Implemented checking with the test set
%% Cell type:markdown id: tags: | ||
# Object detection for Autonomous Systems 1 | ||
This notebook is for experiments with PyTorch and MobileNet. | ||
%% Cell type:markdown id: tags: | ||
## Checking PyTorch works | ||
The following commands are for checking that PyTorch works and if it has access to CUDA (it's okay if it doesn't, computations will just be slower). | ||
%% Cell type:code id: tags: | ||
``` python | ||
from __future__ import print_function | ||
import torch | ||
x = torch.rand(5, 3) | ||
print(x) | ||
``` | ||
%%%% Output: stream | ||
tensor([[0.4026, 0.2347, 0.7283], | ||
[0.9675, 0.2745, 0.0301], | ||
[0.7576, 0.9682, 0.1278], | ||
[0.5820, 0.9611, 0.9176], | ||
[0.0758, 0.9665, 0.9982]]) | ||
%% Cell type:code id: tags: | ||
``` python | ||
import torch | ||
torch.cuda.is_available() | ||
``` | ||
%%%% Output: execute_result | ||
False | ||
%% Cell type:markdown id: tags: | ||
## Loading MobileNet v2 | ||
The commands in the following section are borrowed from [this page](https://pytorch.org/hub/pytorch_vision_mobilenet_v2/). | ||
%% Cell type:code id: tags: | ||
``` python | ||
# Downloading the model from the internet and printing info about it. | ||
model = torch.hub.load('pytorch/vision:v0.5.0', 'mobilenet_v2', pretrained=True) | ||
model.eval() # Sets the module in EVALUATION MODE. | ||
``` | ||
%%%% Output: stream | ||
Using cache found in /home/noric/.cache/torch/hub/pytorch_vision_v0.5.0 | ||
%%%% Output: execute_result | ||
MobileNetV2( | ||
(features): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) | ||
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False) | ||
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(2): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False) | ||
(1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(3): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False) | ||
(1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(4): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False) | ||
(1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(5): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False) | ||
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(6): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False) | ||
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(7): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False) | ||
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(8): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False) | ||
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(9): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False) | ||
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(10): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False) | ||
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(11): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False) | ||
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(12): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False) | ||
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(13): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False) | ||
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(14): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False) | ||
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(15): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False) | ||
