notebooks/kinference/ORTClassification.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-02-10T13:11:49.634012Z",
     "start_time": "2025-02-10T13:11:49.034388Z"
    }
   },
   "source": [
    "%use kotlin-dl\n",
    "\n",
    "val kinferenceVersion = \"0.2.26\"\n",
    "val ktorVersion = \"3.0.3\"\n",
    "\n",
    "USE {\n",
    "    repositories {\n",
    "        mavenCentral()\n",
    "        maven(\"https://packages.jetbrains.team/maven/p/ki/maven\")\n",
    "        maven(\"https://packages.jetbrains.team/maven/p/grazi/grazie-platform-public\")\n",
    "    }\n",
    "    dependencies {\n",
    "        implementation(\"io.kinference:inference-core-jvm:$kinferenceVersion\")\n",
    "        implementation(\"io.kinference:inference-ort-jvm:$kinferenceVersion\")\n",
    "        implementation(\"io.kinference:serializer-protobuf-jvm:$kinferenceVersion\")\n",
    "        implementation(\"io.kinference:ndarray-core-jvm:$kinferenceVersion\")\n",
    "\n",
    "        implementation(\"io.ktor:ktor-client-core-jvm:$ktorVersion\")\n",
    "        implementation(\"io.ktor:ktor-client-cio-jvm:$ktorVersion\")\n",
    "    }\n",
    "}"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-10T13:11:51.657130Z",
     "start_time": "2025-02-10T13:11:51.591525Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import io.kinference.ndarray.arrays.*\n",
    "import io.kinference.ndarray.arrays.FloatNDArray.Companion.invoke\n",
    "import io.kinference.ort.ORTEngine\n",
    "import io.kinference.ort.data.tensor.ORTTensor\n",
    "import io.kinference.utils.CommonDataLoader\n",
    "import io.kinference.utils.inlines.InlineInt\n",
    "import io.kinference.utils.toLongArray\n",
    "import okio.Path.Companion.toPath\n",
    "import org.jetbrains.kotlinx.dl.dataset.generator.FromFolders\n",
    "import org.jetbrains.kotlinx.dl.impl.inference.imagerecognition.InputType\n",
    "import java.awt.image.BufferedImage\n",
    "import kotlin.collections.mutableMapOf\n",
    "import io.kinference.core.KIONNXData\n",
    "import io.kinference.core.data.tensor.KITensor\n",
    "import io.kinference.ndarray.arrays.LongNDArray\n",
    "import io.kinference.ndarray.arrays.NumberNDArrayCore\n",
    "import io.ktor.client.HttpClient\n",
    "import io.ktor.client.plugins.HttpTimeout\n",
    "import io.ktor.client.request.prepareRequest\n",
    "import io.ktor.client.statement.bodyAsChannel\n",
    "import io.ktor.util.cio.writeChannel\n",
    "import io.ktor.utils.io.copyAndClose\n",
    "import java.io.File\n",
    "import kotlinx.coroutines.runBlocking"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-10T13:12:04.361732Z",
     "start_time": "2025-02-10T13:12:04.112986Z"
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   },
   "cell_type": "code",
   "source": [
    "/**\n",
    " * Directory used to store cached files.\n",
    " *\n",
    " * This variable combines the user's current working directory\n",
    " * with a \"cache\" subdirectory to create the path for storing cache files.\n",
    " * It is used in various functions to check for existing files or directories,\n",
    " * create new ones if they do not exist, and manage the caching of downloaded files.\n",
    " */\n",
    "val cacheDirectory = System.getProperty(\"user.dir\") + \"/.cache/\"\n",
    "\n",
    "\n",
    "/**\n",
    " * Downloads a file from the given URL and saves it with the specified file name.\n",
    " *\n",
    " * Checks if the directory specified by `cacheDirectory` exists.\n",
    " * If not, it creates the directory. If the file already exists,\n",
    " * the download is skipped. Otherwise, the file is downloaded\n",
    " * using an HTTP client with a 10-minute timeout setting.\n",
    " *\n",
    " * @param url The URL from which to download the file.\n",
    " * @param fileName The name to use for the downloaded file.\n",
    " * @param timeout Optional timeout duration for the download request, in milliseconds.\n",
    " * Defaults to 600_000 milliseconds (10 minutes).\n",
    " * Increase the timeout if you are not sure that download for the particular model with fit into the default timeout.\n",
    " */\n",
    "suspend fun downloadFile(url: String, fileName: String, timeout: Long = 600_000) {\n",
    "    // Ensure the predefined path is treated as a directory\n",
    "    val directory = File(cacheDirectory)\n",
    "\n",
    "    // Check if the directory exists, if not create it\n",
    "    if (!directory.exists()) {\n",
    "        println(\"Predefined directory doesn't exist. Creating directory at $cacheDirectory.\")\n",
    "        directory.mkdirs() // Create the directory if it doesn't exist\n",
    "    }\n",
    "\n",
    "    // Check if the file already exists\n",
    "    val file = File(directory, fileName)\n",
    "    if (file.exists()) {\n",
    "        println(\"File already exists at ${file.absolutePath}. Skipping download.\")\n",
    "        return // Exit the function if the file exists\n",
    "    }\n",
    "\n",
