Image Embedding with Timm

author: Jael Gu, Filip

Description

An image embedding operator generates a vector given an image. This operator extracts features for image with pre-trained models provided by Timm. Timm is a deep-learning library developed by Ross Wightman, who maintains SOTA deep-learning models and tools in computer vision.

Code Example

Load an image from path './towhee.jpeg' and use the pre-trained ResNet50 model ('resnet50') to generate an image embedding.

Write the pipeline in simplified style:

import towhee

towhee.glob('./towhee.jpeg') \
      .image_decode() \
      .image_embedding.timm(model_name='resnet50') \
      .show()

Write a same pipeline with explicit inputs/outputs name specifications:

import towhee

towhee.glob['path']('./towhee.jpeg') \
      .image_decode['path', 'img']() \
      .image_embedding.timm['img', 'vec'](model_name='resnet50') \
      .select['img', 'vec']() \
      .show()

Factory Constructor

Create the operator via the following factory method:

image_embedding.timm(model_name='resnet34', num_classes=1000, skip_preprocess=False)

Parameters:

model_name: str

The model name in string. The default value is "resnet34". Refer to Timm Docs to get a full list of supported models.

num_classes: int

The number of classes. The default value is 1000. It is related to model and dataset.

skip_preprocess: bool

The flag to control whether to skip image pre-process. The default value is False. If set to True, it will skip image preprocessing steps (transforms). In this case, input image data must be prepared in advance in order to properly fit the model.

Interface

An image embedding operator takes a towhee image as input. It uses the pre-trained model specified by model name to generate an image embedding in ndarray.

Parameters:

data: towhee.types.Image

The decoded image data in towhee Image (a subset of numpy.ndarray).

Returns: numpy.ndarray

An image embedding generated by model, in shape of (feature_dim,).

Towhee Serve

Models which is supported the towhee.serve.

Model List

models	models	models	models
adv_inception_v3	bat_resnext26ts	beit_base_patch16_224	beit_base_patch16_224_in22k
beit_base_patch16_384	beit_large_patch16_224	beit_large_patch16_224_in22k	beit_large_patch16_384
beit_large_patch16_512	botnet26t_256	cait_m36_384	cait_m48_448
cait_s24_224	cait_s24_384	cait_s36_384	cait_xs24_384
cait_xxs24_224	cait_xxs24_384	cait_xxs36_224	cait_xxs36_384
coat_lite_mini	coat_lite_small	coat_lite_tiny	convit_base
convit_small	convit_tiny	convmixer_768_32	convmixer_1024_20_ks9_p14
convmixer_1536_20	convnext_base	convnext_base_384_in22ft1k	convnext_base_in22ft1k
convnext_base_in22k	convnext_large	convnext_large_384_in22ft1k	convnext_large_in22ft1k
convnext_large_in22k	convnext_small	convnext_small_384_in22ft1k	convnext_small_in22ft1k
convnext_small_in22k	convnext_tiny	convnext_tiny_384_in22ft1k	convnext_tiny_hnf
convnext_tiny_in22ft1k	convnext_tiny_in22k	convnext_xlarge_384_in22ft1k	convnext_xlarge_in22ft1k
convnext_xlarge_in22k	cs3darknet_focus_l	cs3darknet_focus_m	cs3darknet_l
cs3darknet_m	cspdarknet53	cspresnet50	cspresnext50
darknet53	deit3_base_patch16_224	deit3_base_patch16_224_in21ft1k	deit3_base_patch16_384
deit3_base_patch16_384_in21ft1k	deit3_huge_patch14_224	deit3_huge_patch14_224_in21ft1k	deit3_large_patch16_224
deit3_large_patch16_224_in21ft1k	deit3_large_patch16_384	deit3_large_patch16_384_in21ft1k	deit3_small_patch16_224
deit3_small_patch16_224_in21ft1k	deit3_small_patch16_384	deit3_small_patch16_384_in21ft1k	deit_base_distilled_patch16_224
deit_base_distilled_patch16_384	deit_base_patch16_224	deit_base_patch16_384	deit_small_distilled_patch16_224
deit_small_patch16_224	deit_tiny_distilled_patch16_224	deit_tiny_patch16_224	densenet121
densenet161	densenet169	densenet201	densenetblur121d
dla34	dla46_c	dla46x_c