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(16): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False) | ||
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(17): InvertedResidual( | ||
(conv): Sequential( | ||
(0): ConvBNReLU( | ||
(0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(1): ConvBNReLU( | ||
(0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False) | ||
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
(2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
) | ||
) | ||
(18): ConvBNReLU( | ||
(0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
(1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | ||
(2): ReLU6(inplace=True) | ||
) | ||
) | ||
(classifier): Sequential( | ||
(0): Dropout(p=0.2, inplace=False) | ||
(1): Linear(in_features=1280, out_features=1000, bias=True) | ||
) | ||
) | ||
%% Cell type:markdown id: tags: | ||
CNNs have two different types of layers: | ||
1. Convolutional layers | ||
2. Fully connected layer | ||
If MobileNet is like that, we can know where to cut our network. Just re-use the convolutional layers. | ||
%% Cell type:markdown id: tags: | ||
This above printed (I think) the structure of the network. | ||
What's particularly interesting is the last layer of the network, called classifier. | ||
Sequential() seems to be just a way to "link" layers together (see [Sequential doc](https://pytorch.org/docs/stable/nn.html?highlight=sequential#torch.nn.Sequential)). The "classifier" is composed of two layers: | ||
1. Dropout layer: which during training zeroes randomly some elements of the input tensor (This is good for the | ||
2. Linear layer: Linear transformation of incoming data | ||
This seems a bit stiff to use fo classifying our pictures (I was hoping for a fully connected layer) but ok. | ||
%% Cell type:code id: tags: | ||
``` python | ||
# Download an example image from the pytorch website | ||
import urllib | ||
url, filename = ("https://github.com/pytorch/hub/raw/master/dog.jpg", "dog.jpg") | ||
try: urllib.URLopener().retrieve(url, filename) | ||
except: urllib.request.urlretrieve(url, filename) | ||
``` | ||
%% Cell type:markdown id: tags: | ||
The command above downloaded a file and put it in `dog.jpg` in the the notebook directory. | ||
The dog is cute. | ||
%% Cell type:code id: tags: | ||
``` python | ||
# sample execution (requires torchvision) | ||
from PIL import Image | ||
from torchvision import transforms | ||
input_image = Image.open(filename) | ||
# I think this resized the picture and does some processing on pixels. | ||
preprocess = transforms.Compose([ | ||
transforms.Resize(256), | ||
transforms.CenterCrop(224), | ||
transforms.ToTensor(), | ||
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), | ||
]) | ||
input_tensor = preprocess(input_image) | ||
input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model | ||
# move the input and model to GPU for speed if available | ||
if torch.cuda.is_available(): | ||
input_batch = input_batch.to('cuda') | ||
model.to('cuda') | ||
with torch.no_grad(): | ||
output = model(input_batch) | ||
# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes | ||
print(output[0]) | ||
# The output has unnormalized scores. To get probabilities, you can run a softmax on it. | ||
print(torch.nn.functional.softmax(output[0], dim=0)) | ||
``` | ||
%%%% Output: stream | ||
tensor([ 2.0977e+00, -1.7348e+00, -2.2355e+00, -2.9669e+00, -2.3805e+00, | ||
9.7397e-01, -1.6049e+00, 3.6914e+00, 6.3812e+00, -1.2929e+00, | ||
