van_2007', 'Chevrolet_Avalanche_Crew_Cab_2012', 'Chevrolet_Cobalt_SS_2010', 'Chevrolet_Malibu_Hybrid_Sedan_2010', 'Chevrolet_TrailBlazer_SS_2009', 'Chevrolet_Silverado_2500HD_Regular_Cab_2012', 'Chevrolet_Silverado_1500_Classic_Extended_Cab_2007', 'Chevrolet_Express_Van_2007', 'Chevrolet_Monte_Carlo_Coupe_2007', 'Chevrolet_Malibu_Sedan_2007', 'Chevrolet_Silverado_1500_Extended_Cab_2012', 'Chevrolet_Silverado_1500_Regular_Cab_2012', 'Chrysler_Aspen_SUV_2009', 'Chrysler_Sebring_Convertible_2010', 'Chrysler_Town_and_Country_Minivan_2012', 'Chrysler_300_SRT-8_2010', 'Chrysler_Crossfire_Convertible_2008', 'Chrysler_PT_Cruiser_Convertible_2008', 'Daewoo_Nubira_Wagon_2002', 'Dodge_Caliber_Wagon_2012', 'Dodge_Caliber_Wagon_2007', 'Dodge_Caravan_Minivan_1997', 'Dodge_Ram_Pickup_3500_Crew_Cab_2010', 'Dodge_Ram_Pickup_3500_Quad_Cab_2009', 'Dodge_Sprinter_Cargo_Van_2009', 'Dodge_Journey_SUV_2012', 'Dodge_Dakota_Crew_Cab_2010', 'Dodge_Dakota_Club_Cab_2007', 'Dodge_Magnum_Wagon_2008', 'Dodge_Challenger_SRT8_2011', 'Dodge_Durango_SUV_2012', 'Dodge_Durango_SUV_2007', 'Dodge_Charger_Sedan_2012', 'Dodge_Charger_SRT-8_2009', 'Eagle_Talon_Hatchback_1998', 'FIAT_500_Abarth_2012', 'FIAT_500_Convertible_2012', 'Ferrari_FF_Coupe_2012', 'Ferrari_California_Convertible_2012', 'Ferrari_458_Italia_Convertible_2012', 'Ferrari_458_Italia_Coupe_2012', 'Fisker_Karma_Sedan_2012', 'Ford_F-450_Super_Duty_Crew_Cab_2012', 'Ford_Mustang_Convertible_2007', 'Ford_Freestar_Minivan_2007', 'Ford_Expedition_EL_SUV_2009', 'Ford_Edge_SUV_2012', 'Ford_Ranger_SuperCab_2011', 'Ford_GT_Coupe_2006', 'Ford_F-150_Regular_Cab_2012', 'Ford_F-150_Regular_Cab_2007', 'Ford_Focus_Sedan_2007', 'Ford_E-Series_Wagon_Van_2012', 'Ford_Fiesta_Sedan_2012', 'GMC_Terrain_SUV_2012', 'GMC_Savana_Van_2012', 'GMC_Yukon_Hybrid_SUV_2012', 'GMC_Acadia_SUV_2012', 'GMC_Canyon_Extended_Cab_2012', 'Geo_Metro_Convertible_1993', 'HUMMER_H3T_Crew_Cab_2010', 'HUMMER_H2_SUT_Crew_Cab_2009', 'Honda_Odyssey_Minivan_2012', 'Honda_Odyssey_Minivan_2007', 'Honda_Accord_Coupe_2012', 'Honda_Accord_Sedan_2012', 'Hyundai_Veloster_Hatchback_2012', 'Hyundai_Santa_Fe_SUV_2012', 'Hyundai_Tucson_SUV_2012', 'Hyundai_Veracruz_SUV_2012', 'Hyundai_Sonata_Hybrid_Sedan_2012', 'Hyundai_Elantra_Sedan_2007', 'Hyundai_Accent_Sedan_2012', 'Hyundai_Genesis_Sedan_2012', 'Hyundai_Sonata_Sedan_2012', 'Hyundai_Elantra_Touring_Hatchback_2012', 'Hyundai_Azera_Sedan_2012', 'Infiniti_G_Coupe_IPL_2012', 'Infiniti_QX56_SUV_2011', 'Isuzu_Ascender_SUV_2008', 'Jaguar_XK_XKR_2012', 'Jeep_Patriot_SUV_2012', 'Jeep_Wrangler_SUV_2012', 'Jeep_Liberty_SUV_2012', 'Jeep_Grand_Cherokee_SUV_2012', 'Jeep_Compass_SUV_2012', 'Lamborghini_Reventon_Coupe_2008', 'Lamborghini_Aventador_Coupe_2012', 'Lamborghini_Gallardo_LP_570-4_Superleggera_2012', 'Lamborghini_Diablo_Coupe_2001', 'Land_Rover_Range_Rover_SUV_2012', 'Land_Rover_LR2_SUV_2012', 'Lincoln_Town_Car_Sedan_2011', 'MINI_Cooper_Roadster_Convertible_2012', 'Maybach_Landaulet_Convertible_2012', 'Mazda_Tribute_SUV_2011', 'McLaren_MP4-12C_Coupe_2012', 'Mercedes-Benz_300-Class_Convertible_1993', 'Mercedes-Benz_C-Class_Sedan_2012', 'Mercedes-Benz_SL-Class_Coupe_2009', 'Mercedes-Benz_E-Class_Sedan_2012', 'Mercedes-Benz_S-Class_Sedan_2012', 'Mercedes-Benz_Sprinter_Van_2012', 'Mitsubishi_Lancer_Sedan_2012', 'Nissan_Leaf_Hatchback_2012', 'Nissan_NV_Passenger_Van_2012', 'Nissan_Juke_Hatchback_2012', 'Nissan_240SX_Coupe_1998', 'Plymouth_Neon_Coupe_1999', 'Porsche_Panamera_Sedan_2012', 'Ram_C_V_Cargo_Van_Minivan_2012', 'Rolls-Royce_Phantom_Drophead_Coupe_Convertible_2012', 'Rolls-Royce_Ghost_Sedan_2012', 'Rolls-Royce_Phantom_Sedan_2012', 'Scion_xD_Hatchback_2012', 'Spyker_C8_Convertible_2009', 'Spyker_C8_Coupe_2009', 'Suzuki_Aerio_Sedan_2007', 'Suzuki_Kizashi_Sedan_2012', 'Suzuki_SX4_Hatchback_2012', 'Suzuki_SX4_Sedan_2012', 'Tesla_Model_S_Sedan_2012', 'Toyota_Sequoia_SUV_2012', 'Toyota_Camry_Sedan_2012', 'Toyota_Corolla_Sedan_2012', 'Toyota_4Runner_SUV_2012', 'Volkswagen_Golf_Hatchback_2012', 'Volkswagen_Golf_Hatchback_1991', 'Volkswagen_Beetle_Hatchback_2012', 'Volvo_C30_Hatchback_2012', 'Volvo_240_Sedan_1993', 'Volvo_XC90_SUV_2007', 'smart_fortwo_Convertible_2012'].\n" ] } ] }, { "cell_type": "markdown", "source": [ "Organize training and testing data into sub-folders containing the class name as the sub-folder name based on the annotations bounding box." ], "metadata": { "id": "lGtAP_Xid7cV" } }, { "cell_type": "code", "source": [ "img_width, img_height = 224, 224\n", "\n", "def save_data_into_directory_based_on_bounding_box(fnames, labels, bboxes, src_folder, dst_folder, csv_file_name = None):\n", " num_samples = len(fnames)\n", " pb = ProgressBar(total=100, prefix='Saving data', suffix='', decimals=3, length=50, fill='=')\n", " writer = None\n", " if csv_file_name:\n", " fp = open(csv_file_name, 'a', newline='')\n", " fieldnames = ['file_name', 'label']\n", " writer = csv.DictWriter(fp, fieldnames=fieldnames)\n", " writer.writeheader()\n", "\n", " for i in range(num_samples):\n", " fname = fnames[i]\n", " label = labels[i]\n", " (x1, y1, x2, y2) = bboxes[i]\n", "\n", " src_path = os.path.join(src_folder, fname)\n", " src_image = cv.imread(src_path)\n", " height, width = src_image.shape[:2]\n", " # margins of 16 pixels\n", " margin = 16\n", " x1 = max(0, x1 - margin)\n", " y1 = max(0, y1 - margin)\n", " x2 = min(x2 + margin, width)\n", " y2 = min(y2 + margin, height)\n", " # print(\"{} -> {}\".format(fname, label))\n", " pb.print_progress_bar((i + 1) * 100 / num_samples)\n", " dst_path = os.path.join(dst_folder, label)\n", " if not os.path.exists(dst_path):\n", " os.makedirs(dst_path)\n", " dst_path = os.path.join(dst_path, fname)\n", "\n", " crop_image = src_image[y1:y2, x1:x2]\n", " dst_img = cv.resize(src=crop_image, dsize=(img_height, img_width))\n", " cv.imwrite(dst_path, dst_img)\n", " if writer:\n", " writer.writerow({'file_name': dst_path, 'label': label})" ], "metadata": { "id": "5tLFY3dXd7Uy" }, "execution_count": 42, "outputs": [] }, { "cell_type": "markdown", "source": [ "Process Data" ], "metadata": { "id": "JMeYl_nufnVv" } }, { "cell_type": "code", "source": [ "def get_bboxes_classid_fname_labels_from_annotations(car_annotation_path):\n", " print(f\"Processing data {car_annotation_path}.