    "    // Create an instance of HttpClient with custom timeout settings\n",
    "    val client = HttpClient {\n",
    "        install(HttpTimeout) {\n",
    "            requestTimeoutMillis = timeout\n",
    "        }\n",
    "    }\n",
    "\n",
    "    // Download the file and write to the specified output path\n",
    "    client.prepareRequest(url).execute { response ->\n",
    "        response.bodyAsChannel().copyAndClose(file.writeChannel())\n",
    "    }\n",
    "\n",
    "    client.close()\n",
    "}"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-10T13:12:11.084298Z",
     "start_time": "2025-02-10T13:12:11.056438Z"
    }
   },
   "cell_type": "code",
   "source": [
    "// Constants for input and output tensor names used in the CaffeNet model\n",
    "val INPUT_TENSOR_NAME = \"data_0\"\n",
    "val OUTPUT_TENSOR_NAME = \"prob_1\""
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-10T13:12:15.618890Z",
     "start_time": "2025-02-10T13:12:15.028959Z"
    }
   },
   "cell_type": "code",
   "source": [
    "// Preprocessing pipeline for input images using KotlinDL\n",
    "val preprocessing = pipeline<BufferedImage>()\n",
    "    .resize {\n",
    "        outputWidth = 224\n",
    "        outputHeight = 224\n",
    "        interpolation = InterpolationType.BILINEAR\n",
    "    }\n",
    "    .convert { colorMode = ColorMode.BGR }\n",
    "    .toFloatArray { }\n",
    "    .call(InputType.CAFFE.preprocessing())\n",
    "\n",
    "// Path to the small dataset of dogs vs cats images (100 images)\n",
    "val dogsVsCatsDatasetPath = dogsCatsSmallDatasetPath()"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-10T13:12:21.225331Z",
     "start_time": "2025-02-10T13:12:21.003035Z"
    }
   },
   "cell_type": "code",
   "source": [
    "/**\n",
    " * Creates a Map of input tensors categorized by their respective classes (e.g., \"cat\" and \"dog\").\n",
    " *\n",
    " * This function reads images from the dataset, preprocesses them,\n",
    " * transposes the tensors to the required format, and groups them\n",
    " * based on their class label.\n",
    " *\n",
    " * @return A Map where the keys are the class labels (e.g., \"cat\" and \"dog\"),\n",
    " * and the values are lists of KITensor objects representing the input tensors\n",
    " * for each class.\n",
    " */\n",
    "suspend fun createInputs(): Map<String, List<ORTTensor>> {\n",
    "    val dataset = OnFlyImageDataset.create(\n",
    "        File(dogsVsCatsDatasetPath),\n",
    "        FromFolders(mapping = mapOf(\"cat\" to 0, \"dog\" to 1)),\n",
    "        preprocessing\n",
    "    ).shuffle()\n",
    "\n",
    "\n",
    "    val tensorShape = intArrayOf(1, 224, 224, 3)        // Original tensor shape is [batch, width, height, channel]\n",
    "    val permuteAxis = intArrayOf(0, 3, 1, 2)            // Permutations for shape [batch, channel, width, height]\n",
    "    val inputTensors = mutableMapOf<String, MutableList<ORTTensor>>()\n",
    "\n",
    "    for (i in 0 until dataset.xSize()) {\n",
    "        val inputData = dataset.getX(i)\n",
    "        val inputClass = if (dataset.getY(i).toInt() == 0) \"cat\" else \"dog\"\n",
    "        val floatNDArray = FloatNDArray(tensorShape) { index: InlineInt -> inputData[index.value] }.transpose(permuteAxis)  // Create an NDArray from the image data\n",
    "        val inputTensor = ORTTensor(floatNDArray.array.toArray(), floatNDArray.shape.toLongArray(), INPUT_TENSOR_NAME)      // Transpose and create a tensor from the NDArray\n",
    "        inputTensors.putIfAbsent(inputClass, mutableListOf())\n",
    "        inputTensors[inputClass]!!.add(inputTensor)\n",
    "    }\n",
    "\n",
    "    return inputTensors\n",
    "}"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-10T13:12:25.643303Z",
     "start_time": "2025-02-10T13:12:25.515901Z"
    }
   },
   "cell_type": "code",
   "source": [
    "/**\n",
    " * Displays the top 5 predictions with their corresponding labels and scores.\n",
    " *\n",
    " * @param predictions The predicted scores in a multidimensional array format.\n",
    " * @param classLabels The list of class labels corresponding to the predictions.\n",
    " * @param originalClass The actual class label of the instance being predicted.\n",
    " */\n",
    "fun displayTopPredictions(predictions: ORTTensor, classLabels: List<String>, originalClass: String) {\n",
    "    val predictionArray = predictions.toFloatArray()\n",
    "    val indexedScores = predictionArray.withIndex().sortedByDescending { it.value }.take(5)\n",
    "\n",
    "    println(\"\\nOriginal class: $originalClass\")\n",
    "    println(\"Top 5 predictions:\")\n",
    "    for ((index, score) in indexedScores) {\n",
    "        val predictedClassLabel = if (index in classLabels.indices) classLabels[index] else \"Unknown\"\n",
    "        println(\"${predictedClassLabel}: ${\"%.2f\".format(score * 100)}%\")\n",
    "    }\n",
    "}"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-10T13:12:31.362861Z",
     "start_time": "2025-02-10T13:12:31.333284Z"