-6.7555e+00, -3.3525e+00, -7.9619e+00, -4.4554e+00, -5.6423e+00, | ||
-4.6624e+00, -1.9577e+00, -3.5811e-01, -1.2812e+00, -4.6707e+00, | ||
-3.2935e+00, -2.5674e+00, -2.4351e+00, -1.3017e+00, -3.2453e+00, | ||
-1.4237e+00, -1.2001e+00, 4.1274e-01, -1.6093e+00, 1.5871e+00, | ||
2.7725e-01, -6.2652e-01, -2.9734e-01, -3.8219e+00, -1.5450e+00, | ||
-2.8976e+00, -5.6528e-01, -2.3938e+00, -3.3704e-01, 1.2809e+00, | ||
-1.2516e+00, -2.6469e+00, -3.1011e+00, -2.2447e+00, -4.4385e-01, | ||
-1.2620e+00, 8.2895e-01, -2.0436e+00, -6.6037e-01, -8.6523e-02, | ||
4.8967e-01, -1.7190e+00, -7.7943e-01, -1.1046e+00, -5.3857e-01, | ||
-2.9254e+00, -1.9327e+00, -2.7273e+00, -6.0903e-01, -1.6802e+00, | ||
1.3443e+00, -4.2062e+00, -1.4768e+00, -4.5581e+00, -3.2726e+00, | ||
-4.0086e+00, 1.5702e-01, -1.9921e+00, -7.4553e-01, -4.2230e+00, | ||
-3.8855e+00, -9.4837e-01, -2.1373e+00, -3.5562e+00, -2.4602e+00, | ||
-3.2339e+00, -3.1414e+00, -2.6786e+00, -3.4321e-01, 1.3021e+00, | ||
-1.8081e+00, -6.9590e-01, 6.0993e-01, 1.0629e+00, 7.0008e-01, | ||
-2.1399e+00, -8.3321e-01, -1.2712e+00, -2.9445e+00, 2.6159e+00, | ||
-3.0859e+00, -3.9236e+00, -5.0966e+00, -2.6899e+00, -4.2541e+00, | ||
-5.7496e+00, -1.4612e+00, -3.3379e+00, -5.1146e+00, 1.3199e-01, | ||
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-2.5831e+00, -1.4274e+00, -3.0766e+00, 1.6652e+00, 3.3317e+00]) | ||
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1.7046e-06, 4.6027e-09, 8.2547e-10, 1.0181e-06, 1.2746e-07, 4.5022e-07, | ||
6.2792e-08, 7.0071e-07, 1.3727e-08, 6.5962e-08, 7.4989e-08, 1.8120e-06, | ||
1.1253e-08, 2.4808e-08, 1.7414e-07, 6.9314e-08, 2.4229e-08, 6.0354e-07, | ||
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1.8997e-08, 1.6186e-07, 4.3947e-07, 3.1349e-06, 1.4959e-06, 3.8973e-06, | ||
1.2536e-06, 3.0484e-06, 6.2363e-07, 5.8275e-07, 1.3441e-07, 2.4783e-07, | ||
3.7679e-06, 2.0309e-06, 3.8502e-07, 2.1562e-07, 3.4836e-06, 1.5991e-06, | ||
2.0279e-07, 2.4760e-07, 8.3103e-07, 3.0521e-07, 6.8899e-06, 1.3158e-07, | ||
9.2566e-08, 1.2987e-07, 6.6900e-07, 2.8101e-07, 2.5624e-06, 4.6730e-05, | ||
4.5822e-08, 1.6842e-06, 1.6064e-06, 2.8302e-05, 4.8026e-07, 1.5593e-07, | ||
1.8008e-06, 3.3965e-07, 2.6141e-07, 8.9039e-09, 2.4791e-06, 1.7449e-07, | ||
3.0641e-07, 8.6237e-07, 1.4638e-08, 4.3117e-07, 2.9507e-07, 9.4550e-07, | ||
2.5855e-07, 1.3750e-06, 1.0229e-05, 3.5426e-07, 7.6847e-06, 9.0605e-07, | ||
8.0181e-08, 6.1467e-08, 9.5402e-07, 5.3596e-07, 1.6593e-07, 6.5679e-07, | ||
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9.1085e-09, 2.6142e-06, 5.4483e-07, 5.4164e-08, 1.1281e-07, 9.1327e-07, | ||
4.5163e-07, 4.3125e-07, 3.3360e-08, 7.4191e-08, 4.6817e-07, 1.2071e-07, | ||
2.7035e-07, 3.4775e-08, 8.8645e-06, 1.0556e-06, 1.6237e-06, 2.9757e-06, | ||
1.4359e-07, 1.3167e-06, 4.3485e-06, 2.2305e-05, 7.6890e-08, 8.5960e-07, | ||
8.8747e-09, 3.6452e-07, 5.3267e-06, 1.5137e-07, 1.0537e-06, 6.6923e-05, | ||
7.4396e-08, 4.8483e-05, 7.0330e-09, 6.5001e-07, 2.5980e-08, 3.2111e-07, | ||
3.6964e-07, 1.8244e-08, 5.0944e-07, 1.5400e-07, 3.2367e-06, 1.6521e-08, | ||
1.7036e-05, 2.6989e-07, 3.7400e-07, 6.4892e-06, 7.8334e-07, 1.4516e-05, | ||
3.3903e-06, 3.9301e-05, 1.0771e-05, 3.1425e-06, 4.9378e-06, 8.1967e-08, | ||
4.8106e-05, 3.5479e-07, 6.2127e-06, 3.1483e-07, 1.5537e-06, 2.0423e-05, | ||
1.7494e-08, 9.5895e-06, 5.3306e-06, 2.9762e-06, 9.0725e-07, 2.2622e-08, | ||
1.9300e-06, 6.1189e-07, 7.6442e-07, 2.8748e-07, 2.5639e-07, 4.8020e-08, | ||