\")\n", " cars_annos = scipy.io.loadmat(car_annotation_path)\n", " annotations = cars_annos['annotations']\n", " annotations = np.transpose(annotations)\n", "\n", " fnames = []\n", " class_ids = []\n", " bboxes = []\n", " labels = []\n", "\n", " for annotation in annotations:\n", " bbox_x1 = annotation[0][0][0][0]\n", " bbox_y1 = annotation[0][1][0][0]\n", " bbox_x2 = annotation[0][2][0][0]\n", " bbox_y2 = annotation[0][3][0][0]\n", " class_id = annotation[0][4][0][0]\n", " labels.append(cars_name_list[class_id-1])\n", " fname = annotation[0][5][0]\n", " bboxes.append((bbox_x1, bbox_y1, bbox_x2, bbox_y2))\n", " class_ids.append(class_id)\n", " fnames.append(fname)\n", " return bboxes, class_ids, fnames, labels" ], "metadata": { "id": "FJv3TQjgfnMA" }, "execution_count": 43, "outputs": [] }, { "cell_type": "markdown", "source": [ "Invoke the above defined functions for training data." ], "metadata": { "id": "44JQg3wchg1W" } }, { "cell_type": "code", "source": [ "training_labels = []\n", "if not len(training_labels):\n", " bboxes, training_class_ids, training_fnames, training_labels = get_bboxes_classid_fname_labels_from_annotations(car_train_annos)\n", " save_data_into_directory_based_on_bounding_box(training_fnames, training_labels, bboxes, src_folder = (cars_training_path),\n", " dst_folder = cars_training_output_path)\n", " training_labels_count=len(np.unique(training_labels))\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8qKMchYhhgt0", "outputId": "f98a5307-22b7-447d-bfaa-d72532c2d60f" }, "execution_count": 44, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Processing data cars_annotations/devkit/cars_train_annos.mat.\n", "Saving data |==================================================| 100.000% \n" ] } ] }, { "cell_type": "markdown", "source": [ "Invoke the above defined functions for testing data." ], "metadata": { "id": "_qFcguTHd7M4" } }, { "cell_type": "markdown", "source": [ "Print the total number of downloaded cars training files." ], "metadata": { "id": "ascpBF5zkNi9" } }, { "cell_type": "code", "source": [ "pathlist = cars_training_output_path.glob('**/*.jpg')\n", "image_path_list = []\n", "for path in pathlist:\n", " image_path_list.append(str(path))\n", "print(f\"There are {len(image_path_list)} images in this path.\") " ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "d2ufnucDkNZO", "outputId": "0b3fd56e-6f0b-4e06-9fdc-9f62eafd9fc1" }, "execution_count": 47, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "There are 8144 images in this path.\n" ] } ] }, { "cell_type": "markdown", "source": [ "Print the training class ids, training labels, training_fnames and the total number of unique cars used for training." ], "metadata": { "id": "i7RqRY6akRqJ" } }, { "cell_type": "code", "source": [ "print(\"training_class_ids = {}.\".format(np.unique(training_class_ids)))\n", "print(\"training_labels = {}.\".format(np.unique(training_labels)))\n", "print(\"training_fnames = {}.\".format(training_fnames))\n", "print('The number of different cars = {}'.format(training_labels_count))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bV60E_k0kRfO", "outputId": "4372512a-f7da-46e4-d6c9-3cd47b62238f" }, "execution_count": 46, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "training_class_ids = [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18\n", " 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36\n", " 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54\n", " 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72\n", " 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90\n", " 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108\n", " 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126\n", " 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144\n", " 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162\n", " 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180\n", " 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196].\n", "training_labels = ['AM_General_Hummer_SUV_2000' 'Acura_Integra_Type_R_2001'\n", " 'Acura_RL_Sedan_2012' 'Acura_TL_Sedan_2012' 'Acura_TL_Type-S_2008'\n", " 'Acura_TSX_Sedan_2012' 'Acura_ZDX_Hatchback_2012'\n", " 'Aston_Martin_V8_Vantage_Convertible_2012'\n", " 'Aston_Martin_V8_Vantage_Coupe_2012'\n", " 'Aston_Martin_Virage_Convertible_2012' 'Aston_Martin_Virage_Coupe_2012'\n", " 'Audi_100_Sedan_1994' 'Audi_100_Wagon_1994' 'Audi_A5_Coupe_2012'\n", " 'Audi_R8_Coupe_2012' 'Audi_RS_4_Convertible_2008' 'Audi_S4_Sedan_2007'\n", " 'Audi_S4_Sedan_2012' 'Audi_S5_Convertible_2012' 'Audi_S5_Coupe_2012'\n", " 'Audi_S6_Sedan_2011' 'Audi_TTS_Coupe_2012' 'Audi_TT_Hatchback_2011'\n", " 'Audi_TT_RS_Coupe_2012' 'Audi_V8_Sedan_1994'\n", " 'BMW_1_Series_Convertible_2012' 'BMW_1_Series_Coupe_2012'\n", " 'BMW_3_Series_Sedan_2012' 'BMW_3_Series_Wagon_2012'\n", " 'BMW_6_Series_Convertible_2007' 'BMW_ActiveHybrid_5_Sedan_2012'\n", " 'BMW_M3_Coupe_2012' 'BMW_M5_Sedan_2010' 'BMW_M6_Convertible_2010'\n", " 'BMW_X3_SUV_2012' 'BMW_X5_SUV_2007' 'BMW_X6_SUV_2012'\n", " 'BMW_Z4_Convertible_2012' 'Bentley_Arnage_Sedan_2009'\n", " 'Bentley_Continental_Flying_Spur_Sedan_2007'\n", " 'Bentley_Continental_GT_Coupe_2007' 'Bentley_Continental_GT_Coupe_2012'\n", " 'Bentley_Continental_Supersports_Conv._Convertible_2012'\n", " 'Bentley_Mulsanne_Sedan_2011' 'Bugatti_Veyron_16.4_Convertible_2009'\n", " 'Bugatti_Veyron_16.4_Coupe_2009' 'Buick_Enclave_SUV_2012'\n", " 'Buick_Rainier_SUV_2007' 'Buick_Regal_GS_2012' 'Buick_Verano_Sedan_2012'\n", " 'Cadillac_CTS-V_Sedan_2012' 'Cadillac_Escalade_EXT_Crew_Cab_2007'\n", " 'Cadillac_SRX_SUV_2012' 'Chevrolet_Avalanche_Crew_Cab_2012'\n", " 'Chevrolet_Camaro_Convertible_2012' 'Chevrolet_Cobalt_SS_2010'\n", " 'Chevrolet_Corvette_Convertible_2012'\n", " 'Chevrolet_Corvette_Ron_Fellows_Edition_Z06_2007'\n", " 'Chevrolet_Corvette_ZR1_2012' 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'Ford_Ranger_SuperCab_2011'\n", " 'GMC_Acadia_SUV_2012' 'GMC_Canyon_Extended_Cab_2012'\n", " 'GMC_Savana_Van_2012' 'GMC_Terrain_SUV_2012' 'GMC_Yukon_Hybrid_SUV_2012'\n", " 'Geo_Metro_Convertible_1993' 'HUMMER_H2_SUT_Crew_Cab_2009'\n", " 'HUMMER_H3T_Crew_Cab_2010' 'Honda_Accord_Coupe_2012'\n", " 'Honda_Accord_Sedan_2012' 'Honda_Odyssey_Minivan_2007'\n", " 'Honda_Odyssey_Minivan_2012' 'Hyundai_Accent_Sedan_2012'\n", " 'Hyundai_Azera_Sedan_2012' 'Hyundai_Elantra_Sedan_2007'\n", " 'Hyundai_Elantra_Touring_Hatchback_2012' 'Hyundai_Genesis_Sedan_2012'\n", " 'Hyundai_Santa_Fe_SUV_2012' 'Hyundai_Sonata_Hybrid_Sedan_2012'\n", " 'Hyundai_Sonata_Sedan_2012' 'Hyundai_Tucson_SUV_2012'\n", " 'Hyundai_Veloster_Hatchback_2012' 'Hyundai_Veracruz_SUV_2012'\n", " 'Infiniti_G_Coupe_IPL_2012' 'Infiniti_QX56_SUV_2011'\n", " 'Isuzu_Ascender_SUV_2008' 'Jaguar_XK_XKR_2012' 'Jeep_Compass_SUV_2012'\n", " 'Jeep_Grand_Cherokee_SUV_2012' 'Jeep_Liberty_SUV_2012'\n", " 'Jeep_Patriot_SUV_2012' 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'08032.jpg', '08033.jpg', '08034.jpg', '08035.jpg', '08036.jpg', '08037.jpg', '08038.jpg', '08039.jpg', '08040.jpg', '08041.jpg', '08042.jpg', '08043.jpg', '08044.jpg', '08045.jpg', '08046.jpg', '08047.jpg', '08048.jpg', '08049.jpg', '08050.jpg', '08051.jpg', '08052.jpg', '08053.jpg', '08054.jpg', '08055.jpg', '08056.jpg', '08057.jpg', '08058.jpg', '08059.jpg', '08060.jpg', '08061.jpg', '08062.jpg', '08063.jpg', '08064.jpg', '08065.jpg', '08066.jpg', '08067.jpg', '08068.jpg', '08069.jpg', '08070.jpg', '08071.jpg', '08072.jpg', '08073.jpg', '08074.jpg', '08075.jpg', '08076.jpg', '08077.jpg', '08078.jpg', '08079.jpg', '08080.jpg', '08081.jpg', '08082.jpg', '08083.jpg', '08084.jpg', '08085.jpg', '08086.jpg', '08087.jpg', '08088.jpg', '08089.jpg', '08090.jpg', '08091.jpg', '08092.jpg', '08093.jpg', '08094.jpg', '08095.jpg', '08096.jpg', '08097.jpg', '08098.jpg', '08099.jpg', '08100.jpg', '08101.jpg', '08102.jpg', '08103.jpg', '08104.jpg', '08105.jpg', '08106.jpg', '08107.jpg', '08108.jpg', '08109.jpg', '08110.jpg', '08111.jpg', '08112.jpg', '08113.jpg', '08114.jpg', '08115.jpg', '08116.jpg', '08117.jpg', '08118.jpg', '08119.jpg', '08120.jpg', '08121.jpg', '08122.jpg', '08123.jpg', '08124.jpg', '08125.jpg', '08126.jpg', '08127.jpg', '08128.jpg', '08129.jpg', '08130.jpg', '08131.jpg', '08132.jpg', '08133.jpg', '08134.jpg', '08135.jpg', '08136.jpg', '08137.jpg', '08138.jpg', '08139.jpg', '08140.jpg', '08141.jpg', '08142.jpg', '08143.jpg', '08144.jpg'].