    }
   },
   "cell_type": "code",
   "source": [
    "val modelUrl = \"https://github.com/onnx/models/raw/main/validated/vision/classification/caffenet/model/caffenet-12.onnx\"\n",
    "val synsetUrl = \"https://s3.amazonaws.com/onnx-model-zoo/synset.txt\"\n",
    "val modelName = \"CaffeNet\""
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-10T13:12:50.695940Z",
     "start_time": "2025-02-10T13:12:37.789138Z"
    }
   },
   "cell_type": "code",
   "source": [
    "runBlocking {\n",
    "    println(\"Downloading model from: $modelUrl\")\n",
    "    downloadFile(modelUrl, \"$modelName.onnx\")\n",
    "    println(\"Downloading synset from: $synsetUrl\")\n",
    "    downloadFile(synsetUrl, \"synset.txt\")\n",
    "\n",
    "    val classLabels = File(\"$cacheDirectory/synset.txt\").readLines()\n",
    "\n",
    "    println(\"Loading model...\")\n",
    "    val model = ORTEngine.loadModel(\"$cacheDirectory/$modelName.onnx\".toPath())\n",
    "    println(\"Creating inputs...\")\n",
    "    val inputTensors = createInputs()\n",
    "\n",
    "    println(\"Starting inference...\")\n",
    "    inputTensors.forEach { dataClass ->\n",
    "        dataClass.value.forEach { tensor ->\n",
    "            val actualOutputs = model.predict(listOf(tensor))\n",
    "            val predictions = actualOutputs[OUTPUT_TENSOR_NAME]!! as ORTTensor\n",
    "            displayTopPredictions(predictions, classLabels, dataClass.key)\n",
    "        }\n",
    "    }\n",
    "}"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading model from: https://github.com/onnx/models/raw/main/validated/vision/classification/caffenet/model/caffenet-12.onnx\n",
      "Predefined directory doesn't exist. Creating directory at /Users/pavel.gorgulov/Projects/Kotlin/Kotlin-AI-Examples/notebooks/kinference//.cache/.\n",
      "Downloading synset from: https://s3.amazonaws.com/onnx-model-zoo/synset.txt\n",
      "Loading model...\n",
      "Creating inputs...\n",
      "Starting inference...\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n03825788 nipple: 12,60%\n",
      "n02097298 Scotch terrier, Scottish terrier, Scottie: 9,55%\n",
      "n02094433 Yorkshire terrier: 4,42%\n",
      "n03944341 pinwheel: 4,40%\n",
      "n07615774 ice lolly, lolly, lollipop, popsicle: 4,40%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02124075 Egyptian cat: 42,12%\n",
      "n02123045 tabby, tabby cat: 29,15%\n",
      "n02123159 tiger cat: 17,08%\n",
      "n02127052 lynx, catamount: 4,28%\n",
      "n02123394 Persian cat: 3,40%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02104365 schipperke: 9,79%\n",
      "n02107312 miniature pinscher: 9,33%\n",
      "n02105056 groenendael: 4,72%\n",
      "n02110627 affenpinscher, monkey pinscher, monkey dog: 4,03%\n",
      "n02108915 French bulldog: 3,96%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123394 Persian cat: 95,96%\n",
      "n02123045 tabby, tabby cat: 2,02%\n",
      "n02123159 tiger cat: 0,87%\n",
      "n02086240 Shih-Tzu: 0,18%\n",
      "n02086910 papillon: 0,18%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02443114 polecat, fitch, foulmart, foumart, Mustela putorius: 22,63%\n",
      "n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus: 18,98%\n",
      "n02356798 fox squirrel, eastern fox squirrel, Sciurus niger: 11,07%\n",
      "n02441942 weasel: 10,78%\n",
      "n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens: 8,54%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02124075 Egyptian cat: 33,81%\n",
      "n02123597 Siamese cat, Siamese: 32,13%\n",
      "n02127052 lynx, catamount: 8,46%\n",
      "n02123045 tabby, tabby cat: 5,89%\n",
      "n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin: 4,71%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02124075 Egyptian cat: 37,54%\n",
      "n02123045 tabby, tabby cat: 31,74%\n",
      "n02123159 tiger cat: 28,95%\n",
      "n02127052 lynx, catamount: 1,46%\n",
      "n02123394 Persian cat: 0,09%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02124075 Egyptian cat: 46,10%\n",
      "n02123045 tabby, tabby cat: 36,38%\n",
      "n02123159 tiger cat: 15,85%\n",
      "n02127052 lynx, catamount: 1,42%\n",
      "n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor: 0,07%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n04399382 teddy, teddy bear: 12,71%\n",
      "n02342885 hamster: 8,14%\n",
      "n03724870 mask: 7,30%\n",
      "n02655020 puffer, pufferfish, blowfish, globefish: 6,08%\n",
      "n01818515 macaw: 5,13%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02124075 Egyptian cat: 31,53%\n",
      "n02123045 tabby, tabby cat: 17,05%\n",
      "n02123159 tiger cat: 8,13%\n",
      "n02127052 lynx, catamount: 7,82%\n",
      "n02123597 Siamese cat, Siamese: 7,05%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02948072 candle, taper, wax light: 11,13%\n",
      "n02342885 hamster: 8,57%\n",
      "n04493381 tub, vat: 6,47%\n",
      "n02120079 Arctic fox, white fox, Alopex lagopus: 6,14%\n",