4.4060e-07, 7.7503e-07, 3.1007e-07, 7.0331e-07, 7.8625e-08, 9.6247e-08, | ||
3.3385e-07, 2.7131e-07, 2.7978e-06, 1.0515e-06, 4.4754e-07, 4.9199e-07, | ||
2.4201e-08, 2.1902e-06, 6.8253e-08, 5.6619e-08, 1.1455e-06, 1.9107e-06, | ||
6.1120e-05, 1.5901e-06, 4.5963e-07, 2.4231e-07, 4.3908e-08, 4.5700e-06, | ||
2.7126e-08, 6.5075e-07, 4.7211e-07, 9.4213e-08, 8.3145e-07, 1.0685e-06, | ||
3.0158e-07, 1.4197e-05, 6.6103e-07, 3.0038e-07, 7.8213e-07, 5.7222e-08, | ||
1.4809e-07, 8.5884e-06, 3.9222e-08, 9.5035e-07, 7.3888e-07, 4.1611e-07, | ||
3.8056e-07, 6.5726e-06, 4.6336e-06, 1.2941e-07, 1.1257e-07, 4.0485e-07, | ||
4.9622e-07, 1.8005e-05, 1.5965e-06, 2.2188e-06, 1.5157e-06, 1.9226e-06, | ||
1.6532e-06, 2.0562e-08, 3.1499e-06, 8.5155e-07, 6.8987e-07, 2.0769e-05, | ||
2.2714e-07, 2.0677e-06, 1.2234e-06, 2.9070e-06, 1.7912e-06, 6.7512e-07, | ||
5.9088e-06, 5.4768e-07, 6.6581e-08, 7.0871e-08, 4.5414e-06, 2.8613e-06, | ||
1.7365e-07, 1.4766e-06, 2.7468e-07, 1.1376e-06, 1.9202e-07, 2.6962e-06, | ||
1.2406e-04, 1.5485e-07, 1.6923e-07, 1.2458e-06, 1.5717e-07, 7.8793e-09, | ||
1.0798e-07, 4.2017e-08, 5.1216e-08, 6.5024e-07, 5.1441e-07, 4.5326e-07, | ||
3.3495e-07, 9.3595e-09, 3.7403e-07, 1.4191e-06, 6.3541e-07, 5.5097e-07, | ||
9.5614e-06, 2.3034e-06, 1.3032e-07, 1.5851e-08, 3.9380e-09, 4.4827e-07, | ||
9.3790e-06, 1.0875e-07, 1.1909e-07, 1.9340e-05, 4.6176e-06, 6.1346e-07, | ||
2.2519e-05, 2.7272e-07, 2.4799e-08, 7.8123e-08, 3.7809e-07, 2.0109e-09, | ||
8.2828e-08, 4.2335e-07, 2.0325e-08, 9.9324e-08, 2.5617e-07, 2.9431e-07, | ||
7.6843e-08, 1.3858e-06, 2.5660e-06, 3.0705e-06, 2.7318e-05, 1.9467e-06, | ||
1.8253e-08, 3.0549e-07, 1.4173e-06, 3.2822e-07, 5.1450e-06, 4.5799e-07, | ||
4.1337e-07, 2.8971e-06, 1.1367e-06, 9.5385e-07, 9.9672e-07, 9.7991e-07, | ||
1.5353e-05, 6.7839e-07, 1.3549e-07, 9.7582e-08, 4.3272e-07, 3.1815e-09, | ||
1.8463e-07, 3.5902e-07, 2.8727e-07, 5.1396e-08, 2.3341e-08, 4.0379e-07, | ||
7.7843e-08, 2.6144e-08, 2.3666e-09, 3.1901e-07, 1.5370e-07, 2.7065e-05, | ||
2.4361e-06, 2.3417e-07, 4.4093e-06, 1.0163e-08, 3.7309e-07, 1.9445e-06, | ||
3.7269e-06, 2.0246e-06, 2.7814e-07, 1.1297e-06, 1.6989e-06, 4.5535e-08, | ||
8.3805e-07, 6.1257e-07, 5.3641e-08, 1.6760e-08, 4.4251e-08, 8.8049e-08, | ||
6.5732e-06, 2.4231e-06, 3.4349e-07, 2.9437e-07, 1.9981e-08, 3.2105e-07, | ||
1.2023e-07, 3.4457e-07, 1.3067e-07, 6.8178e-07, 9.5957e-06, 1.7218e-07, | ||
2.9186e-07, 9.8741e-07, 1.2198e-07, 5.1106e-08, 9.7978e-09, 6.3165e-08, | ||
1.4853e-06, 3.6861e-09, 6.7289e-07, 2.1981e-06, 1.5383e-06, 3.7863e-06, | ||
8.7545e-07, 8.8764e-09, 2.3495e-07, 9.1230e-06, 8.1576e-08, 2.9458e-06, | ||
3.5886e-07, 1.2731e-07, 7.2442e-07, 4.9291e-07, 4.0808e-07, 3.9548e-08, | ||
3.8268e-07, 2.9060e-07, 2.1999e-07, 3.5320e-06, 3.7342e-07, 3.1346e-08, | ||
9.6835e-07, 8.3035e-08, 2.9030e-07, 5.2149e-10, 7.5826e-09, 3.6561e-08, | ||
1.1612e-07, 2.2320e-08, 2.5588e-06, 1.3546e-05]) | ||
%% Cell type:code id: tags: | ||
``` python | ||
# We load the labels for the classes from a file | ||
import json | ||
class_idx = json.load(open("imagenet_class_index.json", 'r')) | ||
idx2label = [class_idx[str(k)][1] for k in range(len(class_idx))] | ||
``` | ||
%% Cell type:code id: tags: | ||
``` python | ||
idx2label | ||
``` | ||
%%%% Output: execute_result | ||
['tench', | ||
'goldfish', | ||
'great_white_shark', | ||
'tiger_shark', | ||
'hammerhead', | ||
'electric_ray', | ||
'stingray', | ||
'cock', | ||
'hen', | ||
