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1789 lines
17 MiB
1789 lines
17 MiB
6 years ago
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "GeneralizationToImageNetV2",
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"version": "0.3.2",
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"provenance": [],
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"collapsed_sections": [],
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/rwightman/pytorch-image-models/blob/master/notebooks/GeneralizationToImageNetV2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "NxIFjw_uOaHa",
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"colab_type": "text"
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},
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"source": [
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"# How Do ImageNet-1k Models Generalize to ImageNet-V2?\n",
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"\n",
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"I was recently [benchmarking the runtime performance](https://colab.research.google.com/github/rwightman/pytorch-image-models/blob/master/notebooks/EffResNetComparison.ipynb) of some models in Colab on the [ImageNet-V2 dataset](https://github.com/modestyachts/ImageNetV2) and noticed something interesting: the [Facebook WSL Instagram pretrained ResNeXt](https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/) model had a smaller accuracy gap than any model I'd seen to date. I decided to dig in a bit more in this notebook and compare the rest of the WSL models and a reasonable sampling of other models wrt to their generalization gap on ImageNet-1k vs ImageNet-V2."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "gncNNhwIOMma",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 136
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},
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"outputId": "631f1f6a-4959-4bff-9e11-51d61b33fa5b"
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},
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"source": [
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"!pip install timm"
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],
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: timm in /usr/local/lib/python3.6/dist-packages (0.1.8)\n",
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"Requirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (from timm) (0.3.0)\n",
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"Requirement already satisfied: torch>=1.0 in /usr/local/lib/python3.6/dist-packages (from timm) (1.1.0)\n",
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"Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision->timm) (4.3.0)\n",
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"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision->timm) (1.16.4)\n",
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"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision->timm) (1.12.0)\n",
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"Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow>=4.1.1->torchvision->timm) (0.46)\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "xBDgzTWROeED",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 306
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},
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"outputId": "76c9e437-2dda-41aa-c61d-e42553ccf443"
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},
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"source": [
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"# For our convenience, take a peek at what we're working with\n",
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"!nvidia-smi"
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],
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Sat Jul 6 22:42:48 2019 \n",
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"+-----------------------------------------------------------------------------+\n",
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"| NVIDIA-SMI 418.67 Driver Version: 410.79 CUDA Version: 10.0 |\n",
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"|-------------------------------+----------------------+----------------------+\n",
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"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
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"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
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"|===============================+======================+======================|\n",
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"| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n",
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"| N/A 48C P8 16W / 70W | 0MiB / 15079MiB | 0% Default |\n",
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"+-------------------------------+----------------------+----------------------+\n",
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" \n",
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"+-----------------------------------------------------------------------------+\n",
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"| Processes: GPU Memory |\n",
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"| GPU PID Type Process name Usage |\n",
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"|=============================================================================|\n",
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"| No running processes found |\n",
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"+-----------------------------------------------------------------------------+\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "jXEyxmp_OoLF",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 85
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},
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"outputId": "dbfc984a-9039-42ca-d96c-3d42ad23337b"
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},
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"source": [
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"# Import the core modules, check which GPU we end up with and scale batch size accordingly\n",
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"import torch\n",
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"torch.backends.cudnn.benchmark = True\n",
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"\n",
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"import timm\n",
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"from timm.data import *\n",
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"from timm.utils import *\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import pynvml\n",
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"from collections import OrderedDict\n",
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"import logging\n",
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"import time\n",
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"\n",
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"def log_gpu_memory():\n",
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" handle = pynvml.nvmlDeviceGetHandleByIndex(0)\n",
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" info = pynvml.nvmlDeviceGetMemoryInfo(handle)\n",
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" info.free = round(info.free / 1024**2)\n",
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" info.used = round(info.used / 1024**2)\n",
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" logging.info('GPU memory free: {}, memory used: {}'.format(info.free, info.used))\n",
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" return info.used\n",
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"\n",
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"def get_gpu_memory_total():\n",
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" handle = pynvml.nvmlDeviceGetHandleByIndex(0)\n",
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" info = pynvml.nvmlDeviceGetMemoryInfo(handle)\n",
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" info.total = round(info.total / 1024**2)\n",
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" return info.total\n",
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"\n",
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"setup_default_logging()\n",
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" \n",
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"print('PyTorch version:', torch.__version__)\n",
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"if torch.cuda.is_available():\n",
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" print('CUDA available')\n",
