img2vec-pytorch 1.0.1

Use pre-trained models in PyTorch to extract vector embeddings for any image

Image 2 Vec with PyTorch

Medium post on building the first version from scratch: https://becominghuman.ai/extract-a-feature-vector-for-any-image-with-pytorch-9717561d1d4c

Looking for a simpler image vector integration for your project? Check out our free API at https://latentvector.space

Applications of image embeddings:

• Ranking for recommender systems
• Clustering images to different categories
• Classification tasks
• Image compression

Available models

Model name	Return vector length
Resnet-18	512
Alexnet	4096
Vgg-11	4096
Densenet	1024
efficientnet_b0	1280
efficientnet_b1	1280
efficientnet_b2	1408
efficientnet_b3	1536
efficientnet_b4	1792
efficientnet_b5	2048
efficientnet_b6	2304
efficientnet_b7	2560

Installation

Tested on Python 3.6 and torchvision 0.11.0 (nightly, 2021-09-25)

Requires Pytorch: http://pytorch.org/

conda install -c pytorch-nightly torchvision

pip install img2vec_pytorch

Run test

python -m img2vec_pytorch.test_img_to_vec

Using img2vec as a library

from img2vec_pytorch import Img2Vec
from PIL import Image

# Initialize Img2Vec with GPU
img2vec = Img2Vec(cuda=True)

# Read in an image (rgb format)
img = Image.open('test.jpg')
# Get a vector from img2vec, returned as a torch FloatTensor
vec = img2vec.get_vec(img, tensor=True)
# Or submit a list
vectors = img2vec.get_vec(list_of_PIL_images)

For running the example, you will additionally need:

• Pillow: pip install Pillow
• Sklearn pip install scikit-learn

Running the example

git clone https://github.com/christiansafka/img2vec.git

cd img2vec/example

python test_img_similarity.py

Expected output

Which filename would you like similarities for?
cat.jpg
0.72832 cat2.jpg
0.641478 catdog.jpg
0.575845 face.jpg
0.516689 face2.jpg

Which filename would you like similarities for?
face2.jpg
0.668525 face.jpg
0.516689 cat.jpg
0.50084 cat2.jpg
0.484863 catdog.jpg

Try adding your own photos!

Img2Vec Params

cuda = (True, False)   # Run on GPU?     default: False
model = ('resnet-18', 'alexnet', 'vgg', 'densenet')   # Which model to use?     default: 'resnet-18'

Advanced users

---

Read only file systems

If you use this library from the app running in read only environment (for example, docker container), specify writable directory where app can store pre-trained models.

export TORCH_HOME=/tmp/torch

Additional Parameters

layer = 'layer_name' or int   # For advanced users, which layer of the model to extract the output from.   default: 'avgpool'
layer_output_size = int   # Size of the output of your selected layer

Resnet-18

Defaults: (layer = 'avgpool', layer_output_size = 512)
Layer parameter must be an string representing the name of a layer below

conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
bn1 = nn.BatchNorm2d(64)
relu = nn.ReLU(inplace=True)
maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
layer1 = self._make_layer(block, 64, layers[0])
layer2 = self._make_layer(block, 128, layers[1], stride=2)
layer3 = self._make_layer(block, 256, layers[2], stride=2)
layer4 = self._make_layer(block, 512, layers[3], stride=2)
avgpool = nn.AvgPool2d(7)
fc = nn.Linear(512 * block.expansion, num_classes)

Alexnet

Defaults: (layer = 2, layer_output_size = 4096)
Layer parameter must be an integer representing one of the layers below

alexnet.classifier = nn.Sequential(
            7. nn.Dropout(),                  < - output_size = 9216
            6. nn.Linear(256 * 6 * 6, 4096),  < - output_size = 4096
            5. nn.ReLU(inplace=True),         < - output_size = 4096
            4. nn.Dropout(),		      < - output_size = 4096
            3. nn.Linear(4096, 4096),	      < - output_size = 4096
            2. nn.ReLU(inplace=True),         < - output_size = 4096
            1. nn.Linear(4096, num_classes),  < - output_size = 4096
        )

Vgg

Defaults: (layer = 2, layer_output_size = 4096)

vgg.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, num_classes),
        )

Densenet

Defaults: (layer = 1 from features, layer_output_size = 1024)

densenet.features = nn.Sequential(OrderedDict([
	('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2,
						padding=3, bias=False)),
	('norm0', nn.BatchNorm2d(num_init_features)),
	('relu0', nn.ReLU(inplace=True)),
	('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
]))

EfficientNet

Defaults: (layer = 1 from features, layer_output_size = 1280 for efficientnet_b0 model)

To-do

• Benchmark speed and accuracy
• Add ability to fine-tune on input data
• Export documentation to a normal place