'ostrich', | ||
'brambling', | ||
'goldfinch', | ||
'house_finch', | ||
'junco', | ||
'indigo_bunting', | ||
'robin', | ||
'bulbul', | ||
'jay', | ||
'magpie', | ||
'chickadee', | ||
'water_ouzel', | ||
'kite', | ||
'bald_eagle', | ||
'vulture', | ||
'great_grey_owl', | ||
'European_fire_salamander', | ||
'common_newt', | ||
'eft', | ||
'spotted_salamander', | ||
'axolotl', | ||
'bullfrog', | ||
'tree_frog', | ||
'tailed_frog', | ||
'loggerhead', | ||
'leatherback_turtle', | ||
'mud_turtle', | ||
'terrapin', | ||
'box_turtle', | ||
'banded_gecko', | ||
'common_iguana', | ||
'American_chameleon', | ||
'whiptail', | ||
'agama', | ||
'frilled_lizard', | ||
'alligator_lizard', | ||
'Gila_monster', | ||
'green_lizard', | ||
'African_chameleon', | ||
'Komodo_dragon', | ||
'African_crocodile', | ||
'American_alligator', | ||
'triceratops', | ||
'thunder_snake', | ||
'ringneck_snake', | ||
'hognose_snake', | ||
'green_snake', | ||
'king_snake', | ||
'garter_snake', | ||
'water_snake', | ||
'vine_snake', | ||
'night_snake', | ||
'boa_constrictor', | ||
'rock_python', | ||
'Indian_cobra', | ||
'green_mamba', | ||
'sea_snake', | ||
'horned_viper', | ||
'diamondback', | ||
'sidewinder', | ||
'trilobite', | ||
'harvestman', | ||
'scorpion', | ||
'black_and_gold_garden_spider', | ||
'barn_spider', | ||
'garden_spider', | ||
'black_widow', | ||
'tarantula', | ||
'wolf_spider', | ||
'tick', | ||
'centipede', | ||
'black_grouse', | ||
'ptarmigan', | ||
'ruffed_grouse', | ||
'prairie_chicken', | ||
'peacock', | ||
'quail', | ||
'partridge', | ||
'African_grey', | ||
'macaw', | ||
'sulphur-crested_cockatoo', | ||
'lorikeet', | ||
'coucal', | ||
'bee_eater', | ||
'hornbill', | ||
'hummingbird', | ||
'jacamar', | ||
'toucan', | ||
'drake', | ||
'red-breasted_merganser', | ||
'goose', | ||
'black_swan', | ||
'tusker', | ||
'echidna', | ||
'platypus', | ||
'wallaby', | ||
'koala', | ||
'wombat', | ||
'jellyfish', | ||
'sea_anemone', | ||
'brain_coral', | ||
'flatworm', | ||
'nematode', | ||
'conch', | ||
'snail', | ||
'slug', | ||
'sea_slug', | ||
'chiton', | ||
'chambered_nautilus', | ||
'Dungeness_crab', | ||
'rock_crab', | ||
'fiddler_crab', | ||
'king_crab', | ||
'American_lobster', | ||
'spiny_lobster', | ||
'crayfish', | ||
'hermit_crab', | ||
'isopod', | ||
'white_stork', | ||
'black_stork', | ||
'spoonbill', | ||
'flamingo', | ||
'little_blue_heron', | ||
'American_egret', | ||
'bittern', | ||
'crane', | ||
'limpkin', | ||
'European_gallinule', | ||
'American_coot', | ||
'bustard', | ||
'ruddy_turnstone', | ||
'red-backed_sandpiper', | ||
'redshank', | ||
'dowitcher', | ||
'oystercatcher', | ||
'pelican', | ||
'king_penguin', | ||
'albatross', | ||
'grey_whale', | ||
'killer_whale', | ||
'dugong', | ||
'sea_lion', | ||
'Chihuahua', | ||
'Japanese_spaniel', | ||
'Maltese_dog', | ||
'Pekinese', | ||
'Shih-Tzu', | ||
'Blenheim_spaniel', | ||
'papillon', | ||
'toy_terrier', | ||
'Rhodesian_ridgeback', | ||
'Afghan_hound', | ||
'basset', | ||
'beagle', | ||
'bloodhound', | ||
'bluetick', | ||
'black-and-tan_coonhound', | ||
'Walker_hound', | ||
'English_foxhound', | ||
'redbone', | ||
'borzoi', | ||
'Irish_wolfhound', | ||
'Italian_greyhound', | ||
'whippet', | ||
'Ibizan_hound', | ||
'Norwegian_elkhound', | ||
'otterhound', | ||
'Saluki', | ||
'Scottish_deerhound', | ||
'Weimaraner', | ||
'Staffordshire_bullterrier', | ||
'American_Staffordshire_terrier', | ||
'Bedlington_terrier', | ||
'Border_terrier', | ||