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" device='cuda'\n",
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"else:\n",
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" print('CUDA is not available')\n",
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" device='cpu'\n",
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"\n",
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"BATCH_SIZE = 128\n",
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"if device == 'cuda':\n",
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" pynvml.nvmlInit()\n",
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" log_gpu_memory()\n",
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" total_gpu_mem = get_gpu_memory_total()\n",
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" HAS_T4 = False\n",
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" if total_gpu_mem > 12300:\n",
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" HAS_T4 = True\n",
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" logging.info('Running on a T4 GPU or other with > 12GB memory, setting batch size to {}'.format(BATCH_SIZE))\n",
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" else:\n",
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" BATCH_SIZE = 64\n",
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" logging.info('Running on a K80 GPU or other with < 12GB memory, batch size set to {}'.format(BATCH_SIZE))\n",
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"\n",
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" "
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],
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"execution_count": 3,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"GPU memory free: 15069, memory used: 11\n",
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"Running on a T4 GPU or other with > 12GB memory, setting batch size to 128\n"
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],
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"name": "stderr"
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},
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{
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"output_type": "stream",
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"text": [
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"PyTorch version: 1.1.0\n",
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"CUDA available\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "aZPg2V_Ft6mk",
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"colab_type": "text"
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},
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"source": [
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"# The Dataset\n",
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"\n",
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"ImageNet-V2 (https://github.com/modestyachts/ImageNetV2) is a useful collection of 3 ImageNet-like validation sets that have been collected more recently, 10 years after the original ImageNet.\n",
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"\n",
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"Aside from being conveniently smaller and easier to deploy in a notebook, it's a useful test set to compare how models might generalize beyond the original ImageNet-1k data. We're going to use the 'Matched Frequency' version of the dataset. You can read more about the dataset in the paper by its creators (Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar): [\"Do ImageNet Classifiers Generalize to ImageNet?\"](http://people.csail.mit.edu/ludwigs/papers/imagenet.pdf)"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "rdHLlfERO6d_",
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"colab_type": "code",
|
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"colab": {}
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},
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"source": [
|
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|
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"# Download and extract the dataset (note it's not actually a gz like the file says)\n",
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"if not os.path.exists('./imagenetv2-matched-frequency'):\n",
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" !curl -s https://s3-us-west-2.amazonaws.com/imagenetv2public/imagenetv2-matched-frequency.tar.gz | tar x\n",
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"dataset = Dataset('./imagenetv2-matched-frequency/')\n",
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"assert len(dataset) == 10000"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "EH13ZwRhqDAi",
|
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"colab_type": "text"
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},
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"source": [
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"Let's take a look at some random images in the dataset...\n"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "QhmlwJO7VxlC",
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"colab_type": "code",
|
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"colab": {
|
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"outputId": "93522b51-0dae-41a2-96ab-b25a282ff543"
|
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},
|
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"source": [
|
||
|
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"from torchvision.utils import make_grid\n",
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"import torchvision.transforms as transforms\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"def show_img(ax, img):\n",
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" npimg = img.numpy()\n",
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" ax.imshow(np.transpose(npimg, (1,2,0)), interpolation='bicubic')\n",
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"\n",
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"fig = plt.figure(figsize=(8, 16), dpi=100)\n",
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"ax = fig.add_subplot('111')\n",
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"num_images = 4*8\n",
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"images = []\n",
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"dataset.transform = transforms.Compose([\n",
|
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|
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" transforms.Resize(320),\n",
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|
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" transforms.CenterCrop(320),\n",
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" transforms.ToTensor()])\n",
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"for i in np.random.permutation(np.arange(len(dataset)))[:num_images]:\n",
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" images.append(dataset[i][0])\n",
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" \n",
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"grid_img = make_grid(images, nrow=4, padding=10, normalize=True, scale_each=True)\n",
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"show_img(ax, grid_img) \n",
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"\n"
|
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],
|
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"execution_count": 5,
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||
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"outputs": [
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||
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{
|
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"output_type": "display_data",
|
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"data": {
|
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|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAqIAAAToCAYAAADAEqLDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD2SV/T6TU6YUa
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 800x1600 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"tags": []
|
||
|
|
}
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "j7DIVWU2Pcoz",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# a basic validation routine and runner that configures each model and loader\n",
|
||
|
|
"from timm.models import TestTimePoolHead\n",
|
||
|
|
"\n",
|
||
|
|
"def validate(model, loader, criterion=None, device='cuda'):\n",
|
||
|
|
" # metrics\n",
|
||
|
|
" batch_time = timm.utils.AverageMeter()\n",
|
||
|
|
" losses = AverageMeter()\n",
|
||
|
|
" top1 = AverageMeter()\n",
|
||
|
|