      "n02328150 Angora, Angora rabbit: 5,26%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123394 Persian cat: 50,95%\n",
      "n02342885 hamster: 15,36%\n",
      "n02104365 schipperke: 5,43%\n",
      "n02442845 mink: 5,11%\n",
      "n02112350 keeshond: 4,79%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123045 tabby, tabby cat: 68,75%\n",
      "n02123159 tiger cat: 17,15%\n",
      "n02124075 Egyptian cat: 14,00%\n",
      "n02127052 lynx, catamount: 0,07%\n",
      "n02971356 carton: 0,01%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123159 tiger cat: 16,50%\n",
      "n02085620 Chihuahua: 12,48%\n",
      "n02113023 Pembroke, Pembroke Welsh corgi: 9,89%\n",
      "n02112018 Pomeranian: 7,61%\n",
      "n02105855 Shetland sheepdog, Shetland sheep dog, Shetland: 6,52%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02124075 Egyptian cat: 47,66%\n",
      "n02123045 tabby, tabby cat: 32,55%\n",
      "n02123159 tiger cat: 14,07%\n",
      "n02127052 lynx, catamount: 3,46%\n",
      "n04265275 space heater: 0,93%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123045 tabby, tabby cat: 33,85%\n",
      "n02124075 Egyptian cat: 28,17%\n",
      "n02127052 lynx, catamount: 20,78%\n",
      "n02123394 Persian cat: 7,81%\n",
      "n02123159 tiger cat: 7,72%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02443114 polecat, fitch, foulmart, foumart, Mustela putorius: 29,87%\n",
      "n02123597 Siamese cat, Siamese: 26,00%\n",
      "n02441942 weasel: 11,64%\n",
      "n02443484 black-footed ferret, ferret, Mustela nigripes: 9,34%\n",
      "n02124075 Egyptian cat: 3,57%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n01514859 hen: 13,62%\n",
      "n02124075 Egyptian cat: 12,72%\n",
      "n02123045 tabby, tabby cat: 11,09%\n",
      "n01704323 triceratops: 6,24%\n",
      "n02123159 tiger cat: 4,47%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123159 tiger cat: 64,81%\n",
      "n02123045 tabby, tabby cat: 17,86%\n",
      "n02127052 lynx, catamount: 5,79%\n",
      "n02123394 Persian cat: 5,29%\n",
      "n02124075 Egyptian cat: 4,16%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123045 tabby, tabby cat: 77,07%\n",
      "n02123159 tiger cat: 11,52%\n",
      "n02124075 Egyptian cat: 7,80%\n",
      "n02971356 carton: 0,50%\n",
      "n03887697 paper towel: 0,31%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02492035 capuchin, ringtail, Cebus capucinus: 48,79%\n",
      "n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor: 20,10%\n",
      "n02119789 kit fox, Vulpes macrotis: 2,67%\n",
      "n02119022 red fox, Vulpes vulpes: 2,56%\n",
      "n02442845 mink: 2,26%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123045 tabby, tabby cat: 28,93%\n",
      "n02124075 Egyptian cat: 14,67%\n",
      "n02123159 tiger cat: 10,48%\n",
      "n02127052 lynx, catamount: 9,74%\n",
      "n02325366 wood rabbit, cottontail, cottontail rabbit: 6,35%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123045 tabby, tabby cat: 29,96%\n",
      "n02123159 tiger cat: 29,02%\n",
      "n02124075 Egyptian cat: 15,57%\n",
      "n02127052 lynx, catamount: 13,35%\n",
      "n02129604 tiger, Panthera tigris: 1,74%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02106382 Bouvier des Flandres, Bouviers des Flandres: 19,01%\n",
      "n02090721 Irish wolfhound: 16,64%\n",
      "n02111277 Newfoundland, Newfoundland dog: 14,96%\n",
      "n02111129 Leonberg: 11,21%\n",
      "n02108551 Tibetan mastiff: 6,65%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02124075 Egyptian cat: 40,29%\n",
      "n02123045 tabby, tabby cat: 32,39%\n",
      "n02123159 tiger cat: 13,81%\n",
      "n02123597 Siamese cat, Siamese: 5,14%\n",
      "n03793489 mouse, computer mouse: 2,98%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123159 tiger cat: 38,32%\n",
      "n02123045 tabby, tabby cat: 34,74%\n",
      "n02123394 Persian cat: 9,84%\n",
      "n02124075 Egyptian cat: 9,00%\n",
      "n02127052 lynx, catamount: 7,74%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02124075 Egyptian cat: 74,99%\n",
      "n02123597 Siamese cat, Siamese: 8,17%\n",
      "n02123045 tabby, tabby cat: 7,37%\n",
      "n02123159 tiger cat: 1,04%\n",
      "n02109961 Eskimo dog, husky: 0,71%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123597 Siamese cat, Siamese: 92,10%\n",
      "n02328150 Angora, Angora rabbit: 4,67%\n",
      "n02127052 lynx, catamount: 1,99%\n",
      "n02123394 Persian cat: 0,61%\n",
      "n02124075 Egyptian cat: 0,21%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02110627 affenpinscher, monkey pinscher, monkey dog: 27,30%\n",
      "n02097298 Scotch terrier, Scottish terrier, Scottie: 21,86%\n",
      "n02097047 miniature schnauzer: 18,38%\n",
      "n02093859 Kerry blue terrier: 14,50%\n",
      "n02106382 Bouvier des Flandres, Bouviers des Flandres: 3,21%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123597 Siamese cat, Siamese: 99,92%\n",
      "n03887697 paper towel: 0,01%\n",