\n", "The number of different cars = 196\n" ] } ] }, { "cell_type": "markdown", "source": [ "Invoke the above defined functions for testing data.\n", "\n", "Print the total number of downloaded cars testing files.\n", "\n", "Print the testing class ids, testing labels, testing_fnames and the total number of unique cars used for testing." ], "metadata": { "id": "ZetKJNfXoh_9" } }, { "cell_type": "code", "source": [ "testing_labels = []\n", "if not len(testing_labels):\n", " testing_bboxes, testing_class_ids, testing_fnames, testing_labels = get_bboxes_classid_fname_labels_from_annotations(cars_test_annos)\n", " save_data_into_directory_based_on_bounding_box(testing_fnames, testing_labels, testing_bboxes, src_folder = (cars_testing_path),\n", " dst_folder = cars_testing_output_path,\n", " csv_file_name = 'testing_data.csv')\n", " testing_labels_count=len(np.unique(testing_labels))\n", "\n", "pathlist = cars_testing_output_path.glob('**/*.jpg')\n", "image_path_list = []\n", "for path in pathlist:\n", " image_path_list.append(str(path))\n", "print(f\"There are {len(image_path_list)} images in this path.\") \n", "\n", "print(\"testing_class_ids = {}.\".format(np.unique(testing_class_ids)))\n", "print(\"testing_labels = {}.\".format(np.unique(testing_labels)))\n", "print(\"testing_fnames = {}.\".format(testing_fnames))\n", "print('The number of different cars = {}'.format(testing_labels_count))\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dTtc3kKcd7Et", "outputId": "baeefbf3-f07f-414b-8fc0-3e3ac1de5719" }, "execution_count": 52, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Processing data cars_annotations/devkit/cars_test_annos_withlabels.mat.\n", "Saving data |==================================================| 100.000% \n", "There are 8041 images in this path.\n", "testing_class_ids = [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18\n", " 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36\n", " 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54\n", " 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72\n", " 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90\n", " 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108\n", " 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126\n", " 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144\n", " 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162\n", " 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180\n", " 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196].\n", "testing_labels = ['AM_General_Hummer_SUV_2000' 'Acura_Integra_Type_R_2001'\n", " 'Acura_RL_Sedan_2012' 'Acura_TL_Sedan_2012' 'Acura_TL_Type-S_2008'\n", " 'Acura_TSX_Sedan_2012' 'Acura_ZDX_Hatchback_2012'\n", " 'Aston_Martin_V8_Vantage_Convertible_2012'\n", " 'Aston_Martin_V8_Vantage_Coupe_2012'\n", " 'Aston_Martin_Virage_Convertible_2012' 'Aston_Martin_Virage_Coupe_2012'\n", " 'Audi_100_Sedan_1994' 'Audi_100_Wagon_1994' 'Audi_A5_Coupe_2012'\n", " 'Audi_R8_Coupe_2012' 'Audi_RS_4_Convertible_2008' 'Audi_S4_Sedan_2007'\n", " 'Audi_S4_Sedan_2012' 'Audi_S5_Convertible_2012' 'Audi_S5_Coupe_2012'\n", " 'Audi_S6_Sedan_2011' 'Audi_TTS_Coupe_2012' 'Audi_TT_Hatchback_2011'\n", " 'Audi_TT_RS_Coupe_2012' 'Audi_V8_Sedan_1994'\n", " 'BMW_1_Series_Convertible_2012' 'BMW_1_Series_Coupe_2012'\n", " 'BMW_3_Series_Sedan_2012' 'BMW_3_Series_Wagon_2012'\n", " 'BMW_6_Series_Convertible_2007' 'BMW_ActiveHybrid_5_Sedan_2012'\n", " 'BMW_M3_Coupe_2012' 'BMW_M5_Sedan_2010' 'BMW_M6_Convertible_2010'\n", " 'BMW_X3_SUV_2012' 'BMW_X5_SUV_2007' 'BMW_X6_SUV_2012'\n", " 'BMW_Z4_Convertible_2012' 'Bentley_Arnage_Sedan_2009'\n", " 'Bentley_Continental_Flying_Spur_Sedan_2007'\n", " 'Bentley_Continental_GT_Coupe_2007' 'Bentley_Continental_GT_Coupe_2012'\n", " 'Bentley_Continental_Supersports_Conv._Convertible_2012'\n", " 'Bentley_Mulsanne_Sedan_2011' 'Bugatti_Veyron_16.4_Convertible_2009'\n", " 'Bugatti_Veyron_16.4_Coupe_2009' 'Buick_Enclave_SUV_2012'\n", " 'Buick_Rainier_SUV_2007' 'Buick_Regal_GS_2012' 'Buick_Verano_Sedan_2012'\n", " 'Cadillac_CTS-V_Sedan_2012' 'Cadillac_Escalade_EXT_Crew_Cab_2007'\n", " 'Cadillac_SRX_SUV_2012' 'Chevrolet_Avalanche_Crew_Cab_2012'\n", " 'Chevrolet_Camaro_Convertible_2012' 'Chevrolet_Cobalt_SS_2010'\n", " 'Chevrolet_Corvette_Convertible_2012'\n", " 'Chevrolet_Corvette_Ron_Fellows_Edition_Z06_2007'\n", " 'Chevrolet_Corvette_ZR1_2012' 'Chevrolet_Express_Cargo_Van_2007'\n", " 'Chevrolet_Express_Van_2007' 'Chevrolet_HHR_SS_2010'\n", " 'Chevrolet_Impala_Sedan_2007' 'Chevrolet_Malibu_Hybrid_Sedan_2010'\n", " 'Chevrolet_Malibu_Sedan_2007' 'Chevrolet_Monte_Carlo_Coupe_2007'\n", " 'Chevrolet_Silverado_1500_Classic_Extended_Cab_2007'\n", " 'Chevrolet_Silverado_1500_Extended_Cab_2012'\n", " 'Chevrolet_Silverado_1500_Hybrid_Crew_Cab_2012'\n", " 'Chevrolet_Silverado_1500_Regular_Cab_2012'\n", " 'Chevrolet_Silverado_2500HD_Regular_Cab_2012'\n", " 'Chevrolet_Sonic_Sedan_2012' 'Chevrolet_Tahoe_Hybrid_SUV_2012'\n", " 'Chevrolet_TrailBlazer_SS_2009' 'Chevrolet_Traverse_SUV_2012'\n", " 'Chrysler_300_SRT-8_2010' 'Chrysler_Aspen_SUV_2009'\n", " 'Chrysler_Crossfire_Convertible_2008'\n", " 'Chrysler_PT_Cruiser_Convertible_2008'\n", " 'Chrysler_Sebring_Convertible_2010'\n", " 'Chrysler_Town_and_Country_Minivan_2012' 'Daewoo_Nubira_Wagon_2002'\n", " 'Dodge_Caliber_Wagon_2007' 'Dodge_Caliber_Wagon_2012'\n", " 'Dodge_Caravan_Minivan_1997' 'Dodge_Challenger_SRT8_2011'\n", " 'Dodge_Charger_SRT-8_2009' 'Dodge_Charger_Sedan_2012'\n", " 'Dodge_Dakota_Club_Cab_2007' 'Dodge_Dakota_Crew_Cab_2010'\n", " 'Dodge_Durango_SUV_2007' 'Dodge_Durango_SUV_2012'\n", " 'Dodge_Journey_SUV_2012' 'Dodge_Magnum_Wagon_2008'\n", " 'Dodge_Ram_Pickup_3500_Crew_Cab_2010'\n", " 'Dodge_Ram_Pickup_3500_Quad_Cab_2009' 'Dodge_Sprinter_Cargo_Van_2009'\n", " 'Eagle_Talon_Hatchback_1998' 'FIAT_500_Abarth_2012'\n", " 'FIAT_500_Convertible_2012' 'Ferrari_458_Italia_Convertible_2012'\n", " 'Ferrari_458_Italia_Coupe_2012' 'Ferrari_California_Convertible_2012'\n", " 'Ferrari_FF_Coupe_2012' 'Fisker_Karma_Sedan_2012'\n", " 'Ford_E-Series_Wagon_Van_2012' 'Ford_Edge_SUV_2012'\n", " 'Ford_Expedition_EL_SUV_2009' 'Ford_F-150_Regular_Cab_2007'\n", " 'Ford_F-150_Regular_Cab_2012' 'Ford_F-450_Super_Duty_Crew_Cab_2012'\n", " 'Ford_Fiesta_Sedan_2012' 'Ford_Focus_Sedan_2007'\n", " 