" top5 = AverageMeter()\n",
|
||
|
|
" \n",
|
||
|
|
" # for collecting per sample prediction/loss details\n",
|
||
|
|
" losses_val = []\n",
|
||
|
|
" top5_idx = []\n",
|
||
|
|
" top5_val = []\n",
|
||
|
|
" \n",
|
||
|
|
" end = time.time()\n",
|
||
|
|
" with torch.no_grad():\n",
|
||
|
|
" for i, (input, target) in enumerate(loader):\n",
|
||
|
|
" target = target.to(device)\n",
|
||
|
|
" input = input.to(device)\n",
|
||
|
|
" output = model(input)\n",
|
||
|
|
" \n",
|
||
|
|
" if criterion is not None:\n",
|
||
|
|
" loss = criterion(output, target)\n",
|
||
|
|
" if not loss.size():\n",
|
||
|
|
" losses.update(loss.item(), input.size(0))\n",
|
||
|
|
" else:\n",
|
||
|
|
" # only bother collecting top5 we're also collecting per-example loss\n",
|
||
|
|
" output = output.softmax(1)\n",
|
||
|
|
" top5v, top5i = output.topk(5, 1, True, True)\n",
|
||
|
|
" top5_val.append(top5v.cpu().numpy())\n",
|
||
|
|
" top5_idx.append(top5i.cpu().numpy())\n",
|
||
|
|
" losses_val.append(loss.cpu().numpy())\n",
|
||
|
|
" losses.update(loss.mean().item(), input.size(0))\n",
|
||
|
|
" \n",
|
||
|
|
" prec1, prec5 = timm.utils.accuracy(output, target, topk=(1, 5))\n",
|
||
|
|
" top1.update(prec1.item(), input.size(0))\n",
|
||
|
|
" top5.update(prec5.item(), input.size(0))\n",
|
||
|
|
"\n",
|
||
|
|
" batch_time.update(time.time() - end)\n",
|
||
|
|
" end = time.time()\n",
|
||
|
|
"\n",
|
||
|
|
" if i % 20 == 0:\n",
|
||
|
|
" print('Test: [{0}/{1}]\\t'\n",
|
||
|
|
" 'Time {batch_time.val:.3f} ({batch_time.avg:.3f}, {rate_avg:.3f}/s) \\t'\n",
|
||
|
|
" 'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\\t'\n",
|
||
|
|
" 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(\n",
|
||
|
|
" i, len(loader), batch_time=batch_time,\n",
|
||
|
|
" rate_avg=input.size(0) / batch_time.avg,\n",
|
||
|
|
" top1=top1, top5=top5))\n",
|
||
|
|
"\n",
|
||
|
|
" results = OrderedDict(\n",
|
||
|
|
" top1=top1.avg, top1_err=100 - top1.avg,\n",
|
||
|
|
" top5=top5.avg, top5_err=100 - top5.avg,\n",
|
||
|
|
" )\n",
|
||
|
|
" if criterion is not None:\n",
|
||
|
|
" results['loss'] = losses.avg\n",
|
||
|
|
" if len(top5_idx):\n",
|
||
|
|
" results['top5_val'] = np.concatenate(top5_val, axis=0)\n",
|
||
|
|
" results['top5_idx'] = np.concatenate(top5_idx, axis=0)\n",
|
||
|
|
" if len(losses_val):\n",
|
||
|
|
" results['losses_val'] = np.concatenate(losses_val, axis=0)\n",
|
||
|
|
" print(' * Prec@1 {:.3f} ({:.3f}) Prec@5 {:.3f} ({:.3f})'.format(\n",
|
||
|
|
" results['top1'], results['top1_err'], results['top5'], results['top5_err']))\n",
|
||
|
|
" return results\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"def runner(model_args, dataset, device='cuda', collect_loss=False):\n",
|
||
|
|
" model_name = model_args['model']\n",
|
||
|
|
" model = timm.create_model(model_name, pretrained=True)\n",
|
||
|
|
" ttp = False\n",
|
||
|
|
" if 'ttp' in model_args and model_args['ttp']:\n",
|
||
|
|
" ttp = True\n",
|
||
|
|
" logging.info('Applying test time pooling to model')\n",
|
||
|
|
" model = TestTimePoolHead(model, original_pool=model.default_cfg['pool_size'])\n",
|
||
|
|
" model = model.to(device)\n",
|
||
|
|
" model.eval()\n",
|
||
|
|
" if HAS_T4:\n",
|
||
|
|
" model = model.half()\n",
|
||
|
|
"\n",
|
||
|
|
" data_config = timm.data.resolve_data_config(model_args, model=model, verbose=True)\n",
|
||
|
|
" \n",
|
||
|
|
" loader = timm.data.create_loader(\n",
|
||
|
|
" dataset,\n",
|
||
|
|
" input_size=data_config['input_size'],\n",
|
||
|
|
" batch_size=BATCH_SIZE,\n",
|
||
|
|
" use_prefetcher=True,\n",
|
||
|
|
" interpolation='bicubic',\n",
|
||
|
|
" mean=data_config['mean'],\n",
|
||
|
|
" std=data_config['std'],\n",
|
||
|
|
" fp16=HAS_T4,\n",
|
||
|
|
" crop_pct=1.0 if ttp else data_config['crop_pct'],\n",
|
||
|
|
" num_workers=2)\n",
|
||
|
|
"\n",
|
||
|
|
" criterion = None\n",
|
||
|
|
" if collect_loss:\n",
|
||
|
|
" criterion = torch.nn.CrossEntropyLoss(reduction='none').to(device)\n",
|
||
|
|
" results = validate(model, loader, criterion, device)\n",
|
||
|
|
" \n",
|
||
|
|
" # cleanup checkpoint cache to avoid running out of disk space\n",
|
||
|
|
" shutil.rmtree(os.path.join(os.environ['HOME'], '.cache', 'torch', 'checkpoints'), True)\n",
|
||
|
|
" \n",
|
||
|
|
" # add some non-metric values for charting / comparisons\n",
|
||
|
|
" results['model'] = model_name\n",
|
||
|
|
" results['img_size'] = data_config['input_size'][-1]\n",
|
||
|
|
"\n",
|
||
|
|
" # create key to identify model in charts\n",
|
||
|
|
" key = [model_name, str(data_config['input_size'][-1])]\n",
|
||
|
|
" if ttp:\n",
|
||
|
|
" key += ['ttp']\n",
|
||
|
|
" key = '-'.join(key)\n",
|
||
|
|
" return key, results\n"
|
||
|
|
],
|
||
|
|
"execution_count": 0,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "wyOyVOehQqXL",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 1000
|
||
|
|
},
|
||
|
|
"outputId": "1d928a20-8838-4a81-b8b9-d213de5ea435"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"models = [\n",
|
||
|
|
" dict(model='mobilenetv3_100'),\n",
|
||
|
|
" dict(model='dpn68b'),\n",
|
||
|
|
" dict(model='gluon_resnet50_v1d'),\n",
|
||
|
|
" dict(model='efficientnet_b2'),\n",
|
||
|
|
" dict(model='gluon_seresnext50_32x4d'),\n",
|
||
|
|
" dict(model='dpn92'),\n",
|
||
|
|
" dict(model='gluon_seresnext101_32x4d'),\n",
|
||
|
|
" dict(model='inception_resnet_v2'),\n",
|
||
|
|
" dict(model='pnasnet5large'),\n",
|
||
|
|
" dict(model='tf_efficientnet_b5'),\n",
|
||
|
|
" dict(model='ig_resnext101_32x8d'),\n",
|
||
|
|
" dict(model='ig_resnext101_32x16d'),\n",
|
||
|
|
" dict(model='ig_resnext101_32x32d'),\n",
|
||
|
|
" dict(model='ig_resnext101_32x48d'),\n",
|
||
|
|
"]\n",
|
||
|
|
"\n",
|
||
|
|
"results = OrderedDict()\n",
|
||
|
|
"for ma in models:\n",
|
||
|
|
" mk, mr = runner(ma, dataset, device)\n",
|
||
|
|
" results[mk] = mr\n",
|
||
|
|
"\n",
|
||
|
|
"results_df = pd.DataFrame.from_dict(results, orient='index')\n",
|
||
|
|
"results_df.to_csv('./cached-results.csv')"
|
||
|
|
],
|
||
|
|
"execution_count": 7,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth\" to /root/.cache/torch/checkpoints/mobilenetv3_100-35495452.pth\n",
|
||
|
|
"100%|██████████| 22064048/22064048 [00:00<00:00, 51730902.08it/s]\n",
|
||
|
|
"Data processing configuration for current model + dataset:\n",
|
||
|
|
"\tinput_size: (3, 224, 224)\n",
|
||
|
|
"\tinterpolation: bicubic\n",
|
||
|
|
"\tmean: (0.485, 0.456, 0.406)\n",
|
||
|
|
"\tstd: (0.229, 0.224, 0.225)\n",
|
||
|
|
"\tcrop_pct: 0.875\n"
|
||
|
|
],
|
||
|
|
"name": "stderr"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Test: [0/79]\tTime 2.193 (2.193, 58.355/s) \tPrec@1 71.094 (71.094)\tPrec@5 91.406 (91.406)\n",
|
||
|
|
"Test: [20/79]\tTime 0.086 (0.750, 170.707/s) \tPrec@1 59.375 (67.857)\tPrec@5 83.594 (87.537)\n",
|
||
|
|
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"Downloading: \"https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn68b_extra-84854c156.pth\" to /root/.cache/torch/checkpoints/dpn68b_extra-84854c156.pth\n",
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"Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2-cf78dc4d.pth\" to /root/.cache/torch/checkpoints/efficientnet_b2-cf78dc4d.pth\n",
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"Downloading: \"https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext50_32x4d-90cf2d6e.pth\" to /root/.cache/torch/checkpoints/gluon_seresnext50_32x4d-90cf2d6e.pth\n",
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"Downloading: \"https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn92_extra-b040e4a9b.pth\" to /root/.cache/torch/checkpoints/dpn92_extra-b040e4a9b.pth\n",
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"\tmean: (0.48627450980392156, 0.4588235294117647, 0.40784313725490196)\n",
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"\tstd: (0.23482446870963955, 0.23482446870963955, 0.23482446870963955)\n",
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"text": [
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"Downloading: \"https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_32x4d-cf52900d.pth\" to /root/.cache/torch/checkpoints/gluon_seresnext101_32x4d-cf52900d.pth\n",
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"\tmean: (0.485, 0.456, 0.406)\n",
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"Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/inception_resnet_v2-940b1cd6.pth\" to /root/.cache/torch/checkpoints/inception_resnet_v2-940b1cd6.pth\n",
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"Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/pnasnet5large-bf079911.pth\" to /root/.cache/torch/checkpoints/pnasnet5large-bf079911.pth\n",