      "n02112350 keeshond: 0,01%\n",
      "n02127052 lynx, catamount: 0,01%\n",
      "n04265275 space heater: 0,01%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123394 Persian cat: 59,49%\n",
      "n02123597 Siamese cat, Siamese: 10,85%\n",
      "n03325584 feather boa, boa: 5,50%\n",
      "n02123045 tabby, tabby cat: 4,68%\n",
      "n02123159 tiger cat: 2,79%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02124075 Egyptian cat: 55,67%\n",
      "n02123045 tabby, tabby cat: 34,51%\n",
      "n02123159 tiger cat: 9,65%\n",
      "n02127052 lynx, catamount: 0,10%\n",
      "n02325366 wood rabbit, cottontail, cottontail rabbit: 0,02%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02105056 groenendael: 50,96%\n",
      "n02104365 schipperke: 24,68%\n",
      "n02445715 skunk, polecat, wood pussy: 2,21%\n",
      "n02099267 flat-coated retriever: 2,19%\n",
      "n02106382 Bouvier des Flandres, Bouviers des Flandres: 1,48%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123045 tabby, tabby cat: 69,15%\n",
      "n02123159 tiger cat: 17,21%\n",
      "n02124075 Egyptian cat: 12,80%\n",
      "n02123394 Persian cat: 0,11%\n",
      "n02127052 lynx, catamount: 0,10%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n04235860 sleeping bag: 45,35%\n",
      "n02443484 black-footed ferret, ferret, Mustela nigripes: 4,77%\n",
      "n02342885 hamster: 3,51%\n",
      "n03825788 nipple: 2,47%\n",
      "n03814639 neck brace: 2,30%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02124075 Egyptian cat: 78,11%\n",
      "n02123045 tabby, tabby cat: 15,53%\n",
      "n02123159 tiger cat: 5,91%\n",
      "n02127052 lynx, catamount: 0,36%\n",
      "n01883070 wombat: 0,05%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02124075 Egyptian cat: 52,71%\n",
      "n02123045 tabby, tabby cat: 32,32%\n",
      "n02123159 tiger cat: 5,05%\n",
      "n02123597 Siamese cat, Siamese: 4,17%\n",
      "n02127052 lynx, catamount: 2,00%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02124075 Egyptian cat: 70,32%\n",
      "n02127052 lynx, catamount: 17,60%\n",
      "n02123159 tiger cat: 6,90%\n",
      "n02123045 tabby, tabby cat: 3,53%\n",
      "n02128757 snow leopard, ounce, Panthera uncia: 0,84%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123597 Siamese cat, Siamese: 28,02%\n",
      "n01877812 wallaby, brush kangaroo: 22,79%\n",
      "n02098286 West Highland white terrier: 10,68%\n",
      "n02328150 Angora, Angora rabbit: 5,79%\n",
      "n02085620 Chihuahua: 4,33%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02124075 Egyptian cat: 22,17%\n",
      "n02123045 tabby, tabby cat: 14,99%\n",
      "n02123597 Siamese cat, Siamese: 5,90%\n",
      "n03887697 paper towel: 5,32%\n",
      "n02123159 tiger cat: 4,61%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n03476991 hair spray: 9,00%\n",
      "n03729826 matchstick: 3,95%\n",
      "n03325584 feather boa, boa: 3,62%\n",
      "n01443537 goldfish, Carassius auratus: 3,59%\n",
      "n04456115 torch: 3,59%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02328150 Angora, Angora rabbit: 20,84%\n",
      "n02364673 guinea pig, Cavia cobaya: 10,39%\n",
      "n02086910 papillon: 8,62%\n",
      "n02101388 Brittany spaniel: 7,56%\n",
      "n02086646 Blenheim spaniel: 4,62%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02113186 Cardigan, Cardigan Welsh corgi: 28,37%\n",
      "n02123045 tabby, tabby cat: 14,76%\n",
      "n02124075 Egyptian cat: 6,84%\n",
      "n02105855 Shetland sheepdog, Shetland sheep dog, Shetland: 3,71%\n",
      "n02123597 Siamese cat, Siamese: 3,16%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02100236 German short-haired pointer: 35,06%\n",
      "n02088632 bluetick: 12,47%\n",
      "n02095314 wire-haired fox terrier: 8,47%\n",
      "n02087046 toy terrier: 7,71%\n",
      "n02093647 Bedlington terrier: 5,28%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123045 tabby, tabby cat: 63,85%\n",
      "n02124075 Egyptian cat: 16,04%\n",
      "n02123159 tiger cat: 15,30%\n",
      "n02127052 lynx, catamount: 4,45%\n",
      "n01883070 wombat: 0,18%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123394 Persian cat: 80,13%\n",
      "n02123045 tabby, tabby cat: 9,62%\n",
      "n02124075 Egyptian cat: 4,23%\n",
      "n02123159 tiger cat: 3,29%\n",
      "n02112350 keeshond: 0,52%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02123597 Siamese cat, Siamese: 25,15%\n",
      "n02096585 Boston bull, Boston terrier: 17,39%\n",
      "n02112706 Brabancon griffon: 10,57%\n",
      "n02113978 Mexican hairless: 6,20%\n",
      "n02087046 toy terrier: 6,04%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02124075 Egyptian cat: 49,89%\n",
      "n02123045 tabby, tabby cat: 18,54%\n",
      "n02123159 tiger cat: 13,55%\n",
      "n02127052 lynx, catamount: 11,27%\n",
      "n02342885 hamster: 1,49%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02127052 lynx, catamount: 91,21%\n",
      "n02123159 tiger cat: 1,97%\n",
      "n02115913 dhole, Cuon alpinus: 1,60%\n",