'Ford_Freestar_Minivan_2007' 'Ford_GT_Coupe_2006'\n", " 'Ford_Mustang_Convertible_2007' 'Ford_Ranger_SuperCab_2011'\n", " 'GMC_Acadia_SUV_2012' 'GMC_Canyon_Extended_Cab_2012'\n", " 'GMC_Savana_Van_2012' 'GMC_Terrain_SUV_2012' 'GMC_Yukon_Hybrid_SUV_2012'\n", " 'Geo_Metro_Convertible_1993' 'HUMMER_H2_SUT_Crew_Cab_2009'\n", " 'HUMMER_H3T_Crew_Cab_2010' 'Honda_Accord_Coupe_2012'\n", " 'Honda_Accord_Sedan_2012' 'Honda_Odyssey_Minivan_2007'\n", " 'Honda_Odyssey_Minivan_2012' 'Hyundai_Accent_Sedan_2012'\n", " 'Hyundai_Azera_Sedan_2012' 'Hyundai_Elantra_Sedan_2007'\n", " 'Hyundai_Elantra_Touring_Hatchback_2012' 'Hyundai_Genesis_Sedan_2012'\n", " 'Hyundai_Santa_Fe_SUV_2012' 'Hyundai_Sonata_Hybrid_Sedan_2012'\n", " 'Hyundai_Sonata_Sedan_2012' 'Hyundai_Tucson_SUV_2012'\n", " 'Hyundai_Veloster_Hatchback_2012' 'Hyundai_Veracruz_SUV_2012'\n", " 'Infiniti_G_Coupe_IPL_2012' 'Infiniti_QX56_SUV_2011'\n", " 'Isuzu_Ascender_SUV_2008' 'Jaguar_XK_XKR_2012' 'Jeep_Compass_SUV_2012'\n", " 'Jeep_Grand_Cherokee_SUV_2012' 'Jeep_Liberty_SUV_2012'\n", " 'Jeep_Patriot_SUV_2012' 'Jeep_Wrangler_SUV_2012'\n", " 'Lamborghini_Aventador_Coupe_2012' 'Lamborghini_Diablo_Coupe_2001'\n", " 'Lamborghini_Gallardo_LP_570-4_Superleggera_2012'\n", " 'Lamborghini_Reventon_Coupe_2008' 'Land_Rover_LR2_SUV_2012'\n", " 'Land_Rover_Range_Rover_SUV_2012' 'Lincoln_Town_Car_Sedan_2011'\n", " 'MINI_Cooper_Roadster_Convertible_2012'\n", " 'Maybach_Landaulet_Convertible_2012' 'Mazda_Tribute_SUV_2011'\n", " 'McLaren_MP4-12C_Coupe_2012' 'Mercedes-Benz_300-Class_Convertible_1993'\n", " 'Mercedes-Benz_C-Class_Sedan_2012' 'Mercedes-Benz_E-Class_Sedan_2012'\n", " 'Mercedes-Benz_S-Class_Sedan_2012' 'Mercedes-Benz_SL-Class_Coupe_2009'\n", " 'Mercedes-Benz_Sprinter_Van_2012' 'Mitsubishi_Lancer_Sedan_2012'\n", " 'Nissan_240SX_Coupe_1998' 'Nissan_Juke_Hatchback_2012'\n", " 'Nissan_Leaf_Hatchback_2012' 'Nissan_NV_Passenger_Van_2012'\n", " 'Plymouth_Neon_Coupe_1999' 'Porsche_Panamera_Sedan_2012'\n", " 'Ram_C_V_Cargo_Van_Minivan_2012' 'Rolls-Royce_Ghost_Sedan_2012'\n", " 'Rolls-Royce_Phantom_Drophead_Coupe_Convertible_2012'\n", " 'Rolls-Royce_Phantom_Sedan_2012' 'Scion_xD_Hatchback_2012'\n", " 'Spyker_C8_Convertible_2009' 'Spyker_C8_Coupe_2009'\n", " 'Suzuki_Aerio_Sedan_2007' 'Suzuki_Kizashi_Sedan_2012'\n", " 'Suzuki_SX4_Hatchback_2012' 'Suzuki_SX4_Sedan_2012'\n", " 'Tesla_Model_S_Sedan_2012' 'Toyota_4Runner_SUV_2012'\n", " 'Toyota_Camry_Sedan_2012' 'Toyota_Corolla_Sedan_2012'\n", " 'Toyota_Sequoia_SUV_2012' 'Volkswagen_Beetle_Hatchback_2012'\n", " 'Volkswagen_Golf_Hatchback_1991' 'Volkswagen_Golf_Hatchback_2012'\n", " 'Volvo_240_Sedan_1993' 'Volvo_C30_Hatchback_2012' 'Volvo_XC90_SUV_2007'\n", " 'smart_fortwo_Convertible_2012'].\n", "testing_fnames = ['00001.jpg', '00002.jpg', '00003.jpg', '00004.jpg', '00005.jpg', '00006.jpg', '00007.jpg', '00008.jpg', '00009.jpg', '00010.jpg', '00011.jpg', '00012.jpg', '00013.jpg', '00014.jpg', '00015.jpg', '00016.jpg', '00017.jpg', '00018.jpg', '00019.jpg', '00020.jpg', '00021.jpg', '00022.jpg', '00023.jpg', 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'08032.jpg', '08033.jpg', '08034.jpg', '08035.jpg', '08036.jpg', '08037.jpg', '08038.jpg', '08039.jpg', '08040.jpg', '08041.jpg'].\n", "The number of different cars = 196\n" ] } ] }, { "cell_type": "markdown", "source": [ "" ], "metadata": { "id": "Esn_LV5Rd68U" } }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "4NmNrB8Fd61I" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "" ], "metadata": { "id": "xt3PXe9cd6r-" } }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "e-OFRclMd6j7" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "" ], "metadata": { "id": "9wxi2SuGd6cB" } }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "82IPiSWLd6UH" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "" ], "metadata": { "id": "MEcLiWbCd6LW" } }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "_Es3iFG9d6Db" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "" ], "metadata": { "id": "LBswGxSMd55z" } }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "1FyBoQh7d5t8" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "U4vCcep26Em6" }, "source": [ "ds = ImageFolder(cars_training_output_path)\n", "indices = torch.randperm(len(ds)).tolist()\n", "n_val = math.floor(len(indices) * .15)\n", "train_ds = torch.utils.data.Subset(ds, indices[:-n_val])\n", "val_ds = torch.utils.data.Subset(ds, indices[-n_val:])" ], "execution_count": 53, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ABreTsYRoDzy" }, "source": [ "## Show Some Examples\n", "\n", "#### ⚠️ Note - The image search API isn't perfect ⚠️\n", "\n", "You may need to go back, tweak your search terms, and repeat the cells above until the images shown below look good.\n", "\n", "A few bad images is OK, but if they are completely incorrect, you'll definitely want to try again with different terms." ] }, { "cell_type": "code", "metadata": { "id": "wbzsRQRe6iY5", "colab": { "base_uri": "https://localhost:8080/", "height": 422 }, "outputId": "402597c5-06dd-4f69-c719-98ded3c5a5fd" }, "source": [ "plt.figure(figsize=(20,10))\n", "num_examples_per_class = 5\n", "i = 1\n", "for class_idx, class_name in enumerate(ds.classes):\n", " folder = ds.root / class_name\n", " for image_idx, image_path in enumerate(sorted(folder.glob('*'))):\n", " if image_path.suffix in ds.extensions:\n", " image = Image.open(image_path)\n", " plt.subplot(len(ds.classes), num_examples_per_class, i)\n", " ax = plt.gca()\n", " ax.set_title(\n", " class_name,\n", " size='xx-large',\n", " pad=5,\n", " loc='left',\n", " y=0,\n", " backgroundcolor='white'\n", " )\n", " ax.axis('off')\n", " plt.imshow(image)\n", " i += 1\n", "\n", " if image_idx + 1 == num_examples_per_class:\n", " break" ], "execution_count": 54, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "96LqcgYfLGc8" }, "source": [ "## Preparing Labels for Our Model's Config\n", "\n", "By adding `label2id` + `id2label` to our model's config, we'll get friendlier labels in the inference API." ] }, { "cell_type": "code", "metadata": { "id": "mh0yxRKDGDd5", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "1d1e671a-14c8-4b07-d179-4e9122925e76" }, "source": [ "label2id = {}\n", "id2label = {}\n", "\n", "for i, class_name in enumerate(ds.classes):\n", " label2id[class_name] = str(i)\n", " id2label[str(i)] = class_name\n", "print(f'len(label2id)={len(label2id)}, len(id2label) = {len(id2label)}') " ], "execution_count": 55, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "len(label2id)=196, len(id2label) = 196\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "-1aly6Vrx2mM" }, "source": [ "## Image Classification Collator\n", "\n", "To apply our transforms to images, we'll use a custom collator class. We'll initialize it using an instance of `ViTFeatureExtractor` and pass the collator instance to `torch.utils.data.DataLoader`'s `collate_fn` kwarg." ] }, { "cell_type": "code", "metadata": { "id": "D2n0Mf1u1ymf" }, "source": [ "class