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"Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5-c6949ce9.pth\" to /root/.cache/torch/checkpoints/tf_efficientnet_b5-c6949ce9.pth\n",
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"Downloading: \"https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth\" to /root/.cache/torch/checkpoints/ig_resnext101_32x8-c38310e5.pth\n",
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"\tstd: (0.229, 0.224, 0.225)\n",
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"\tcrop_pct: 0.875\n"
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"name": "stderr"
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"output_type": "stream",
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"text": [
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"Test: [0/79]\tTime 5.765 (5.765, 22.204/s) \tPrec@1 80.469 (80.469)\tPrec@5 96.875 (96.875)\n",
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"Test: [20/79]\tTime 0.855 (1.085, 117.918/s) \tPrec@1 75.781 (78.832)\tPrec@5 93.750 (94.271)\n",
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"Test: [40/79]\tTime 0.853 (0.995, 128.593/s) \tPrec@1 66.406 (77.896)\tPrec@5 88.281 (93.807)\n",
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"Test: [60/79]\tTime 0.833 (0.961, 133.263/s) \tPrec@1 74.219 (75.000)\tPrec@5 90.625 (92.623)\n",
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" * Prec@1 73.780 (26.220) Prec@5 92.260 (7.740)\n"
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],
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"name": "stdout"
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},
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{
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"output_type": "stream",
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"text": [
|
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"Downloading: \"https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth\" to /root/.cache/torch/checkpoints/ig_resnext101_32x16-c6f796b0.pth\n",
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"100%|██████████| 777518664/777518664 [00:15<00:00, 50031408.07it/s]\n",
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"Data processing configuration for current model + dataset:\n",
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"\tinput_size: (3, 224, 224)\n",
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"\tinterpolation: bilinear\n",
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"\tmean: (0.485, 0.456, 0.406)\n",
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"\tstd: (0.229, 0.224, 0.225)\n",
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"\tcrop_pct: 0.875\n"
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"name": "stderr"
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"output_type": "stream",
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"text": [
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"Test: [0/79]\tTime 11.569 (11.569, 11.064/s) \tPrec@1 84.375 (84.375)\tPrec@5 99.219 (99.219)\n",
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"Test: [20/79]\tTime 1.649 (2.129, 60.119/s) \tPrec@1 76.562 (80.990)\tPrec@5 95.312 (95.164)\n",
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"Test: [40/79]\tTime 1.620 (1.884, 67.948/s) \tPrec@1 67.969 (79.630)\tPrec@5 89.844 (94.722)\n",
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"Test: [60/79]\tTime 1.637 (1.798, 71.191/s) \tPrec@1 75.781 (77.267)\tPrec@5 90.625 (93.545)\n",
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" * Prec@1 76.020 (23.980) Prec@5 93.070 (6.930)\n"
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],
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"name": "stdout"
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},
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{
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"output_type": "stream",
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"text": [
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"Downloading: \"https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth\" to /root/.cache/torch/checkpoints/ig_resnext101_32x32-e4b90b00.pth\n",
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"100%|██████████| 1876573776/1876573776 [01:36<00:00, 19485230.33it/s]\n",
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"Data processing configuration for current model + dataset:\n",
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"\tinput_size: (3, 224, 224)\n",
|
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"\tinterpolation: bilinear\n",
|
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"\tmean: (0.485, 0.456, 0.406)\n",
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"\tstd: (0.229, 0.224, 0.225)\n",
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"\tcrop_pct: 0.875\n"
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"name": "stderr"
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"output_type": "stream",
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"text": [
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"Test: [0/79]\tTime 19.815 (19.815, 6.460/s) \tPrec@1 86.719 (86.719)\tPrec@5 99.219 (99.219)\n",
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"Test: [20/79]\tTime 3.245 (3.981, 32.154/s) \tPrec@1 77.344 (81.436)\tPrec@5 94.531 (95.238)\n",
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"Test: [60/79]\tTime 3.437 (3.592, 35.638/s) \tPrec@1 79.688 (78.279)\tPrec@5 91.406 (93.699)\n",
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" * Prec@1 77.020 (22.980) Prec@5 93.370 (6.630)\n"
|
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],
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"name": "stdout"
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{
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"output_type": "stream",
|
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"text": [
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"Downloading: \"https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth\" to /root/.cache/torch/checkpoints/ig_resnext101_32x48-3e41cc8a.pth\n",
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"100%|██████████| 3317136976/3317136976 [01:15<00:00, 43847655.19it/s]\n",
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"Data processing configuration for current model + dataset:\n",
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|
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"\tinput_size: (3, 224, 224)\n",
|
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"\tinterpolation: bilinear\n",
|
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"\tmean: (0.485, 0.456, 0.406)\n",
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"\tstd: (0.229, 0.224, 0.225)\n",
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"\tcrop_pct: 0.875\n"
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"text": [
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"Test: [0/79]\tTime 34.840 (34.840, 3.674/s) \tPrec@1 88.281 (88.281)\tPrec@5 100.000 (100.000)\n",
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"Test: [40/79]\tTime 5.890 (6.465, 19.800/s) \tPrec@1 67.969 (80.736)\tPrec@5 89.062 (94.989)\n",
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"Test: [60/79]\tTime 5.872 (6.274, 20.401/s) \tPrec@1 75.000 (78.548)\tPrec@5 92.188 (93.942)\n",
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" * Prec@1 77.280 (22.720) Prec@5 93.610 (6.390)\n"
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"name": "stdout"
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}
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},
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{
|
||
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|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "xA2_6Jl9PlU5",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"import numpy as np\n",
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"plt.rcParams['figure.figsize'] = [16, 10]\n",
|
||
|
|
"\n",
|
||
|
|
"names_all = list(results.keys())\n",
|
||
|
|
"top1_all = np.array([results[m]['top1'] for m in names_all])\n",
|
||
|
|
"top1_sort_ix = np.argsort(top1_all)\n",
|
||
|
|
"top1_sorted = top1_all[top1_sort_ix]\n",
|
||
|
|
"top1_names_sorted = np.array(names_all)[top1_sort_ix]\n",
|
||
|
|
"\n",
|
||
|
|
"top5_all = np.array([results[m]['top5'] for m in names_all])\n",
|
||
|
|
"top5_sort_ix = np.argsort(top5_all)\n",
|
||
|
|
"top5_sorted = top5_all[top5_sort_ix]\n",
|
||
|
|
"top5_names_sorted = np.array(names_all)[top5_sort_ix]"
|
||
|
|
],
|
||
|
|
"execution_count": 0,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"id": "elJkzmkHo2UT",
|
||
|
|
"colab_type": "text"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# Results\n",
|
||
|
|
"\n",
|
||
|
|