      "n02124075 Egyptian cat: 1,31%\n",
      "n02114855 coyote, prairie wolf, brush wolf, Canis latrans: 1,15%\n",
      "\n",
      "Original class: cat\n",
      "Top 5 predictions:\n",
      "n02096051 Airedale, Airedale terrier: 14,10%\n",
      "n02098105 soft-coated wheaten terrier: 11,96%\n",
      "n02095570 Lakeland terrier: 6,89%\n",
      "n02102318 cocker spaniel, English cocker spaniel, cocker: 5,89%\n",
      "n02091635 otterhound, otter hound: 5,37%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02099712 Labrador retriever: 69,51%\n",
      "n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier: 9,96%\n",
      "n02099601 golden retriever: 4,45%\n",
      "n02093256 Staffordshire bullterrier, Staffordshire bull terrier: 4,02%\n",
      "n02085620 Chihuahua: 2,48%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02110806 basenji: 32,48%\n",
      "n02091244 Ibizan hound, Ibizan Podenco: 16,25%\n",
      "n02091134 whippet: 10,54%\n",
      "n02085620 Chihuahua: 9,54%\n",
      "n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier: 6,12%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02113624 toy poodle: 54,05%\n",
      "n02113712 miniature poodle: 41,62%\n",
      "n02113799 standard poodle: 4,03%\n",
      "n02093647 Bedlington terrier: 0,16%\n",
      "n02102973 Irish water spaniel: 0,07%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02099267 flat-coated retriever: 41,45%\n",
      "n02099429 curly-coated retriever: 11,87%\n",
      "n02099712 Labrador retriever: 10,84%\n",
      "n02099849 Chesapeake Bay retriever: 8,46%\n",
      "n02097130 giant schnauzer: 4,32%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02102318 cocker spaniel, English cocker spaniel, cocker: 23,23%\n",
      "n02088094 Afghan hound, Afghan: 13,08%\n",
      "n02110627 affenpinscher, monkey pinscher, monkey dog: 10,48%\n",
      "n02097130 giant schnauzer: 7,04%\n",
      "n02097047 miniature schnauzer: 5,65%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02101006 Gordon setter: 10,17%\n",
      "n02108551 Tibetan mastiff: 9,76%\n",
      "n02107574 Greater Swiss Mountain dog: 8,78%\n",
      "n02107683 Bernese mountain dog: 8,38%\n",
      "n02109525 Saint Bernard, St Bernard: 7,36%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier: 16,48%\n",
      "n02088364 beagle: 15,32%\n",
      "n02087394 Rhodesian ridgeback: 9,30%\n",
      "n02100583 vizsla, Hungarian pointer: 8,11%\n",
      "n02090379 redbone: 7,62%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02100735 English setter: 22,80%\n",
      "n02088238 basset, basset hound: 12,22%\n",
      "n02102040 English springer, English springer spaniel: 9,40%\n",
      "n02109047 Great Dane: 6,24%\n",
      "n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier: 6,18%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02099601 golden retriever: 48,01%\n",
      "n02093991 Irish terrier: 5,83%\n",
      "n02102318 cocker spaniel, English cocker spaniel, cocker: 4,80%\n",
      "n02088466 bloodhound, sleuthhound: 3,28%\n",
      "n02088094 Afghan hound, Afghan: 2,92%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02102318 cocker spaniel, English cocker spaniel, cocker: 96,24%\n",
      "n02102480 Sussex spaniel: 3,09%\n",
      "n02101556 clumber, clumber spaniel: 0,27%\n",
      "n02100735 English setter: 0,09%\n",
      "n02102177 Welsh springer spaniel: 0,06%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02106166 Border collie: 24,83%\n",
      "n02102040 English springer, English springer spaniel: 11,51%\n",
      "n02111277 Newfoundland, Newfoundland dog: 8,92%\n",
      "n02100735 English setter: 8,60%\n",
      "n02106030 collie: 7,60%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02091467 Norwegian elkhound, elkhound: 25,01%\n",
      "n02109961 Eskimo dog, husky: 20,50%\n",
      "n02110185 Siberian husky: 9,59%\n",
      "n03803284 muzzle: 6,81%\n",
      "n02115641 dingo, warrigal, warragal, Canis dingo: 5,45%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02106382 Bouvier des Flandres, Bouviers des Flandres: 29,47%\n",
      "n02110627 affenpinscher, monkey pinscher, monkey dog: 21,49%\n",
      "n02097130 giant schnauzer: 18,30%\n",
      "n02096177 cairn, cairn terrier: 6,55%\n",
      "n02090721 Irish wolfhound: 6,10%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02089867 Walker hound, Walker foxhound: 20,44%\n",
      "n02088238 basset, basset hound: 14,26%\n",
      "n02088632 bluetick: 13,71%\n",
      "n02088364 beagle: 10,41%\n",
      "n02107574 Greater Swiss Mountain dog: 8,38%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02110958 pug, pug-dog: 81,80%\n",
      "n02085620 Chihuahua: 2,11%\n",
      "n02108915 French bulldog: 1,81%\n",
      "n02099712 Labrador retriever: 1,66%\n",
      "n02086079 Pekinese, Pekingese, Peke: 1,62%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02094114 Norfolk terrier: 22,99%\n",
      "n02093991 Irish terrier: 8,92%\n",
      "n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier: 8,68%\n",