ImageClassificationCollator:\n", " def __init__(self, feature_extractor):\n", " self.feature_extractor = feature_extractor\n", " \n", " def __call__(self, batch):\n", " encodings = self.feature_extractor([x[0] for x in batch], return_tensors='pt')\n", " encodings['labels'] = torch.tensor([x[1] for x in batch], dtype=torch.long)\n", " return encodings " ], "execution_count": 56, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "T62uFtK7LTcz" }, "source": [ "## Init Feature Extractor, Model, Data Loaders\n" ] }, { "cell_type": "code", "metadata": { "id": "YovQELKD4Bu8", "colab": { "base_uri": "https://localhost:8080/", "height": 220, "referenced_widgets": [ "2ca95e7a02084ebb9a531f4ea0755b03", "460c6b0c3f864da7bd5ee7a175c6357e", "9e0ea8260e1a47b8acb4d0de44fd571e", "20e028e8774240ac88a956cc614a3e70", "eea8421efe904faf837c039bad828f5c", "a5c5564824d64bea834f3a20dfaff6fc", "aba86d03012446bd8f893cc98f72ce21", "9c8caaffa84c4141a1c9c53a4559a0fd", "209a8b3d556347bb96df3c65b267837f", "dc48577be98c4cb3b804fa8e3e5f71e3", "7cdcc5d8f5ad4ab3adffda9e5e627c70", "58aa15131468448daee43e56bb4c5cdf", "2a487935f2cd47cdae7efa98e82e6259", "54b5beeac37441818d38bbad69ad6d48", "cbb17a1811924195b4bcb1744c39d9e4", "ec5df2577e84425b97efad348a9c73eb", "9c10e1e3afd44265ba9be8a86d273dc6", "7b59cc249c1c4de4b4083b1ad46a0f3f", "5f9bda7a21d44c38abcdb4eac8d65d77", "8be7415b39fc41959712e6a993bc1d91", "fdf69e01402b45bdb47abde8e3b24e85", "031d3867dbba4131a9da92fb53b6d925", "ef474bd3e1da4669b15a6dbc63923a11", "e3160f8b528a480e94c3012cb9615a02", "6be0d04fc1c4440aaf3510c5f41ecd1e", "511439017e2043a6b87eb82c33561319", "122a2c1797f64bf0bb2933336889508c", "6ca21335fa2d42db9ba53214f7d6472b", "07fcd345fe5d4ea292e3f6983abb3892", "9a67ab15aac345ea8f0e7b0abcf3d510", "957f38ea02614ea9a26d13d56dea22b4", "7f1e1bba109c4d5ea4aac2a2a48d7bb3", "bc9af6ded9a54cb08aa3b24840b60820" ] }, "outputId": "bd1220a8-6f93-4b4a-9436-2d52b8853ec3" }, "source": [ "feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')\n", "model = ViTForImageClassification.from_pretrained(\n", " 'google/vit-base-patch16-224-in21k',\n", " num_labels=len(label2id),\n", " label2id=label2id,\n", " id2label=id2label\n", ")\n", "collator = ImageClassificationCollator(feature_extractor)\n", "train_loader = DataLoader(train_ds, batch_size=8, collate_fn=collator, num_workers=2, shuffle=True)\n", "val_loader = DataLoader(val_ds, batch_size=8, collate_fn=collator, num_workers=2)" ], "execution_count": 57, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2ca95e7a02084ebb9a531f4ea0755b03", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/160 [00:00" ] }, "metadata": { "needs_background": "light" } }, { "output_type": "display_data", "data": { "text/plain": [ "
" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 45\n", " 1 1.00 1.00 1.00 45\n", " 2 0.93 0.81 0.87 32\n", " 3 0.93 0.98 0.95 43\n", " 4 0.95 0.98 0.96 42\n", " 5 0.93 0.90 0.91 41\n", " 6 0.97 0.97 0.97 39\n", " 7 0.90 1.00 0.95 45\n", " 8 0.95 1.00 0.98 41\n", " 9 1.00 0.88 0.94 33\n", " 10 1.00 0.97 0.99 38\n", " 11 0.97 0.93 0.95 41\n", " 12 1.00 0.98 0.99 43\n", " 13 0.93 0.90 0.91 41\n", " 14 1.00 0.98 0.99 43\n", " 15 0.97 1.00 0.99 37\n", " 16 0.95 0.87 0.91 45\n", " 17 0.97 0.97 0.97 40\n", " 18 1.00 0.95 0.98 42\n", " 19 0.91 1.00 0.96 43\n", " 20 0.87 0.98 0.92 46\n", " 21 0.95 0.91 0.93 43\n", " 22 0.87 0.95 0.91 41\n", " 23 0.97 0.95 0.96 40\n", " 24 0.94 1.00 0.97 44\n", " 25 0.97 1.00 0.99 36\n", " 26 0.98 1.00 0.99 41\n", " 27 0.98 1.00 0.99 43\n", " 28 1.00 0.98 0.99 42\n", " 29 0.90 0.98 0.93 44\n", " 30 1.00 0.97 0.99 34\n", " 31 0.94 1.00 0.97 45\n", " 32 0.97 0.93 0.95 41\n", " 33 1.00 0.93 0.96 41\n", " 34 0.97 1.00 0.99 39\n", " 35 0.95 0.95 0.95 42\n", " 36 1.00 0.98 0.99 42\n", " 37 1.00 1.00 1.00 41\n", " 38 1.00 1.00 1.00 39\n", " 39 0.98 0.98 0.98 45\n", " 40 1.00 0.93 0.97 46\n", " 41 0.94 0.97 0.96 35\n", " 42 0.97 0.97 0.97 37\n", " 43 1.00 1.00 1.00 36\n", " 44 0.86 0.97 0.91 33\n", " 45 1.00 0.86 0.93 44\n", " 46 1.00 1.00 1.00 42\n", " 47 1.00 1.00 1.00 43\n", " 48 1.00 1.00 1.00 35\n", " 49 1.00 0.92 0.96 38\n", " 50 1.00 1.00 1.00 43\n", " 51 0.96 1.00 0.98 45\n", " 52 0.98 1.00 0.99 41\n", " 53 0.98 0.98 0.98 45\n", " 54 1.00 1.00 1.00 45\n", " 55 1.00 0.98 0.99 42\n", " 56 0.97 0.95 0.96 40\n", " 57 1.00 0.97 0.99 38\n", " 58 0.98 1.00 0.99 47\n", " 59 0.90 0.90 0.90 30\n", " 60 0.94 0.86 0.90 35\n", " 61 1.00 0.97 0.99 37\n", " 62 1.00 0.98 0.99 43\n", " 63 0.91 1.00 0.95 39\n", " 64 0.98 0.98 0.98 45\n", " 65 1.00 0.96 0.98 45\n", " 66 0.98 1.00 0.99 43\n", " 67 0.93 0.91 0.92 44\n", " 68 0.95 0.95 0.95 40\n", " 69 0.88 0.95 0.91 44\n", " 70 0.94 0.79 0.86 38\n", " 71 0.96 0.98 0.97 44\n", " 72 0.95 1.00 0.97 37\n", " 73 1.00 1.00 1.00 40\n", " 74 0.93 0.98 0.96 44\n", " 75 0.92 0.98 0.95 49\n", " 76 1.00 1.00 1.00 44\n", " 77 1.00 0.98 0.99 43\n", " 78 0.94 0.98 0.96 45\n", " 79 1.00 0.88 0.94 41\n", " 80 1.00 0.97 0.99 38\n", " 81 0.98 1.00 0.99 45\n", " 82 0.81 1.00 0.89 42\n", " 83 1.00 0.73 0.85 41\n", " 84 1.00 1.00 1.00 44\n", " 85 1.00 1.00 1.00 39\n", " 86 0.98 0.95 0.96 42\n", " 87 0.98 0.98 0.98 41\n", " 88 1.00 1.00 1.00 39\n", " 89 1.00 0.95 0.97 41\n", " 90 0.96 1.00 0.98 46\n", " 91 0.98 0.98 0.98 44\n", " 92 0.98 0.98 0.98 44\n", " 93 0.95 0.97 0.96 40\n", " 94 0.98 0.98 0.98 43\n", " 95 0.98 0.93 0.95 44\n", " 96 1.00 1.00 1.00 40\n", " 97 0.98 0.98 0.98 46\n", " 98 1.00 1.00 1.00 28\n", " 99 1.00 1.00 1.00 34\n", " 100 1.00 0.97 0.99 40\n", " 101 0.96 1.00 0.98 43\n", " 102 0.97 1.00 0.99 39\n", " 103 1.00 0.98 0.99 42\n", " 104 1.00 0.95 0.98 44\n", " 105 1.00 0.95 0.97 38\n", " 106 0.96 1.00 0.98 43\n", " 107 0.98 0.98 0.98 45\n", " 108 0.96 0.98 0.97 45\n", " 109 0.95 0.98 0.97 43\n", " 110 1.00 0.98 0.99 42\n", " 111 1.00 0.93 0.96 43\n", " 112 0.98 1.00 0.99 45\n", " 113 1.00 1.00 1.00 44\n", " 114 0.98 1.00 0.99 46\n", " 115 0.96 1.00 0.98 45\n", " 116 0.93 0.98 0.95 42\n", " 117 1.00 0.93 0.97 45\n", " 118 0.98 1.00 0.99 40\n", " 119 0.92 0.99 0.95 68\n", " 120 0.98 1.00 0.99 42\n", " 121 1.00 0.91 0.95 43\n", " 122 0.98 1.00 0.99 45\n", " 123 0.98 1.00 0.99 44\n", " 124 1.00 0.97 0.99 39\n", " 125 0.95 1.00 0.97 39\n", " 126 0.97 0.97 0.97 39\n", " 127 1.00 1.00 1.00 41\n", " 128 1.00 0.98 0.99 42\n", " 129 1.00 0.96 0.98 24\n", " 130 0.98 0.98 0.98 42\n", " 131 0.98 1.00 0.99 42\n", " 132 0.91 0.98 0.94 43\n", " 133 1.00 1.00 1.00 44\n", " 134 1.00 0.95 0.98 42\n", " 135 1.00 1.00 1.00 34\n", " 136 0.95 0.97 0.96 40\n", " 137 0.98 1.00 0.99 44\n", " 138 0.98 0.98 0.98 41\n", " 139 0.95 1.00 0.98 42\n", " 140 0.97 1.00 0.99 34\n", " 141 0.97 1.00 0.99 33\n", " 142 0.97 0.97 0.97 40\n", " 143 0.98 0.98 0.98 47\n", " 144 1.00 1.00 1.00 43\n", " 145 0.98 0.96 0.97 45\n", " 146 0.98 0.96 0.97 45\n", " 147 0.98 1.00 0.99 44\n", " 148 1.00 1.00 1.00 43\n", " 149 0.98 1.00 0.99 44\n", " 150 1.00 0.98 0.99 45\n", " 151 1.00 0.94 0.97 36\n", " 152 1.00 1.00 1.00 36\n", " 153 1.00 0.95 0.98 43\n", " 154 1.00 