"We'll walk through the results in a few charts and text dumps...\n",
|
||
|
|
"\n",
|
||
|
|
"1. Top-1 Accuracy by Model\n",
|
||
|
|
"2. Top-1 Accuracy Difference Between ImageNet-1k and ImageNet-V2\n",
|
||
|
|
"3. Top-5 Accuracy Difference Between ImageNet-1k and ImageNet-V2\n",
|
||
|
|
"4. A Text Comparison of Absolute and Relative Differences"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"id": "7iKh048WpWmh",
|
||
|
|
"colab_type": "text"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# Top-1 Accuracy by Model\n",
|
||
|
|
"\n",
|
||
|
|
"The Instagram pretrained ResNeXts push past the mid 70s Top-1 which is great for this test set. If you're familiar with normal ImageNet-1k validation scores, you'll notice they are all quite a bit lower, we'll analyse the differences in the next two charts."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "EoyBP9GIpV7P",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 876
|
||
|
|
},
|
||
|
|
"outputId": "cb8c7272-9d65-416a-c20f-38110637895c"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"fig = plt.figure()\n",
|
||
|
|
"ax1 = fig.add_subplot(111)\n",
|
||
|
|
"ax1.barh(top1_names_sorted, top1_sorted, color='lightcoral')\n",
|
||
|
|
"\n",
|
||
|
|
"ax1.set_title('Top-1 by Model')\n",
|
||
|
|
"ax1.set_xlabel('Top-1 Accuracy (%)')\n",
|
||
|
|
"ax1.set_yticklabels(top1_names_sorted)\n",
|
||
|
|
"ax1.autoscale(True, axis='both')\n",
|
||
|
|
"\n",
|
||
|
|
"acc_min = top1_sorted[0]\n",
|
||
|
|
"acc_max = top1_sorted[-1]\n",
|
||
|
|
"plt.xlim([math.ceil(acc_min - .3*(acc_max - acc_min)), math.ceil(acc_max)])\n",
|
||
|
|
"\n",
|
||
|
|
"plt.vlines(plt.xticks()[0], *plt.ylim(), color='0.5', alpha=0.2, linestyle='--')\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"print('Results by top-1 accuracy:')\n",
|
||
|
|
"results_by_top1 = list(sorted(results.keys(), key=lambda x: results[x]['top1'], reverse=True))\n",
|
||
|
|
"for m in results_by_top1:\n",
|
||
|
|
" print(' Model: {:30} Top-1 {:4.2f}, Top-5 {:4.2f}'.format(m, results[m]['top1'], results[m]['top5']))"
|
||
|
|
],
|
||
|
|
"execution_count": 9,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABDgAAAJcCAYAAAAcpRT5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0z0L+Ej7+SPAs
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1152x720 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"tags": []
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Results by top-1 accuracy:\n",
|
||
|
|
" Model: ig_resnext101_32x48d-224 Top-1 77.28, Top-5 93.61\n",
|
||
|
|
" Model: ig_resnext101_32x32d-224 Top-1 77.02, Top-5 93.37\n",
|
||
|
|
" Model: ig_resnext101_32x16d-224 Top-1 76.02, Top-5 93.07\n",
|
||
|
|
" Model: ig_resnext101_32x8d-224 Top-1 73.78, Top-5 92.26\n",
|
||
|
|
" Model: tf_efficientnet_b5-456 Top-1 72.55, Top-5 91.10\n",
|
||
|
|
" Model: pnasnet5large-331 Top-1 72.41, Top-5 90.25\n",
|
||
|
|
" Model: inception_resnet_v2-299 Top-1 70.10, Top-5 88.70\n",
|
||
|
|
" Model: gluon_seresnext101_32x4d-224 Top-1 70.01, Top-5 88.92\n",
|
||
|
|
" Model: gluon_seresnext50_32x4d-224 Top-1 68.62, Top-5 88.34\n",
|
||
|
|
" Model: dpn92-224 Top-1 67.96, Top-5 87.51\n",
|
||
|
|
" Model: gluon_resnet50_v1d-224 Top-1 67.92, Top-5 87.14\n",
|
||
|
|
" Model: efficientnet_b2-260 Top-1 67.78, Top-5 88.21\n",
|
||
|
|
" Model: dpn68b-224 Top-1 65.65, Top-5 85.93\n",
|
||
|
|
" Model: mobilenetv3_100-224 Top-1 63.22, Top-5 84.50\n"
|
||
|
|
],
|
||
|
|
"name": "stdout"
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "NT8YgacDQ_Bb",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"!wget -q https://raw.githubusercontent.com/rwightman/pytorch-image-models/master/results/results-all.csv\n",
|
||
|
|
"original_df = pd.read_csv('./results-all.csv', index_col=0)\n",
|
||
|
|
"original_results = original_df.to_dict(orient='index')"
|
||
|
|
],
|
||
|
|
"execution_count": 0,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "vXpJrzVnRAc4",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# helper methods for dumbbell plot\n",
|
||
|
|
"import matplotlib.lines as mlines\n",
|
||
|
|
"\n",
|
||
|
|
"def label_line_horiz(ax, line, label, color='0.5', fs=14, halign='center'):\n",
|
||
|
|
" xdata, ydata = line.get_data()\n",
|
||
|
|
" x1, x2 = xdata\n",
|
||
|
|
" xx = 0.5 * (x1 + x2)\n",
|
||
|
|
" text = ax.annotate(\n",
|
||
|
|
" label, xy=(xx, ydata[0]), xytext=(0, 1), textcoords='offset points',\n",
|
||
|
|
" size=fs, color=color, zorder=3,\n",
|
||
|
|
" bbox=dict(boxstyle=\"round\", fc=\"w\", color='0.5'),\n",
|
||
|
|
" horizontalalignment='center',\n",
|
||
|
|
" verticalalignment='center')\n",
|
||
|
|
" return text\n",
|
||
|
|
"\n",
|
||
|
|
"def draw_line_horiz(ax, p1, p2, label, color='black'):\n",
|
||
|
|
" l = mlines.Line2D(*zip(p1, p2), color=color, zorder=0)\n",
|
||
|
|
" ax.add_line(l)\n",
|
||
|
|
" label_line(ax, l, label)\n",
|
||
|
|
" return l\n",
|
||
|
|
"\n",
|
||
|
|
"def label_line_vert(ax, line, label, color='0.5', fs=14, halign='center'):\n",
|
||
|
|
" xdata, ydata = line.get_data()\n",
|
||
|
|
" y1, y2 = ydata\n",
|
||
|
|
" yy = 0.5 * (y1 + y2)\n",
|
||
|
|
" text = ax.annotate(\n",
|
||
|
|
" label, xy=(xdata[0], yy), xytext=(0, 0), textcoords='offset points',\n",
|
||
|
|
" size=fs, color=color, zorder=3,\n",
|
||
|
|
" bbox=dict(boxstyle=\"round\", fc=\"w\", color='0.5'),\n",
|
||
|
|
" horizontalalignment='center',\n",
|
||
|
|
" verticalalignment='center')\n",
|
||
|
|
" return text\n",
|
||
|
|
"\n",
|
||
|
|
"def draw_line_vert(ax, p1, p2, label, color='black'):\n",
|
||
|
|
" l = mlines.Line2D(*zip(p1, p2), color=color, zorder=0)\n",
|
||
|
|
" ax.add_line(l)\n",
|
||
|
|
" label_line_vert(ax, l, label)\n",
|
||
|
|
" return l\n"
|
||
|
|
],
|
||
|
|
"execution_count": 0,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"id": "tkADTg3_plVy",
|
||
|
|
"colab_type": "text"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# Top-1 Accuracy Difference Between ImageNet-1k and ImageNet-V2\n",
|
||
|
|
"\n",
|
||
|
|
"And here we are, the focal point. How does each model's ImageNet-V2 accuracy compare with its original ImageNet-1k score? \n",
|
||
|
|
"\n",
|
||
|
|
"The general trend -- with increased model capacity scores on both sets increase and the gap (generally) narrows. This matches results of the [original paper for the ImageNet-V2](https://arxiv.org/abs/1902.10811). \n",
|
||
|
|
"\n",
|
||
|
|
"Most noteably though, the WSL Instagram ResNeXt101 models are the only with performance gaps less than 10%. I've tested quite a few models on this dataset, more than in this notebook. This is the first time I've run into any models with absolute performance this high and performance gaps this low. Impressive. I hope to explore what this means for transfer learning and adaptation of these models to other tasks."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "PXuGz5uEuNJc",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 1000
|
||
|
|
},
|
||
|
|
"outputId": "a4b3331d-e501-4bc6-e4d0-c5a9f880b064"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"fig = plt.figure()\n",
|
||
|
|
"ax1 = fig.add_subplot(111)\n",
|
||
|
|
"\n",
|
||
|
|
"# draw the ImageNet-V2 dots, we're sorted on this\n",
|
||
|
|
"ax1.scatter(x=top1_names_sorted, y=top1_sorted, s=64, c='lightcoral',marker=\"o\", label='ImageNet-V2 Matched-Freq')\n",
|
||
|
|
"\n",
|
||
|
|
"# draw the original ImageNet-1k validation dots\n",
|
||
|
|
"orig_top1 = [original_results[results[n]['model']]['top1'] for n in top1_names_sorted]\n",
|
||
|
|
"ax1.scatter(x=top1_names_sorted, y=orig_top1, s=64, c='steelblue', marker=\"o\", label='ImageNet-1K')\n",
|
||
|
|
"\n",
|
||
|
|
"for n, vo, vn in zip(top1_names_sorted, orig_top1, top1_sorted):\n",
|
||
|
|
" draw_line_vert(ax1, (n, vo), (n, vn),\n",
|
||
|
|
" str(round(vo - vn, 2)), 'skyblue')\n",
|
||
|
|
"\n",
|
||
|
|
"ax1.set_title('Top-1 Difference')\n",
|
||
|
|
"ax1.set_ylabel('Top-1 Accuracy (%)')\n",
|
||
|
|
"ax1.set_xlabel('Model')\n",
|
||
|
|
"yl, yh = ax1.get_ylim()\n",
|
||
|
|
"yl = 5 * ((yl + 1) // 5 + 1) \n",
|
||
|
|
"yh = 5 * (yh // 5 + 1)\n",
|
||
|
|
"for y in plt.yticks()[0][1:-1]:\n",
|
||
|
|
" ax1.axhline(y, 0.02, 0.98, c='0.5', alpha=0.2, linestyle='-.')\n",
|
||
|
|
"ax1.set_xticklabels(top1_names_sorted, rotation='-30', ha='left')\n",
|
||
|
|
"ax1.legend(loc='upper left')\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"print('Results by absolute accuracy gap between ImageNet-Sketch and original ImageNet top-1:')\n",
|
||
|
|
"gaps = {x: (results[x]['top1'] - original_results[results[x]['model']]['top1']) for x in results.keys()}\n",
|
||
|
|
"sorted_keys = list(sorted(results.keys(), key=lambda x: gaps[x], reverse=True))\n",
|
||
|
|
"for m in sorted_keys:\n",
|
||
|
|
" print(' Model: {:30} {:4.2f}%'.format(m, gaps[m]))\n",
|
||
|
|
"print()\n",
|
||
|
|
"\n",
|
||
|
|