'Kerry_blue_terrier', | ||
'Irish_terrier', | ||
'Norfolk_terrier', | ||
'Norwich_terrier', | ||
'Yorkshire_terrier', | ||
'wire-haired_fox_terrier', | ||
'Lakeland_terrier', | ||
'Sealyham_terrier', | ||
'Airedale', | ||
'cairn', | ||
'Australian_terrier', | ||
'Dandie_Dinmont', | ||
'Boston_bull', | ||
'miniature_schnauzer', | ||
'giant_schnauzer', | ||
'standard_schnauzer', | ||
'Scotch_terrier', | ||
'Tibetan_terrier', | ||
'silky_terrier', | ||
'soft-coated_wheaten_terrier', | ||
'West_Highland_white_terrier', | ||
'Lhasa', | ||
'flat-coated_retriever', | ||
'curly-coated_retriever', | ||
'golden_retriever', | ||
'Labrador_retriever', | ||
'Chesapeake_Bay_retriever', | ||
'German_short-haired_pointer', | ||
'vizsla', | ||
'English_setter', | ||
'Irish_setter', | ||
'Gordon_setter', | ||
'Brittany_spaniel', | ||
'clumber', | ||
'English_springer', | ||
'Welsh_springer_spaniel', | ||
'cocker_spaniel', | ||
'Sussex_spaniel', | ||
'Irish_water_spaniel', | ||
'kuvasz', | ||
'schipperke', | ||
'groenendael', | ||
'malinois', | ||
'briard', | ||
'kelpie', | ||
'komondor', | ||
'Old_English_sheepdog', | ||
'Shetland_sheepdog', | ||
'collie', | ||
'Border_collie', | ||
'Bouvier_des_Flandres', | ||
'Rottweiler', | ||
'German_shepherd', | ||
'Doberman', | ||
'miniature_pinscher', | ||
'Greater_Swiss_Mountain_dog', | ||
'Bernese_mountain_dog', | ||
'Appenzeller', | ||
'EntleBucher', | ||
'boxer', | ||
'bull_mastiff', | ||
'Tibetan_mastiff', | ||
'French_bulldog', | ||
'Great_Dane', | ||
'Saint_Bernard', | ||
'Eskimo_dog', | ||
'malamute', | ||
'Siberian_husky', | ||
'dalmatian', | ||
'affenpinscher', | ||
'basenji', | ||
'pug', | ||
'Leonberg', | ||
'Newfoundland', | ||
'Great_Pyrenees', | ||
'Samoyed', | ||
'Pomeranian', | ||
'chow', | ||
'keeshond', | ||
'Brabancon_griffon', | ||
'Pembroke', | ||
'Cardigan', | ||
'toy_poodle', | ||
'miniature_poodle', | ||
'standard_poodle', | ||
'Mexican_hairless', | ||
'timber_wolf', | ||
'white_wolf', | ||
'red_wolf', | ||
'coyote', | ||
'dingo', | ||
'dhole', | ||
'African_hunting_dog', | ||
'hyena', | ||
'red_fox', | ||
'kit_fox', | ||
'Arctic_fox', | ||
'grey_fox', | ||
'tabby', | ||
'tiger_cat', | ||
'Persian_cat', | ||
'Siamese_cat', | ||
'Egyptian_cat', | ||
'cougar', | ||
'lynx', | ||
'leopard', | ||
'snow_leopard', | ||
'jaguar', | ||
'lion', | ||
'tiger', | ||
'cheetah', | ||
'brown_bear', | ||
'American_black_bear', | ||
'ice_bear', | ||
'sloth_bear', | ||
'mongoose', | ||
'meerkat', | ||
'tiger_beetle', | ||
'ladybug', | ||
'ground_beetle', | ||
'long-horned_beetle', | ||
'leaf_beetle', | ||
'dung_beetle', | ||
'rhinoceros_beetle', | ||
'weevil', | ||
'fly', | ||
'bee', | ||
'ant', | ||
'grasshopper', | ||
'cricket', | ||
'walking_stick', | ||
'cockroach', | ||
'mantis', | ||
'cicada', | ||
'leafhopper', | ||
'lacewing', | ||
'dragonfly', | ||
'damselfly', | ||
'admiral', | ||
'ringlet', | ||
'monarch', | ||
'cabbage_butterfly', | ||
'sulphur_butterfly', | ||
'lycaenid', | ||
'starfish', | ||
'sea_urchin', | ||
'sea_cucumber', | ||
'wood_rabbit', | ||
'hare', | ||
'Angora', | ||
'hamster', | ||
'porcupine', | ||
'fox_squirrel', | ||
'marmot', | ||
'beaver', | ||
'guinea_pig', | ||
'sorrel', | ||
'zebra', | ||
'hog', | ||
'wild_boar', | ||
'warthog', | ||
'hippopotamus', | ||
'ox', | ||
'water_buffalo', | ||
'bison', | ||
'ram', | ||
'bighorn', | ||
'ibex', | ||
'hartebeest', | ||
'impala', | ||
'gazelle', | ||
'Arabian_camel', | ||