1.00 1.00 43\n", " 155 0.95 1.00 0.97 39\n", " 156 1.00 0.95 0.97 37\n", " 157 1.00 0.97 0.98 29\n", " 158 0.97 1.00 0.99 36\n", " 159 0.96 1.00 0.98 44\n", " 160 0.98 1.00 0.99 48\n", " 161 0.98 0.98 0.98 46\n", " 162 1.00 0.91 0.95 44\n", " 163 0.96 0.98 0.97 45\n", " 164 1.00 1.00 1.00 37\n", " 165 1.00 1.00 1.00 41\n", " 166 0.98 1.00 0.99 48\n", " 167 1.00 1.00 1.00 46\n", " 168 0.98 0.98 0.98 44\n", " 169 1.00 1.00 1.00 42\n", " 170 1.00 1.00 1.00 39\n", " 171 1.00 1.00 1.00 44\n", " 172 0.98 1.00 0.99 44\n", " 173 0.95 1.00 0.98 41\n", " 174 0.95 0.92 0.94 39\n", " 175 1.00 0.97 0.98 31\n", " 176 0.98 0.95 0.97 44\n", " 177 1.00 0.95 0.98 42\n", " 178 0.98 0.96 0.97 45\n", " 179 0.95 0.98 0.97 43\n", " 180 1.00 0.95 0.97 38\n", " 181 0.98 0.98 0.98 46\n", " 182 1.00 0.88 0.94 42\n", " 183 0.91 0.98 0.94 41\n", " 184 1.00 1.00 1.00 39\n", " 185 1.00 0.95 0.97 41\n", " 186 0.86 0.98 0.91 44\n", " 187 0.95 0.91 0.93 44\n", " 188 1.00 1.00 1.00 39\n", " 189 0.98 1.00 0.99 43\n", " 190 1.00 1.00 1.00 46\n", " 191 1.00 0.93 0.96 43\n", " 192 1.00 1.00 1.00 46\n", " 193 1.00 1.00 1.00 42\n", " 194 0.98 1.00 0.99 43\n", " 195 1.00 1.00 1.00 40\n", "\n", " accuracy 0.97 8144\n", " macro avg 0.97 0.97 0.97 8144\n", "weighted avg 0.97 0.97 0.97 8144\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 76 }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "Print the model" ], "metadata": { "id": "T3fM-SToyfvo" } }, { "cell_type": "code", "source": [ "print(model)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QTSPLpwAyfl9", "outputId": "90374f0b-1e69-4d09-cb8a-3e8c40a95bfb" }, "execution_count": 77, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "ViTForImageClassification(\n", " (vit): ViTModel(\n", " (embeddings): ViTEmbeddings(\n", " (patch_embeddings): PatchEmbeddings(\n", " (projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n", " )\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (encoder): ViTEncoder(\n", " (layer): ModuleList(\n", " (0): ViTLayer(\n", " (attention): ViTAttention(\n", " (attention): ViTSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (output): ViTSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (intermediate): ViTIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): ViTOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " (1): ViTLayer(\n", " (attention): ViTAttention(\n", " (attention): ViTSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (output): ViTSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (intermediate): ViTIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): ViTOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " (2): ViTLayer(\n", " (attention): ViTAttention(\n", " (attention): ViTSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (output): ViTSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (intermediate): ViTIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): ViTOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " (3): ViTLayer(\n", " (attention): ViTAttention(\n", " (attention): ViTSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (output): ViTSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (intermediate): ViTIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): ViTOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " (4): ViTLayer(\n", " (attention): ViTAttention(\n", " (attention): ViTSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (output): ViTSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (intermediate): ViTIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): ViTOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " (5): ViTLayer(\n", " (attention): ViTAttention(\n", " (attention): ViTSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (output): ViTSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (intermediate): ViTIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): ViTOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " (6): ViTLayer(\n", " (attention): ViTAttention(\n", " (attention): ViTSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (output): ViTSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (intermediate): ViTIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): ViTOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " (7): ViTLayer(\n", " (attention): ViTAttention(\n", " (attention): ViTSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (output): ViTSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (intermediate): ViTIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): ViTOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " (8): ViTLayer(\n", " (attention): ViTAttention(\n", " (attention): ViTSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (output): ViTSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (intermediate): ViTIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): ViTOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " (9): ViTLayer(\n", " (attention): ViTAttention(\n", " (attention): ViTSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (output): ViTSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (intermediate): ViTIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): ViTOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " (10): ViTLayer(\n", " (attention): ViTAttention(\n", " (attention): ViTSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (output): ViTSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (intermediate): ViTIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): ViTOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " (11): ViTLayer(\n", " (attention): ViTAttention(\n", " (attention): ViTSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (output): ViTSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (intermediate): ViTIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): ViTOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " )\n", " )\n", " (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " (classifier): Linear(in_features=768, out_features=196, bias=True)\n", ")\n" ] } ] }, { "cell_type": "markdown", "source": [ "Copy/Paste a test data from the internet and check how well the model hypothesis output matches with the groundtruth data." ], "metadata": { "id": "nRuonYU3ynnx" } }, { "cell_type": "code", "source": [ "import PIL.ImageOps\n", "import requests\n", "from PIL import Image\n", " \n", "url = 'https://www.hondamarysville.com/assets/stock/colormatched_01/white/640/cc_2018hoc020013_01_640/cc_2018hoc020013_01_640_si.jpg?height=400'\n", "response = requests.get(url, stream = True)\n", "img = Image.open(response.raw)\n", "plt.imshow(img)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 286 }, "id": "Hw3fNkdzynfX", "outputId": "d7ef074d-ed89-4618-979a-4f6df601b24e" }, "execution_count": 78, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 78 }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "Try transforming the image." ], "metadata": { "id": "3BnTSXiL0s2n" } }, { "cell_type": "code", "source": [ "# Create the validation data transform object for augmenting the image and resizing the image to fit into an existing pre-trained model.