"print('Results by relative accuracy gap between ImageNet-Sketch and original ImageNet top-1:')\n",
|
||
|
|
"gaps = {x: 100 * (results[x]['top1'] - original_results[results[x]['model']]['top1']) / original_results[results[x]['model']]['top1'] for x in results.keys()}\n",
|
||
|
|
"sorted_keys = list(sorted(results.keys(), key=lambda x: gaps[x], reverse=True))\n",
|
||
|
|
"for m in sorted_keys:\n",
|
||
|
|
" print(' Model: {:30} {:4.2f}%'.format(m, gaps[m]))\n",
|
||
|
|
"print()"
|
||
|
|
],
|
||
|
|
"execution_count": 12,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA/sAAAKsCAYAAACzhK3BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3/7Nmzfz5z//m
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1152x720 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"tags": []
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Results by absolute accuracy gap between ImageNet-Sketch and original ImageNet top-1:\n",
|
||
|
|
" Model: ig_resnext101_32x32d-224 -8.07%\n",
|
||
|
|
" Model: ig_resnext101_32x16d-224 -8.16%\n",
|
||
|
|
" Model: ig_resnext101_32x48d-224 -8.16%\n",
|
||
|
|
" Model: ig_resnext101_32x8d-224 -8.91%\n",
|
||
|
|
" Model: pnasnet5large-331 -10.33%\n",
|
||
|
|
" Model: inception_resnet_v2-299 -10.36%\n",
|
||
|
|
" Model: tf_efficientnet_b5-456 -10.63%\n",
|
||
|
|
" Model: gluon_seresnext101_32x4d-224 -10.89%\n",
|
||
|
|
" Model: gluon_resnet50_v1d-224 -11.15%\n",
|
||
|
|
" Model: gluon_seresnext50_32x4d-224 -11.29%\n",
|
||
|
|
" Model: dpn68b-224 -11.86%\n",
|
||
|
|
" Model: efficientnet_b2-260 -11.97%\n",
|
||
|
|
" Model: dpn92-224 -12.06%\n",
|
||
|
|
" Model: mobilenetv3_100-224 -12.41%\n",
|
||
|
|
"\n",
|
||
|
|
"Results by relative accuracy gap between ImageNet-Sketch and original ImageNet top-1:\n",
|
||
|
|
" Model: ig_resnext101_32x32d-224 -9.49%\n",
|
||
|
|
" Model: ig_resnext101_32x48d-224 -9.55%\n",
|
||
|
|
" Model: ig_resnext101_32x16d-224 -9.69%\n",
|
||
|
|
" Model: ig_resnext101_32x8d-224 -10.77%\n",
|
||
|
|
" Model: pnasnet5large-331 -12.48%\n",
|
||
|
|
" Model: tf_efficientnet_b5-456 -12.78%\n",
|
||
|
|
" Model: inception_resnet_v2-299 -12.88%\n",
|
||
|
|
" Model: gluon_seresnext101_32x4d-224 -13.46%\n",
|
||
|
|
" Model: gluon_resnet50_v1d-224 -14.11%\n",
|
||
|
|
" Model: gluon_seresnext50_32x4d-224 -14.13%\n",
|
||
|
|
" Model: efficientnet_b2-260 -15.01%\n",
|
||
|
|
" Model: dpn92-224 -15.07%\n",
|
||
|
|
" Model: dpn68b-224 -15.31%\n",
|
||
|
|
" Model: mobilenetv3_100-224 -16.41%\n",
|
||
|
|
"\n"
|
||
|
|
],
|
||
|
|
"name": "stdout"
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"id": "qjHwbvYLpwLH",
|
||
|
|
"colab_type": "text"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# Top-5 Accuracy Difference Between ImageNet-1k and ImageNet-V2\n",
|
||
|
|
"\n",
|
||
|
|
"Top-5 differences very similar to the Top-1 above. The same overall trend and the same stand-out performance for the IG ResNeXts."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "pFFXDpl9Jclm",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 1000
|
||
|
|
},
|
||
|
|
"outputId": "01bb350c-b625-434b-9f9c-f6703b28d0ae"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"fig = plt.figure()\n",
|
||
|
|
"ax1 = fig.add_subplot(111)\n",
|
||
|
|
"\n",
|
||
|
|
"# draw the ImageNet-V2 top-5 dots, we're sorted on this\n",
|
||
|
|
"ax1.scatter(x=top5_names_sorted, y=top5_sorted, s=64, c='lightcoral',marker=\"o\", label='ImageNet-V2 Matched-Freq')\n",
|
||
|
|
"\n",
|
||
|
|
"# draw the original ImageNet-1k validation dots\n",
|
||
|
|
"orig_top5 = [original_results[results[n]['model']]['top5'] for n in top5_names_sorted]\n",
|
||
|
|
"ax1.scatter(x=top5_names_sorted, y=orig_top5, s=64, c='steelblue', marker=\"o\", label='ImageNet-1K')\n",
|
||
|
|
"\n",
|
||
|
|
"for n, vo, vn in zip(top5_names_sorted, orig_top5, top5_sorted):\n",
|
||
|
|
" draw_line_vert(ax1, (n, vo), (n, vn),\n",
|
||
|
|
" str(round(vo - vn, 2)), 'skyblue')\n",
|
||
|
|
"\n",
|
||
|
|
"ax1.set_title('Top-5 Difference')\n",
|
||
|
|
"ax1.set_ylabel('Top-5 Accuracy (%)')\n",
|
||
|
|
"ax1.set_xlabel('Model')\n",
|
||
|
|
"yl, yh = ax1.get_ylim()\n",
|
||
|
|
"yl = 5 * ((yl + 1) // 5 + 1) \n",
|
||
|
|
"yh = 5 * (yh // 5 + 1)\n",
|
||
|
|
"for y in plt.yticks()[0][2:-2]:\n",
|
||
|
|
" ax1.axhline(y, 0.02, 0.98, c='0.5', alpha=0.2, linestyle='-.')\n",
|
||
|
|
"ax1.set_xticklabels(top5_names_sorted, rotation='-30', ha='left')\n",
|
||
|
|
"ax1.legend(loc='upper left')\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"print('Results by relative accuracy gap between ImageNet-Sketch and original ImageNet top-5:')\n",
|
||
|
|
"gaps = {x: (results[x]['top5'] - original_results[results[x]['model']]['top5']) for x in results.keys()}\n",
|
||
|
|
"sorted_keys = list(sorted(results.keys(), key=lambda x: gaps[x], reverse=True))\n",
|
||
|
|
"for m in sorted_keys:\n",
|
||
|
|
" print(' Model: {:30} {:4.2f}%'.format(m, gaps[m]))\n",
|
||
|
|
"print()\n",
|
||
|
|
"\n",
|
||
|
|
"print('Results by relative accuracy gap between ImageNet-Sketch and original ImageNet top-5:')\n",
|
||
|
|
"gaps = {x: 100 * (results[x]['top5'] - original_results[results[x]['model']]['top5']) / original_results[results[x]['model']]['top5'] for x in results.keys()}\n",
|
||
|
|
"sorted_keys = list(sorted(results.keys(), key=lambda x: gaps[x], reverse=True))\n",
|
||
|
|
"for m in sorted_keys:\n",
|
||
|
|
" print(' Model: {:30} {:4.2f}%'.format(m, gaps[m]))"
|
||
|
|
],
|
||
|
|
"execution_count": 13,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA/sAAAKsCAYAAACzhK3BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nejWHDh3ixIkTf
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1152x720 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"tags": []
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Results by relative accuracy gap between ImageNet-Sketch and original ImageNet top-5:\n",
|
||
|
|
" Model: ig_resnext101_32x48d-224 -3.96%\n",
|
||
|
|
" Model: ig_resnext101_32x32d-224 -4.07%\n",
|
||
|
|
" Model: ig_resnext101_32x16d-224 -4.13%\n",
|
||
|
|
" Model: ig_resnext101_32x8d-224 -4.37%\n",
|
||
|
|
" Model: tf_efficientnet_b5-456 -5.44%\n",
|
||
|
|
" Model: pnasnet5large-331 -5.79%\n",
|
||
|
|
" Model: gluon_seresnext101_32x4d-224 -6.37%\n",
|
||
|
|
" Model: gluon_seresnext50_32x4d-224 -6.48%\n",
|
||
|
|
" Model: efficientnet_b2-260 -6.50%\n",
|
||
|
|
" Model: inception_resnet_v2-299 -6.61%\n",
|
||
|
|
" Model: dpn92-224 -7.33%\n",
|
||
|
|
" Model: gluon_resnet50_v1d-224 -7.34%\n",
|
||
|
|
" Model: dpn68b-224 -7.89%\n",
|
||
|
|
" Model: mobilenetv3_100-224 -8.21%\n",
|
||
|
|
"\n",
|
||
|
|
"Results by relative accuracy gap between ImageNet-Sketch and original ImageNet top-5:\n",
|
||
|
|
" Model: ig_resnext101_32x48d-224 -4.06%\n",
|
||
|
|
" Model: ig_resnext101_32x32d-224 -4.17%\n",
|
||
|
|
" Model: ig_resnext101_32x16d-224 -4.25%\n",
|
||
|
|
" Model: ig_resnext101_32x8d-224 -4.52%\n",
|
||
|
|
" Model: tf_efficientnet_b5-456 -5.63%\n",
|
||
|
|
" Model: pnasnet5large-331 -6.03%\n",
|
||
|
|
" Model: gluon_seresnext101_32x4d-224 -6.69%\n",
|
||
|
|
" Model: gluon_seresnext50_32x4d-224 -6.83%\n",
|
||
|
|
" Model: efficientnet_b2-260 -6.86%\n",
|
||
|
|
" Model: inception_resnet_v2-299 -6.94%\n",
|
||
|
|
" Model: dpn92-224 -7.73%\n",
|
||
|
|
" Model: gluon_resnet50_v1d-224 -7.76%\n",
|
||
|
|
" Model: dpn68b-224 -8.41%\n",
|
||
|
|
" Model: mobilenetv3_100-224 -8.85%\n"
|
||
|
|
],
|
||
|
|
"name": "stdout"
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"id": "pEHYrp_MJXAn",
|
||
|
|
"colab_type": "text"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# Best and Worst Predictions\n",
|
||
|
|
"We're going to re-run inference on one of our better models -- a ResNext101-32x16 pretrained on Instagram tags. We'll collect per-example losses and top-5 predictions and then display the results."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "2uwWupygI8AM",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# some code to display images in a grid and ground truth vs predictions for specified indices\n",
|
||
|
|
"from torchvision.utils import make_grid\n",
|
||
|
|
"import torchvision.transforms as transforms\n",
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"\n",
|
||
|
|
"def show_img(ax, img):\n",
|
||
|
|
" npimg = img.numpy()\n",
|
||
|
|
" ax.imshow(np.transpose(npimg, (1,2,0)), interpolation='bicubic')\n",
|
||
|
|
" \n",
|
||
|
|
"def show_summary(indices, dataset, nrows):\n",
|
||
|
|
" col_scale = len(indices) // nrows\n",
|
||
|
|
" top5_idx = mr['top5_idx'][indices]\n",
|
||
|
|
" top5_val = mr['top5_val'][indices]\n",
|
||
|
|
"\n",
|
||
|
|
" images = []\n",
|
||
|
|
" labels = []\n",
|
||
|
|
" filenames = []\n",
|
||
|
|
"\n",
|
||
|
|
" dataset.transform = transforms.Compose([\n",
|
||
|
|