      "n02093256 Staffordshire bullterrier, Staffordshire bull terrier: 6,95%\n",
      "n02093754 Border terrier: 5,94%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02111889 Samoyed, Samoyede: 94,90%\n",
      "n02120079 Arctic fox, white fox, Alopex lagopus: 2,15%\n",
      "n02114548 white wolf, Arctic wolf, Canis lupus tundrarum: 0,83%\n",
      "n02112018 Pomeranian: 0,71%\n",
      "n02111500 Great Pyrenees: 0,46%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02088364 beagle: 28,57%\n",
      "n02113023 Pembroke, Pembroke Welsh corgi: 12,52%\n",
      "n02110185 Siberian husky: 6,93%\n",
      "n02085620 Chihuahua: 5,12%\n",
      "n02101388 Brittany spaniel: 4,40%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02091134 whippet: 7,52%\n",
      "n02110958 pug, pug-dog: 5,06%\n",
      "n02108915 French bulldog: 4,39%\n",
      "n02091032 Italian greyhound: 4,29%\n",
      "n02110185 Siberian husky: 2,54%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier: 92,02%\n",
      "n02099712 Labrador retriever: 1,91%\n",
      "n02091134 whippet: 1,52%\n",
      "n02108089 boxer: 1,26%\n",
      "n02093256 Staffordshire bullterrier, Staffordshire bull terrier: 0,90%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02091134 whippet: 15,45%\n",
      "n02091831 Saluki, gazelle hound: 13,97%\n",
      "n02091032 Italian greyhound: 8,77%\n",
      "n02113023 Pembroke, Pembroke Welsh corgi: 6,10%\n",
      "n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier: 5,58%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02102177 Welsh springer spaniel: 18,55%\n",
      "n02100877 Irish setter, red setter: 11,40%\n",
      "n02108089 boxer: 10,48%\n",
      "n02090379 redbone: 8,03%\n",
      "n02102040 English springer, English springer spaniel: 6,08%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02100735 English setter: 16,92%\n",
      "n02098286 West Highland white terrier: 9,71%\n",
      "n02101556 clumber, clumber spaniel: 8,09%\n",
      "n02094114 Norfolk terrier: 6,04%\n",
      "n02095889 Sealyham terrier, Sealyham: 5,83%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02099712 Labrador retriever: 57,07%\n",
      "n02093256 Staffordshire bullterrier, Staffordshire bull terrier: 14,44%\n",
      "n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier: 14,12%\n",
      "n02105412 kelpie: 5,37%\n",
      "n02106550 Rottweiler: 1,93%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02098413 Lhasa, Lhasa apso: 23,38%\n",
      "n02097474 Tibetan terrier, chrysanthemum dog: 21,55%\n",
      "n02096437 Dandie Dinmont, Dandie Dinmont terrier: 15,51%\n",
      "n02094114 Norfolk terrier: 8,93%\n",
      "n02091635 otterhound, otter hound: 5,24%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02085782 Japanese spaniel: 50,31%\n",
      "n02106030 collie: 13,18%\n",
      "n02086910 papillon: 10,76%\n",
      "n02108551 Tibetan mastiff: 4,94%\n",
      "n02086646 Blenheim spaniel: 3,69%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02106662 German shepherd, German shepherd dog, German police dog, alsatian: 64,11%\n",
      "n02105162 malinois: 12,76%\n",
      "n02107312 miniature pinscher: 8,68%\n",
      "n02115641 dingo, warrigal, warragal, Canis dingo: 3,98%\n",
      "n02105412 kelpie: 3,88%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02086646 Blenheim spaniel: 73,56%\n",
      "n02085782 Japanese spaniel: 9,08%\n",
      "n02101388 Brittany spaniel: 6,82%\n",
      "n02086910 papillon: 5,42%\n",
      "n02102480 Sussex spaniel: 2,81%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier: 11,53%\n",
      "n02091134 whippet: 9,76%\n",
      "n02089867 Walker hound, Walker foxhound: 9,31%\n",
      "n02110341 dalmatian, coach dog, carriage dog: 6,66%\n",
      "n02109047 Great Dane: 5,05%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02099267 flat-coated retriever: 20,24%\n",
      "n02104365 schipperke: 16,02%\n",
      "n02099712 Labrador retriever: 15,10%\n",
      "n02099429 curly-coated retriever: 12,60%\n",
      "n02097130 giant schnauzer: 10,63%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02105855 Shetland sheepdog, Shetland sheep dog, Shetland: 34,15%\n",
      "n02113023 Pembroke, Pembroke Welsh corgi: 24,28%\n",
      "n02106030 collie: 17,28%\n",
      "n02113186 Cardigan, Cardigan Welsh corgi: 5,11%\n",
      "n02106662 German shepherd, German shepherd dog, German police dog, alsatian: 4,32%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02115641 dingo, warrigal, warragal, Canis dingo: 21,60%\n",
      "n02110063 malamute, malemute, Alaskan malamute: 7,04%\n",
      "n02113023 Pembroke, Pembroke Welsh corgi: 5,51%\n",
      "n02110185 Siberian husky: 5,45%\n",
      "n02091831 Saluki, gazelle hound: 5,02%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02115913 dhole, Cuon alpinus: 27,72%\n",
      "n02119022 red fox, Vulpes vulpes: 12,29%\n",
      "n02105412 kelpie: 9,46%\n",
      "n02114855 coyote, prairie wolf, brush wolf, Canis latrans: 8,94%\n",