\n", "transform = transforms.Compose([transforms.Resize((224,224)),\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n", " ])\n", "# Define a function to transpose the image.\n", "def im_convert(tensor):\n", " image = tensor.cpu().clone().detach().numpy()\n", " image = image.transpose(1, 2, 0)\n", " image = image * np.array((0.5, 0.5, 0.5)) + np.array((0.5, 0.5, 0.5))\n", " image = image.clip(0, 1)\n", " return image\n", " \n", "img = transform(img) \n", "plt.imshow(im_convert(img))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 286 }, "id": "lLHftTqC0ssg", "outputId": "60577ac4-3875-4e81-ad11-92197e623305" }, "execution_count": 79, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 79 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "Print the class name for the specified input test data image." ], "metadata": { "id": "K91xnzSP0xIn" } }, { "cell_type": "markdown", "source": [ "Define the classes used in feature classification." ], "metadata": { "id": "FtieIQdQHgTS" } }, { "cell_type": "code", "source": [ "classes = [x for x in np.unique(training_labels)]\n", "print(classes)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lcM7p3bxHgL4", "outputId": "67bcecde-04e0-498d-a54d-bb5166475234" }, "execution_count": 83, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "['AM_General_Hummer_SUV_2000', 'Acura_Integra_Type_R_2001', 'Acura_RL_Sedan_2012', 'Acura_TL_Sedan_2012', 'Acura_TL_Type-S_2008', 'Acura_TSX_Sedan_2012', 'Acura_ZDX_Hatchback_2012', 'Aston_Martin_V8_Vantage_Convertible_2012', 'Aston_Martin_V8_Vantage_Coupe_2012', 'Aston_Martin_Virage_Convertible_2012', 'Aston_Martin_Virage_Coupe_2012', 'Audi_100_Sedan_1994', 'Audi_100_Wagon_1994', 'Audi_A5_Coupe_2012', 'Audi_R8_Coupe_2012', 'Audi_RS_4_Convertible_2008', 'Audi_S4_Sedan_2007', 'Audi_S4_Sedan_2012', 'Audi_S5_Convertible_2012', 'Audi_S5_Coupe_2012', 'Audi_S6_Sedan_2011', 'Audi_TTS_Coupe_2012', 'Audi_TT_Hatchback_2011', 'Audi_TT_RS_Coupe_2012', 'Audi_V8_Sedan_1994', 'BMW_1_Series_Convertible_2012', 'BMW_1_Series_Coupe_2012', 'BMW_3_Series_Sedan_2012', 'BMW_3_Series_Wagon_2012', 'BMW_6_Series_Convertible_2007', 'BMW_ActiveHybrid_5_Sedan_2012', 'BMW_M3_Coupe_2012', 'BMW_M5_Sedan_2010', 'BMW_M6_Convertible_2010', 'BMW_X3_SUV_2012', 'BMW_X5_SUV_2007', 'BMW_X6_SUV_2012', 'BMW_Z4_Convertible_2012', 'Bentley_Arnage_Sedan_2009', 'Bentley_Continental_Flying_Spur_Sedan_2007', 'Bentley_Continental_GT_Coupe_2007', 'Bentley_Continental_GT_Coupe_2012', 'Bentley_Continental_Supersports_Conv._Convertible_2012', 'Bentley_Mulsanne_Sedan_2011', 'Bugatti_Veyron_16.4_Convertible_2009', 'Bugatti_Veyron_16.4_Coupe_2009', 'Buick_Enclave_SUV_2012', 'Buick_Rainier_SUV_2007', 'Buick_Regal_GS_2012', 'Buick_Verano_Sedan_2012', 'Cadillac_CTS-V_Sedan_2012', 'Cadillac_Escalade_EXT_Crew_Cab_2007', 'Cadillac_SRX_SUV_2012', 'Chevrolet_Avalanche_Crew_Cab_2012', 'Chevrolet_Camaro_Convertible_2012', 'Chevrolet_Cobalt_SS_2010', 'Chevrolet_Corvette_Convertible_2012', 'Chevrolet_Corvette_Ron_Fellows_Edition_Z06_2007', 'Chevrolet_Corvette_ZR1_2012', 'Chevrolet_Express_Cargo_Van_2007', 'Chevrolet_Express_Van_2007', 'Chevrolet_HHR_SS_2010', 'Chevrolet_Impala_Sedan_2007', 'Chevrolet_Malibu_Hybrid_Sedan_2010', 'Chevrolet_Malibu_Sedan_2007', 'Chevrolet_Monte_Carlo_Coupe_2007', 'Chevrolet_Silverado_1500_Classic_Extended_Cab_2007', 'Chevrolet_Silverado_1500_Extended_Cab_2012', 'Chevrolet_Silverado_1500_Hybrid_Crew_Cab_2012', 'Chevrolet_Silverado_1500_Regular_Cab_2012', 'Chevrolet_Silverado_2500HD_Regular_Cab_2012', 'Chevrolet_Sonic_Sedan_2012', 'Chevrolet_Tahoe_Hybrid_SUV_2012', 'Chevrolet_TrailBlazer_SS_2009', 'Chevrolet_Traverse_SUV_2012', 'Chrysler_300_SRT-8_2010', 'Chrysler_Aspen_SUV_2009', 'Chrysler_Crossfire_Convertible_2008', 'Chrysler_PT_Cruiser_Convertible_2008', 'Chrysler_Sebring_Convertible_2010', 'Chrysler_Town_and_Country_Minivan_2012', 'Daewoo_Nubira_Wagon_2002', 'Dodge_Caliber_Wagon_2007', 'Dodge_Caliber_Wagon_2012', 'Dodge_Caravan_Minivan_1997', 'Dodge_Challenger_SRT8_2011', 'Dodge_Charger_SRT-8_2009', 'Dodge_Charger_Sedan_2012', 'Dodge_Dakota_Club_Cab_2007', 'Dodge_Dakota_Crew_Cab_2010', 'Dodge_Durango_SUV_2007', 'Dodge_Durango_SUV_2012', 'Dodge_Journey_SUV_2012', 'Dodge_Magnum_Wagon_2008', 'Dodge_Ram_Pickup_3500_Crew_Cab_2010', 'Dodge_Ram_Pickup_3500_Quad_Cab_2009', 'Dodge_Sprinter_Cargo_Van_2009', 'Eagle_Talon_Hatchback_1998', 'FIAT_500_Abarth_2012', 'FIAT_500_Convertible_2012', 'Ferrari_458_Italia_Convertible_2012', 'Ferrari_458_Italia_Coupe_2012', 'Ferrari_California_Convertible_2012', 'Ferrari_FF_Coupe_2012', 'Fisker_Karma_Sedan_2012', 'Ford_E-Series_Wagon_Van_2012', 'Ford_Edge_SUV_2012', 'Ford_Expedition_EL_SUV_2009', 'Ford_F-150_Regular_Cab_2007', 'Ford_F-150_Regular_Cab_2012', 'Ford_F-450_Super_Duty_Crew_Cab_2012', 'Ford_Fiesta_Sedan_2012', 'Ford_Focus_Sedan_2007', 'Ford_Freestar_Minivan_2007', 'Ford_GT_Coupe_2006', 'Ford_Mustang_Convertible_2007', 'Ford_Ranger_SuperCab_2011', 'GMC_Acadia_SUV_2012', 'GMC_Canyon_Extended_Cab_2012', 'GMC_Savana_Van_2012', 'GMC_Terrain_SUV_2012', 'GMC_Yukon_Hybrid_SUV_2012', 'Geo_Metro_Convertible_1993', 'HUMMER_H2_SUT_Crew_Cab_2009', 'HUMMER_H3T_Crew_Cab_2010', 'Honda_Accord_Coupe_2012', 'Honda_Accord_Sedan_2012', 'Honda_Odyssey_Minivan_2007', 'Honda_Odyssey_Minivan_2012', 'Hyundai_Accent_Sedan_2012', 'Hyundai_Azera_Sedan_2012', 'Hyundai_Elantra_Sedan_2007', 'Hyundai_Elantra_Touring_Hatchback_2012', 'Hyundai_Genesis_Sedan_2012', 'Hyundai_Santa_Fe_SUV_2012', 'Hyundai_Sonata_Hybrid_Sedan_2012', 'Hyundai_Sonata_Sedan_2012', 'Hyundai_Tucson_SUV_2012', 'Hyundai_Veloster_Hatchback_2012', 'Hyundai_Veracruz_SUV_2012', 'Infiniti_G_Coupe_IPL_2012', 'Infiniti_QX56_SUV_2011', 'Isuzu_Ascender_SUV_2008', 'Jaguar_XK_XKR_2012', 'Jeep_Compass_SUV_2012', 'Jeep_Grand_Cherokee_SUV_2012', 'Jeep_Liberty_SUV_2012', 'Jeep_Patriot_SUV_2012', 'Jeep_Wrangler_SUV_2012', 'Lamborghini_Aventador_Coupe_2012', 'Lamborghini_Diablo_Coupe_2001', 'Lamborghini_Gallardo_LP_570-4_Superleggera_2012', 'Lamborghini_Reventon_Coupe_2008', 'Land_Rover_LR2_SUV_2012', 'Land_Rover_Range_Rover_SUV_2012', 'Lincoln_Town_Car_Sedan_2011', 'MINI_Cooper_Roadster_Convertible_2012', 'Maybach_Landaulet_Convertible_2012', 'Mazda_Tribute_SUV_2011', 'McLaren_MP4-12C_Coupe_2012', 'Mercedes-Benz_300-Class_Convertible_1993', 'Mercedes-Benz_C-Class_Sedan_2012', 'Mercedes-Benz_E-Class_Sedan_2012', 'Mercedes-Benz_S-Class_Sedan_2012', 'Mercedes-Benz_SL-Class_Coupe_2009', 'Mercedes-Benz_Sprinter_Van_2012', 'Mitsubishi_Lancer_Sedan_2012', 'Nissan_240SX_Coupe_1998', 'Nissan_Juke_Hatchback_2012', 'Nissan_Leaf_Hatchback_2012', 'Nissan_NV_Passenger_Van_2012', 'Plymouth_Neon_Coupe_1999', 'Porsche_Panamera_Sedan_2012', 'Ram_C_V_Cargo_Van_Minivan_2012', 'Rolls-Royce_Ghost_Sedan_2012', 'Rolls-Royce_Phantom_Drophead_Coupe_Convertible_2012', 