" transforms.Resize(320, Image.BICUBIC),\n",
|
||
|
|
" transforms.CenterCrop(320),\n",
|
||
|
|
" transforms.ToTensor()])\n",
|
||
|
|
"\n",
|
||
|
|
" for i in indices:\n",
|
||
|
|
" img, label = dataset[i]\n",
|
||
|
|
" images.append(img)\n",
|
||
|
|
" labels.append(label)\n",
|
||
|
|
" filenames = dataset.filenames(list(indices), basename=True)\n",
|
||
|
|
"\n",
|
||
|
|
" fig = plt.figure(figsize=(10, 10 * col_scale), dpi=100)\n",
|
||
|
|
" ax = fig.add_subplot('111')\n",
|
||
|
|
" grid_best = make_grid(images, nrow=nrows, padding=10, normalize=True, scale_each=True)\n",
|
||
|
|
" show_img(ax, grid_best)\n",
|
||
|
|
" plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
" for i, l in enumerate(labels):\n",
|
||
|
|
" print('{} ground truth = {}'.format(\n",
|
||
|
|
" id_to_synset[i] + '/' + filenames[i], id_to_text[l]))\n",
|
||
|
|
" print('Predicted:')\n",
|
||
|
|
" for pi, pv in zip(top5_idx[i], top5_val[i]):\n",
|
||
|
|
" if pv > 2e-5:\n",
|
||
|
|
" print(' {:.3f} {}'.format(100*pv, id_to_text[pi]))\n",
|
||
|
|
" print()"
|
||
|
|
],
|
||
|
|
"execution_count": 0,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "KoNaIzU9hPSV",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# create mappings of label id to text and synset\n",
|
||
|
|
"!wget -q https://raw.githubusercontent.com/HoldenCaulfieldRye/caffe/master/data/ilsvrc12/synset_words.txt\n",
|
||
|
|
"with open('./synset_words.txt', 'r') as f:\n",
|
||
|
|
" split_lines = [l.strip().split(' ') for l in f.readlines()]\n",
|
||
|
|
" id_to_synset = dict(enumerate([l[0] for l in split_lines]))\n",
|
||
|
|
" id_to_text = dict(enumerate([' '.join(l[1:]) for l in split_lines]))"
|
||
|
|
],
|
||
|
|
"execution_count": 0,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "jNMG0pFcJMbZ",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 238
|
||
|
|
},
|
||
|
|
"outputId": "6fabbf01-5d78-492f-9bd4-5209530652f1"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"BATCH_SIZE=128\n",
|
||
|
|
"mk, mr = runner(dict(model='ig_resnext101_32x32d'), dataset, device, collect_loss=True) "
|
||
|
|
],
|
||
|
|
"execution_count": 16,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Downloading: \"https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth\" to /root/.cache/torch/checkpoints/ig_resnext101_32x32-e4b90b00.pth\n",
|
||
|
|
"100%|██████████| 1876573776/1876573776 [01:41<00:00, 18563785.10it/s]\n",
|
||
|
|
"Data processing configuration for current model + dataset:\n",
|
||
|
|
"\tinput_size: (3, 224, 224)\n",
|
||
|
|
"\tinterpolation: bilinear\n",
|
||
|
|
"\tmean: (0.485, 0.456, 0.406)\n",
|
||
|
|
"\tstd: (0.229, 0.224, 0.225)\n",
|
||
|
|
"\tcrop_pct: 0.875\n"
|
||
|
|
],
|
||
|
|
"name": "stderr"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Test: [0/79]\tTime 4.914 (4.914, 26.048/s) \tPrec@1 86.719 (86.719)\tPrec@5 99.219 (99.219)\n",
|
||
|
|
"Test: [20/79]\tTime 3.224 (3.202, 39.978/s) \tPrec@1 77.344 (81.436)\tPrec@5 94.531 (95.238)\n",
|
||
|
|
"Test: [40/79]\tTime 3.365 (3.246, 39.431/s) \tPrec@1 68.750 (80.526)\tPrec@5 89.844 (94.684)\n",
|
||
|
|
"Test: [60/79]\tTime 3.475 (3.309, 38.680/s) \tPrec@1 79.688 (78.279)\tPrec@5 91.406 (93.686)\n",
|
||
|
|
" * Prec@1 77.020 (22.980) Prec@5 93.340 (6.660)\n"
|
||
|
|
],
|
||
|
|
"name": "stdout"
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"id": "bUcaOI0rJBoG",
|
||
|
|
"colab_type": "text"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# The Best Predictions\n",
|
||
|
|
"Harmonicas and Carbonara"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "DE4UUv6aJD3l",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 1000
|
||
|
|
},
|
||
|
|
"outputId": "c2da7a9c-4937-42ba-a206-57a64483f932"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"nrows = 2\n",
|
||
|
|
"num_images = 10\n",
|
||
|
|
"best_idx = np.argsort(mr['losses_val'])[:num_images]\n",
|
||
|
|
"show_summary(best_idx, dataset, nrows)"
|
||
|
|
],
|
||
|
|
"execution_count": 17,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0sAAAewCAYAAAAbXsu/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVSINrHYkNoeRp
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1000x5000 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"tags": []
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"n01440764/7.jpeg ground truth = burrito\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 burrito\n",
|
||
|
|
"\n",
|
||
|
|
"n01443537/1.jpeg ground truth = carbonara\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 carbonara\n",
|
||
|
|
"\n",
|
||
|
|
"n01484850/2.jpeg ground truth = carbonara\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 carbonara\n",
|
||
|
|
"\n",
|
||
|
|
"n01491361/8.jpeg ground truth = washer, automatic washer, washing machine\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 washer, automatic washer, washing machine\n",
|
||
|
|
"\n",
|
||
|
|
"n01494475/6.jpeg ground truth = rugby ball\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 rugby ball\n",
|
||
|
|
"\n",
|
||
|
|
"n01496331/3.jpeg ground truth = harmonica, mouth organ, harp, mouth harp\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 harmonica, mouth organ, harp, mouth harp\n",
|
||
|
|
"\n",
|
||
|
|
"n01498041/8.jpeg ground truth = frilled lizard, Chlamydosaurus kingi\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 frilled lizard, Chlamydosaurus kingi\n",
|
||
|
|
"\n",
|
||
|
|
"n01514668/4.jpeg ground truth = lens cap, lens cover\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 lens cap, lens cover\n",
|
||
|
|
"\n",
|
||
|
|
"n01514859/0.jpeg ground truth = bobsled, bobsleigh, bob\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 bobsled, bobsleigh, bob\n",
|
||
|
|
"\n",
|
||
|
|
"n01518878/5.jpeg ground truth = harmonica, mouth organ, harp, mouth harp\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 harmonica, mouth organ, harp, mouth harp\n",
|
||
|
|
"\n"
|
||
|
|
],
|
||
|
|
"name": "stdout"
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"id": "VP7bb25UJFIh",
|
||
|
|
"colab_type": "text"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# The Worst Predictions\n",
|
||
|
|
"As usual, the worst predicitions are hard, in most cases due to issues with labelling or really challenging images. But hey, some of them are amusing. Who wouldn't want a pirate guinea pig? Pretty sure that's a marmot, not a beaver..."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "EdMgUWVeJGo0",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 1000
|
||
|
|
},
|
||
|
|
"outputId": "80d4e7ea-c98e-4b26-e55c-f16e4d671ece"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"nrows = 2\n",
|
||
|
|
"num_images = 20\n",
|
||
|
|
"worst_idx = np.argsort(mr['losses_val'])[-num_images:][::-1]\n",
|
||
|
|
"show_summary(worst_idx, dataset, nrows)"
|
||
|
|
],
|
||
|
|
"execution_count": 18,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0sAAA8lCAYAAABlGgIoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmvEkFlXkygxA
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1000x10000 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"tags": []
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"n01440764/2.jpeg ground truth = bulbul\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 mousetrap\n",
|
||
|
|
"\n",
|
||
|
|
"n01443537/6.jpeg ground truth = sarong\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 crutch\n",
|
||
|
|
"\n",
|
||
|
|
"n01484850/4.jpeg ground truth = guinea pig, Cavia cobaya\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 pirate, pirate ship\n",
|
||
|
|
"\n",
|
||
|
|
"n01491361/9.jpeg ground truth = beaver\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 robin, American robin, Turdus migratorius\n",
|
||
|
|
"\n",
|
||
|
|
"n01494475/7.jpeg ground truth = doormat, welcome mat\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 hay\n",
|
||
|
|
"\n",
|
||
|
|
"n01496331/6.jpeg ground truth = marmoset\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 jackfruit, jak, jack\n",
|
||
|
|
"\n",
|
||
|
|
"n01498041/6.jpeg ground truth = goblet\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 hip, rose hip, rosehip\n",
|
||
|
|
"\n",
|
||
|
|
"n01514668/5.jpeg ground truth = handkerchief, hankie, hanky, hankey\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 rocking chair, rocker\n",
|
||
|
|
"\n",
|
||
|
|
"n01514859/8.jpeg ground truth = dial telephone, dial phone\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 sewing machine\n",
|
||
|
|
"\n",
|
||
|
|
"n01518878/7.jpeg ground truth = hot pot, hotpot\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 99.658 corn\n",
|
||
|
|
" 0.332 ear, spike, capitulum\n",
|
||
|
|
" 0.011 cucumber, cuke\n",
|
||
|
|
"\n",
|
||
|
|
"n01530575/3.jpeg ground truth = binder, ring-binder\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 99.463 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin\n",