      "n02115641 dingo, warrigal, warragal, Canis dingo: 7,20%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02115641 dingo, warrigal, warragal, Canis dingo: 48,57%\n",
      "n02105412 kelpie: 22,12%\n",
      "n02132136 brown bear, bruin, Ursus arctos: 7,08%\n",
      "n02105162 malinois: 6,16%\n",
      "n02115913 dhole, Cuon alpinus: 3,66%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02088466 bloodhound, sleuthhound: 46,02%\n",
      "n02108422 bull mastiff: 34,16%\n",
      "n02112706 Brabancon griffon: 13,12%\n",
      "n02090379 redbone: 2,42%\n",
      "n02087394 Rhodesian ridgeback: 1,25%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02087394 Rhodesian ridgeback: 58,78%\n",
      "n02108089 boxer: 16,27%\n",
      "n02108422 bull mastiff: 10,42%\n",
      "n02090379 redbone: 4,86%\n",
      "n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier: 3,97%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02107312 miniature pinscher: 51,52%\n",
      "n02107142 Doberman, Doberman pinscher: 47,95%\n",
      "n02087046 toy terrier: 0,30%\n",
      "n02105412 kelpie: 0,10%\n",
      "n02106550 Rottweiler: 0,08%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02085620 Chihuahua: 49,49%\n",
      "n02091032 Italian greyhound: 14,08%\n",
      "n02099712 Labrador retriever: 8,27%\n",
      "n02087046 toy terrier: 3,76%\n",
      "n02115641 dingo, warrigal, warragal, Canis dingo: 3,33%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02086910 papillon: 60,05%\n",
      "n02106030 collie: 18,83%\n",
      "n02105855 Shetland sheepdog, Shetland sheep dog, Shetland: 10,69%\n",
      "n02113186 Cardigan, Cardigan Welsh corgi: 3,13%\n",
      "n02113023 Pembroke, Pembroke Welsh corgi: 2,98%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02099849 Chesapeake Bay retriever: 41,95%\n",
      "n02107142 Doberman, Doberman pinscher: 15,82%\n",
      "n02100583 vizsla, Hungarian pointer: 14,43%\n",
      "n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier: 9,51%\n",
      "n02099712 Labrador retriever: 6,35%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02112018 Pomeranian: 74,53%\n",
      "n02112350 keeshond: 21,09%\n",
      "n02112137 chow, chow chow: 3,09%\n",
      "n02123394 Persian cat: 0,81%\n",
      "n02105855 Shetland sheepdog, Shetland sheep dog, Shetland: 0,22%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02108000 EntleBucher: 34,77%\n",
      "n02107574 Greater Swiss Mountain dog: 23,76%\n",
      "n02088364 beagle: 19,04%\n",
      "n02107908 Appenzeller: 4,92%\n",
      "n02089867 Walker hound, Walker foxhound: 4,42%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02091032 Italian greyhound: 23,20%\n",
      "n02092339 Weimaraner: 19,45%\n",
      "n02099849 Chesapeake Bay retriever: 7,08%\n",
      "n02109047 Great Dane: 5,95%\n",
      "n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier: 4,78%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02107142 Doberman, Doberman pinscher: 38,63%\n",
      "n02106550 Rottweiler: 31,76%\n",
      "n02107312 miniature pinscher: 15,06%\n",
      "n02105412 kelpie: 4,11%\n",
      "n02089078 black-and-tan coonhound: 3,94%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02097047 miniature schnauzer: 49,37%\n",
      "n02097209 standard schnauzer: 15,24%\n",
      "n02096294 Australian terrier: 6,39%\n",
      "n02097658 silky terrier, Sydney silky: 5,63%\n",
      "n02096437 Dandie Dinmont, Dandie Dinmont terrier: 3,64%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02105412 kelpie: 58,90%\n",
      "n02108551 Tibetan mastiff: 13,90%\n",
      "n02106550 Rottweiler: 12,74%\n",
      "n02107908 Appenzeller: 5,79%\n",
      "n02108000 EntleBucher: 1,86%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02087394 Rhodesian ridgeback: 43,91%\n",
      "n02090379 redbone: 19,93%\n",
      "n02093256 Staffordshire bullterrier, Staffordshire bull terrier: 13,83%\n",
      "n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier: 7,41%\n",
      "n02107312 miniature pinscher: 6,34%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02112706 Brabancon griffon: 37,49%\n",
      "n02107312 miniature pinscher: 17,05%\n",
      "n02085620 Chihuahua: 14,83%\n",
      "n02105162 malinois: 9,37%\n",
      "n02108915 French bulldog: 2,54%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02106166 Border collie: 67,09%\n",
      "n02106030 collie: 16,17%\n",
      "n02107683 Bernese mountain dog: 7,44%\n",
      "n02097474 Tibetan terrier, chrysanthemum dog: 2,89%\n",
      "n02105855 Shetland sheepdog, Shetland sheep dog, Shetland: 1,66%\n",
      "\n",
      "Original class: dog\n",
      "Top 5 predictions:\n",
      "n02085620 Chihuahua: 26,05%\n",
      "n02087046 toy terrier: 24,67%\n",
      "n02110806 basenji: 11,80%\n",
      "n02113186 Cardigan, Cardigan Welsh corgi: 10,09%\n",
      "n02095314 wire-haired fox terrier: 1,93%\n"
     ]
    }
   ],
   "execution_count": 9
  }
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