'llama', | ||
'weasel', | ||
'mink', | ||
'polecat', | ||
'black-footed_ferret', | ||
'otter', | ||
'skunk', | ||
'badger', | ||
'armadillo', | ||
'three-toed_sloth', | ||
'orangutan', | ||
'gorilla', | ||
'chimpanzee', | ||
'gibbon', | ||
'siamang', | ||
'guenon', | ||
'patas', | ||
'baboon', | ||
'macaque', | ||
'langur', | ||
'colobus', | ||
'proboscis_monkey', | ||
'marmoset', | ||
'capuchin', | ||
'howler_monkey', | ||
'titi', | ||
'spider_monkey', | ||
'squirrel_monkey', | ||
'Madagascar_cat', | ||
'indri', | ||
'Indian_elephant', | ||
'African_elephant', | ||
'lesser_panda', | ||
'giant_panda', | ||
'barracouta', | ||
'eel', | ||
'coho', | ||
'rock_beauty', | ||
'anemone_fish', | ||
'sturgeon', | ||
'gar', | ||
'lionfish', | ||
'puffer', | ||
'abacus', | ||
'abaya', | ||
'academic_gown', | ||
'accordion', | ||
'acoustic_guitar', | ||
'aircraft_carrier', | ||
'airliner', | ||
'airship', | ||
'altar', | ||
'ambulance', | ||
'amphibian', | ||
'analog_clock', | ||
'apiary', | ||
'apron', | ||
'ashcan', | ||
'assault_rifle', | ||
'backpack', | ||
'bakery', | ||
'balance_beam', | ||
'balloon', | ||
'ballpoint', | ||
'Band_Aid', | ||
'banjo', | ||
'bannister', | ||
'barbell', | ||
'barber_chair', | ||
'barbershop', | ||
'barn', | ||
'barometer', | ||
'barrel', | ||
'barrow', | ||
'baseball', | ||
'basketball', | ||
'bassinet', | ||
'bassoon', | ||
'bathing_cap', | ||
'bath_towel', | ||
'bathtub', | ||
'beach_wagon', | ||
'beacon', | ||
'beaker', | ||
'bearskin', | ||
'beer_bottle', | ||
'beer_glass', | ||
'bell_cote', | ||
'bib', | ||
'bicycle-built-for-two', | ||
'bikini', | ||
'binder', | ||
'binoculars', | ||
'birdhouse', | ||
'boathouse', | ||
'bobsled', | ||
'bolo_tie', | ||
'bonnet', | ||
'bookcase', | ||
'bookshop', | ||
'bottlecap', | ||
'bow', | ||
'bow_tie', | ||
'brass', | ||
'brassiere', | ||
'breakwater', | ||
'breastplate', | ||
'broom', | ||
'bucket', | ||
'buckle', | ||
'bulletproof_vest', | ||
'bullet_train', | ||
'butcher_shop', | ||
'cab', | ||
'caldron', | ||
'candle', | ||
'cannon', | ||
'canoe', | ||
'can_opener', | ||
'cardigan', | ||
'car_mirror', | ||
'carousel', | ||
"carpenter's_kit", | ||
'carton', | ||
'car_wheel', | ||
'cash_machine', | ||
'cassette', | ||
'cassette_player', | ||
'castle', | ||
'catamaran', | ||
'CD_player', | ||
'cello', | ||
'cellular_telephone', | ||
'chain', | ||
'chainlink_fence', | ||
'chain_mail', | ||
'chain_saw', | ||
'chest', | ||
'chiffonier', | ||
'chime', | ||
'china_cabinet', | ||
'Christmas_stocking', | ||
'church', | ||
'cinema', | ||
'cleaver', | ||
'cliff_dwelling', | ||
'cloak', | ||
'clog', | ||
'cocktail_shaker', | ||
'coffee_mug', | ||
'coffeepot', | ||
'coil', | ||
'combination_lock', | ||
'computer_keyboard', | ||
'confectionery', | ||
'container_ship', | ||
'convertible', | ||
'corkscrew', | ||
'cornet', | ||
'cowboy_boot', | ||
'cowboy_hat', | ||
'cradle', | ||
'crane', | ||
'crash_helmet', | ||
'crate', | ||
'crib', | ||
'Crock_Pot', | ||
'croquet_ball', | ||
'crutch', | ||
'cuirass', | ||
'dam', | ||
'desk', | ||
'desktop_computer', | ||
'dial_telephone', | ||
'diaper', | ||
'digital_clock', | ||
'digital_watch', | ||
'dining_table', | ||
'dishrag', | ||
'dishwasher', | ||
'disk_brake', | ||
'dock', | ||
'dogsled', | ||
'dome', | ||
'doormat', | ||
'drilling_platform', | ||
'drum', | ||
'drumstick', | ||
'dumbbell', | ||
'Dutch_oven', | ||
'electric_fan', | ||
'electric_guitar', | ||
'electric_locomotive', | ||