|
||
|
|
" 0.522 garbage truck, dustcart\n",
|
||
|
|
"\n",
|
||
|
|
"n01531178/8.jpeg ground truth = space bar\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 maze, labyrinth\n",
|
||
|
|
"\n",
|
||
|
|
"n01532829/0.jpeg ground truth = syringe\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 97.461 stethoscope\n",
|
||
|
|
" 2.556 lab coat, laboratory coat\n",
|
||
|
|
"\n",
|
||
|
|
"n01534433/7.jpeg ground truth = cornet, horn, trumpet, trump\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 accordion, piano accordion, squeeze box\n",
|
||
|
|
"\n",
|
||
|
|
"n01537544/5.jpeg ground truth = sarong\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 umbrella\n",
|
||
|
|
"\n",
|
||
|
|
"n01558993/9.jpeg ground truth = sandal\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 park bench\n",
|
||
|
|
" 0.011 sunglass\n",
|
||
|
|
"\n",
|
||
|
|
"n01560419/2.jpeg ground truth = sweatshirt\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 99.902 acoustic guitar\n",
|
||
|
|
" 0.088 pick, plectrum, plectron\n",
|
||
|
|
"\n",
|
||
|
|
"n01580077/5.jpeg ground truth = modem\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 99.756 carton\n",
|
||
|
|
" 0.106 packet\n",
|
||
|
|
" 0.069 envelope\n",
|
||
|
|
" 0.015 binder, ring-binder\n",
|
||
|
|
" 0.014 tray\n",
|
||
|
|
"\n",
|
||
|
|
"n01582220/6.jpeg ground truth = jersey, T-shirt, tee shirt\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 99.902 park bench\n",
|
||
|
|
" 0.010 neck brace\n",
|
||
|
|
" 0.009 gasmask, respirator, gas helmet\n",
|
||
|
|
" 0.005 soccer ball\n",
|
||
|
|
" 0.002 cowboy hat, ten-gallon hat\n",
|
||
|
|
"\n",
|
||
|
|
"n01592084/4.jpeg ground truth = Rottweiler\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 99.951 malinois\n",
|
||
|
|
" 0.054 German shepherd, German shepherd dog, German police dog, alsatian\n",
|
||
|
|
" 0.002 Leonberg\n",
|
||
|
|
"\n"
|
||
|
|
],
|
||
|
|
"name": "stdout"
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"id": "lQlFqynvbFwy",
|
||
|
|
"colab_type": "text"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# The Worst Predictions with Test Time Pooling\n",
|
||
|
|
"\n",
|
||
|
|
"Looking at the worst predictions above, there are a number of examples where the label for the image was for smaller, less obvious objects at the periphery of the scene (ie syringe at very edge of stethoscope, or trumpet being much less prominent than the accordion ). Seeing this I decided to run it again at a higher resolution, with Test Time Pooling enabled and a 100% crop. This results in a little over 1% boost in top-1 and top-5 and yes, those mentioned examples are no longer among the worst."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "U0nw7gjtNjU-",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 1000
|
||
|
|
},
|
||
|
|
"outputId": "c041e96d-8045-4d3b-c240-edd5c3d0976e"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# only doing this one if we're on a T4\n",
|
||
|
|
"if HAS_T4:\n",
|
||
|
|
" mk, mr = runner(dict(model='ig_resnext101_32x32d', img_size=288, ttp=True), dataset, device, collect_loss=True)\n",
|
||
|
|
" nrows = 2\n",
|
||
|
|
" num_images = 20\n",
|
||
|
|
" worst_idx = np.argsort(mr['losses_val'])[-num_images:][::-1]\n",
|
||
|
|
" show_summary(worst_idx, dataset, nrows)"
|
||
|
|
],
|
||
|
|
"execution_count": 19,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Downloading: \"https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth\" to /root/.cache/torch/checkpoints/ig_resnext101_32x32-e4b90b00.pth\n",
|
||
|
|
"100%|██████████| 1876573776/1876573776 [01:35<00:00, 19579360.70it/s]\n",
|
||
|
|
"Applying test time pooling to model\n",
|
||
|
|
"Data processing configuration for current model + dataset:\n",
|
||
|
|
"\tinput_size: (3, 288, 288)\n",
|
||
|
|
"\tinterpolation: bicubic\n",
|
||
|
|
"\tmean: (0.485, 0.456, 0.406)\n",
|
||
|
|
"\tstd: (0.229, 0.224, 0.225)\n",
|
||
|
|
"\tcrop_pct: 0.875\n"
|
||
|
|
],
|
||
|
|
"name": "stderr"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Test: [0/79]\tTime 36.513 (36.513, 3.506/s) \tPrec@1 87.500 (87.500)\tPrec@5 100.000 (100.000)\n",
|
||
|
|
"Test: [20/79]\tTime 5.699 (6.974, 18.353/s) \tPrec@1 79.688 (82.329)\tPrec@5 96.094 (95.945)\n",
|
||
|
|
"Test: [40/79]\tTime 5.764 (6.389, 20.033/s) \tPrec@1 70.312 (81.326)\tPrec@5 90.625 (95.312)\n",
|
||
|
|
"Test: [60/79]\tTime 5.792 (6.186, 20.694/s) \tPrec@1 81.250 (79.188)\tPrec@5 95.312 (94.365)\n",
|
||
|
|
" * Prec@1 78.100 (21.900) Prec@5 94.100 (5.900)\n"
|
||
|
|
],
|
||
|
|
"name": "stdout"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0sAAA8lCAYAAABlGgIoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmvEkFlXkygxA
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1000x10000 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"tags": []
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"n01440764/2.jpeg ground truth = bulbul\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 mousetrap\n",
|
||
|
|
"\n",
|
||
|
|
"n01443537/6.jpeg ground truth = sarong\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 crutch\n",
|
||
|
|
"\n",
|
||
|
|
"n01484850/2.jpeg ground truth = pot, flowerpot\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 Polaroid camera, Polaroid Land camera\n",
|
||
|
|
"\n",
|
||
|
|
"n01491361/7.jpeg ground truth = hot pot, hotpot\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 99.902 corn\n",
|
||
|
|
" 0.100 ear, spike, capitulum\n",
|
||
|
|
"\n",
|
||
|
|
"n01494475/6.jpeg ground truth = jersey, T-shirt, tee shirt\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 park bench\n",
|
||
|
|
"\n",
|
||
|
|
"n01496331/6.jpeg ground truth = goblet\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 hip, rose hip, rosehip\n",
|
||
|
|
"\n",
|
||
|
|
"n01498041/6.jpeg ground truth = marmoset\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 jackfruit, jak, jack\n",
|
||
|
|
"\n",
|
||
|
|
"n01514668/7.jpeg ground truth = custard apple\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 ant, emmet, pismire\n",
|
||
|
|
"\n",
|
||
|
|
"n01514859/7.jpeg ground truth = corn\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 hotdog, hot dog, red hot\n",
|
||
|
|
"\n",
|
||
|
|
"n01518878/5.jpeg ground truth = sarong\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 umbrella\n",
|
||
|
|
"\n",
|
||
|
|
"n01530575/3.jpeg ground truth = wool, woolen, woollen\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 doormat, welcome mat\n",
|
||
|
|
"\n",
|
||
|
|
"n01531178/9.jpeg ground truth = groom, bridegroom\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 sombrero\n",
|
||
|
|
"\n",
|
||
|
|
"n01532829/8.jpeg ground truth = space bar\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 99.951 maze, labyrinth\n",
|
||
|
|
" 0.015 joystick\n",
|
||
|
|
" 0.010 jigsaw puzzle\n",
|
||
|
|
"\n",
|
||
|
|
"n01534433/0.jpeg ground truth = theater curtain, theatre curtain\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 99.951 altar\n",
|
||
|
|
" 0.045 throne\n",
|
||
|
|
" 0.003 monastery\n",
|
||
|
|
" 0.002 church, church building\n",
|
||
|
|
"\n",
|
||
|
|
"n01537544/5.jpeg ground truth = common iguana, iguana, Iguana iguana\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 99.951 fountain\n",
|
||
|
|
" 0.007 triceratops\n",
|
||
|
|
" 0.006 pedestal, plinth, footstall\n",
|
||
|
|
" 0.003 palace\n",
|
||
|
|
"\n",
|
||
|
|
"n01558993/5.jpeg ground truth = modem\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 99.951 carton\n",
|
||
|
|
" 0.050 packet\n",
|
||
|
|
" 0.008 tray\n",
|
||
|
|
" 0.003 crate\n",
|
||
|
|
"\n",
|
||
|
|
"n01560419/4.jpeg ground truth = handkerchief, hankie, hanky, hankey\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 99.951 accordion, piano accordion, squeeze box\n",
|
||
|
|
" 0.017 stage\n",
|
||
|
|
" 0.012 unicycle, monocycle\n",
|
||
|
|
" 0.006 spatula\n",
|
||
|
|
" 0.003 plunger, plumber's helper\n",
|
||
|
|
"\n",
|
||
|
|
"n01580077/6.jpeg ground truth = sunglasses, dark glasses, shades\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 volleyball\n",
|
||
|
|
"\n",
|
||
|
|
"n01582220/5.jpeg ground truth = swing\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 carousel, carrousel, merry-go-round, roundabout, whirligig\n",
|
||
|
|
"\n",
|
||
|
|
"n01592084/9.jpeg ground truth = beaver\n",
|
||
|
|
"Predicted:\n",
|
||
|
|
" 100.000 robin, American robin, Turdus migratorius\n",
|
||
|
|
"\n"
|
||
|
|
],
|
||
|
|
"name": "stdout"
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "uFaD6fW1T_K3",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
""
|
||
|
|
],
|
||
|
|
"execution_count": 0,
|
||
|
|
"outputs": []
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|