'Rolls-Royce_Phantom_Sedan_2012', 'Scion_xD_Hatchback_2012', 'Spyker_C8_Convertible_2009', 'Spyker_C8_Coupe_2009', 'Suzuki_Aerio_Sedan_2007', 'Suzuki_Kizashi_Sedan_2012', 'Suzuki_SX4_Hatchback_2012', 'Suzuki_SX4_Sedan_2012', 'Tesla_Model_S_Sedan_2012', 'Toyota_4Runner_SUV_2012', 'Toyota_Camry_Sedan_2012', 'Toyota_Corolla_Sedan_2012', 'Toyota_Sequoia_SUV_2012', 'Volkswagen_Beetle_Hatchback_2012', 'Volkswagen_Golf_Hatchback_1991', 'Volkswagen_Golf_Hatchback_2012', 'Volvo_240_Sedan_1993', 'Volvo_C30_Hatchback_2012', 'Volvo_XC90_SUV_2007', 'smart_fortwo_Convertible_2012']\n" ] } ] }, { "cell_type": "code", "source": [ "#image = img.to(device).unsqueeze(0)\n", "image = img.unsqueeze(0)\n", "output = model(image)\n", "preds = output.logits.softmax(1).argmax(1)\n", "for pred in preds:\n", " print(f'pred={pred}, original_label ={classes[pred]}')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aTIqslsM0w-9", "outputId": "95a7d792-f9a9-4e97-b7a1-17654d9c58d7" }, "execution_count": 85, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "pred=125, original_label =Honda_Accord_Coupe_2012\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "4FlEoisI9S-e" }, "source": [ "# Push to 🤗 Hub 🚀\n", "\n", "You'll need to authenticate with your Hugging Face account, so make sure to [sign up](https://huggingface.co/join) if you haven't already.\n", "\n", "Your repo will be created at `{hf_username}/{model_id}`, so pick a `model_id` you like 😊" ] }, { "cell_type": "code", "source": [ "notebook_login()" ], "metadata": { "id": "d1t_A7PkNRiY", "colab": { "base_uri": "https://localhost:8080/", "height": 415, "referenced_widgets": [ "64d413e3a8f04aeb9aec82f0c5543583", "212050123d3f4ddeb77233b3bcdf2ca2", "f5bb0251d12b4fd384ab197576fcdfe7", "176c82b21eb2470a82c22a722aaec0dd", "659ff46affc5474d91fa6fb1f3ea4f55", "0b31b7751a3d484a956e0e3d8185e9d1", "1aae2033d6e7445fa1bbd347ef9ec68c", "632167f6e6eb4691a8300949cf5463a5", "3cdd5afc113a4a3fa5f418c4499209ec", "36300054a3aa4c448a9ffd514367ccb5", "cc68c2605e15460a80c88361e7dfc4a0", "0b74e57d9c35433298d448a685943c94", "efb5df0c779144d0aecfec65ce589bc7", "9de72496549f49709f70c03a5555da9c", "384127e77aa146b8a37c723546b67aec", "b5926fe7886a42b29206ee8d6c26b74e", "ffb27bf26d124c8788e806ab071b1fd4" ] }, "outputId": "014ad979-0d54-47e9-b821-bf34c5d4373b" }, "execution_count": 72, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "t58j9AN-iELM", "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 185, "referenced_widgets": [ "347a83d4a21f46c38eabe192967754f7", "45df660745844b7db94108c0709f45ba", "a9aee8bdfd5544ed993183219d126138", "af4e9ca0da674498abb2acfd4ec7f4e5", "095ce91a05f44a11ba39128766f7bd9b", "0ed40b4eb50447e2bf974cffe90402b5", "ffdea2c1280b4bd69d8e7c9702c1f192", "ac672d134c6945eca70560b0046806d6", "68d9d88a959f4806a586f31e84564276", "cbbeed17b8f44452b069fe8056d2e307", "7578a104b07845c6b4ab8e145d22e99f", "84ec54139ba14192b2633a0fed07ef57", "2259342f4ecd41eebc070b6267f10f97", "0f44de183a7242fba18e49e15275d741", "2755f094cdf549c6b70a28528d8a99ad", "fbf65c11228a4f108ed2736bf8cb3e03", "54a2c16cad3e43f68332d3f0e2dd38c8", "db251934808e4a74b1eef3e8e012a75c", "bbfcc05a10494b0d9e08907ad2f195cd", "ffb51db24cdd4b67b20631b01cb25332", "351cc6964fce4e9da119adad7b94e9bb", "c47dd37267f14445bfd7bbefd4457303" ] }, "outputId": "94645886-21d5-410f-e57c-22e1c842474c" }, "source": [ "model_id = \"sriram-car-classifier\" #@param {type:\"string\"}\n", "\n", "description = \"\"\"\n", "Autogenerated by HuggingPics🤗🖼️\n", "\n", "Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).\n", "\n", "Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).\n", "\"\"\"\n", "task_name = \"Image Classification\"\n", "task_type = 'image-classification'\n", "metric_name = 'Accuracy'\n", "metric_type = 'accuracy'\n", "metric_value = trainer.callback_metrics['val_acc'].item()\n", "\n", "# Delete model folder, as we (re)create it here.\n", "if Path('./model').exists():\n", " shutil.rmtree('./model')\n", "\n", "token = HfFolder().get_token()\n", "if not token:\n", " raise RuntimeError(\"You must log in to push to hub! Run notebook_login() in the cell above.\")\n", "\n", "hf_username = HfApi().whoami()['name']\n", "model_url = HfApi().create_repo(token=token, name=model_id, exist_ok=True)\n", "model_repo = Repository(\"./model\", clone_from=model_url, use_auth_token=token, git_email=f\"{hf_username}@users.noreply.huggingface.co\", git_user=hf_username)\n", "model.save_pretrained(model_repo.local_dir)\n", "feature_extractor.save_pretrained(model_repo.local_dir)\n", "\n", "# Copy over tensorboard logs from lightning_logs/ into ./model/runs/\n", "tensorboard_logs_path = next(Path(trainer.logger.log_dir).glob('events.out*'))\n", "model_repo_logs_path = Path(model_repo.local_dir) / 'runs'\n", "model_repo_logs_path.mkdir(exist_ok=True, parents=True)\n", "shutil.copy(tensorboard_logs_path, model_repo_logs_path)\n", "\n", "# Copy over a few example images\n", "example_images_dir = Path(model_repo.local_dir) / 'images'\n", "example_images_dir.mkdir(exist_ok=True, parents=True)\n", "image_markdown_template = '''\n", "#### {class_name}\n", "\n", "![{class_name}](images/{example_image_path})\n", "'''\n", "example_images_markdown = \"\"\n", "for class_idx, class_name in enumerate(ds.classes):\n", " folder = ds.root / class_name\n", " image_path = sorted(folder.glob('*'))[0]\n", " example_image_path = example_images_dir / f\"{class_name.replace(' ', '_')}{image_path.suffix}\"\n", " shutil.copy(image_path, example_image_path)\n", " example_images_markdown += image_markdown_template.format(\n", " class_name=class_name,\n", " example_image_path=example_image_path.name\n", " )\n", "\n", "\n", "# Prepare README.md from information gathered above\n", "readme_txt = f\"\"\"\n", "---\n", "tags:\n", "- image-classification\n", "- pytorch\n", "- huggingpics\n", "metrics:\n", "- {metric_type}\n", "\n", "model-index:\n", "- name: {model_id}\n", " results:\n", " - task:\n", " name: {task_name}\n", " type: {task_type}\n", " metrics:\n", " - name: {metric_name}\n", " type: {metric_type}\n", " value: {metric_value}\n", "---\n", "\n", "# {model_id}\n", "\n", "{description}\n", "\n", "## Example Images\n", "\n", "{example_images_markdown}\n", "\n", "\"\"\".strip()\n", "\n", "(Path(model_repo.local_dir) / 'README.md').write_text(readme_txt)\n", "\n", "commit_url = model_repo.push_to_hub()\n", "\n", "print(\"Check out your model at:\")\n", "print(f\"https://huggingface.co/{hf_username}/{model_id}\")" ], "execution_count": 73, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Cloning https://huggingface.co/SriramSridhar78/sriram-car-classifier into local empty directory.\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "347a83d4a21f46c38eabe192967754f7", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.37k/328M [00:00 main\n", "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Check out your model at:\n", "https://huggingface.co/SriramSridhar78/sriram-car-classifier\n" ] } ] } ] }