'entertainment_center', | ||
'envelope', | ||
'espresso_maker', | ||
'face_powder', | ||
'feather_boa', | ||
'file', | ||
'fireboat', | ||
'fire_engine', | ||
'fire_screen', | ||
'flagpole', | ||
'flute', | ||
'folding_chair', | ||
'football_helmet', | ||
'forklift', | ||
'fountain', | ||
'fountain_pen', | ||
'four-poster', | ||
'freight_car', | ||
'French_horn', | ||
'frying_pan', | ||
'fur_coat', | ||
'garbage_truck', | ||
'gasmask', | ||
'gas_pump', | ||
'goblet', | ||
'go-kart', | ||
'golf_ball', | ||
'golfcart', | ||
'gondola', | ||
'gong', | ||
'gown', | ||
'grand_piano', | ||
'greenhouse', | ||
'grille', | ||
'grocery_store', | ||
'guillotine', | ||
'hair_slide', | ||
'hair_spray', | ||
'half_track', | ||
'hammer', | ||
'hamper', | ||
'hand_blower', | ||
'hand-held_computer', | ||
'handkerchief', | ||
'hard_disc', | ||
'harmonica', | ||
'harp', | ||
'harvester', | ||
'hatchet', | ||
'holster', | ||
'home_theater', | ||
'honeycomb', | ||
'hook', | ||
'hoopskirt', | ||
'horizontal_bar', | ||
'horse_cart', | ||
'hourglass', | ||
'iPod', | ||
'iron', | ||
"jack-o'-lantern", | ||
'jean', | ||
'jeep', | ||
'jersey', | ||
'jigsaw_puzzle', | ||
'jinrikisha', | ||
'joystick', | ||
'kimono', | ||
'knee_pad', | ||
'knot', | ||
'lab_coat', | ||
'ladle', | ||
'lampshade', | ||
'laptop', | ||
'lawn_mower', | ||
'lens_cap', | ||
'letter_opener', | ||
'library', | ||
'lifeboat', | ||
'lighter', | ||
'limousine', | ||
'liner', | ||
'lipstick', | ||
'Loafer', | ||
'lotion', | ||
'loudspeaker', | ||
'loupe', | ||
'lumbermill', | ||
'magnetic_compass', | ||
'mailbag', | ||
'mailbox', | ||
'maillot', | ||
'maillot', | ||
'manhole_cover', | ||
'maraca', | ||
'marimba', | ||
'mask', | ||
'matchstick', | ||
'maypole', | ||
'maze', | ||
'measuring_cup', | ||
'medicine_chest', | ||
'megalith', | ||
'microphone', | ||
'microwave', | ||
'military_uniform', | ||
'milk_can', | ||
'minibus', | ||
'miniskirt', | ||
'minivan', | ||
'missile', | ||
'mitten', | ||
'mixing_bowl', | ||
'mobile_home', | ||
'Model_T', | ||
'modem', | ||
'monastery', | ||
'monitor', | ||
'moped', | ||
'mortar', | ||
'mortarboard', | ||
'mosque', | ||
'mosquito_net', | ||
'motor_scooter', | ||
'mountain_bike', | ||
'mountain_tent', | ||
'mouse', | ||
'mousetrap', | ||
'moving_van', | ||
'muzzle', | ||
'nail', | ||
'neck_brace', | ||
'necklace', | ||
'nipple', | ||
'notebook', | ||
'obelisk', | ||
'oboe', | ||
'ocarina', | ||
'odometer', | ||
'oil_filter', | ||
'organ', | ||
'oscilloscope', | ||
'overskirt', | ||
'oxcart', | ||
'oxygen_mask', | ||
'packet', | ||
'paddle', | ||
'paddlewheel', | ||
'padlock', | ||
'paintbrush', | ||
'pajama', | ||
'palace', | ||
'panpipe', | ||
'paper_towel', | ||
'parachute', | ||
'parallel_bars', | ||
'park_bench', | ||
'parking_meter', | ||
'passenger_car', | ||
'patio', | ||
'pay-phone', | ||
'pedestal', | ||
'pencil_box', | ||
'pencil_sharpener', | ||
'perfume', | ||
'Petri_dish', | ||
'photocopier', | ||
'pick', | ||
'pickelhaube', | ||
'picket_fence', | ||
'pickup', | ||
'pier', | ||
'piggy_bank', | ||
'pill_bottle', | ||
'pillow', | ||
'ping-pong_ball', | ||
'pinwheel', | ||
'pirate', | ||
'pitcher', | ||
'plane', | ||
'planetarium', | ||
'plastic_bag', | ||
'plate_rack', | ||
'plow', | ||
'plunger', | ||
'Polaroid_camera', | ||
'pole', | ||
'police_van', | ||
'poncho', | ||
'pool_table', | ||
'pop_bottle', | ||
'pot', | ||
"potter's_wheel", | ||
'power_drill', | ||
'prayer_rug', | ||
'printer', | ||