copied
Readme
Files and versions
2.3 KiB
Image Embedding with Timm
author: Jael Gu, Filip
Desription
An image embedding operator generates a vector given an image. This operator extracts features for image with pretrained models provided by Timm. Timm is a deep-learning library developed by Ross Wightman, which maintains SOTA deep-learning models and tools in computer vision.
Code Example
Load an image from path './dog.jpg' and use the pretrained ResNet50 model ('resnet50') to generate an image embedding.
Write the pipeline in simplified style:
from towhee import dc
dc.glob('./dog.jpg') \
.image_decode.cv2() \
.image_embedding.timm(model_name='resnet50') \
.show()
Write a same pipeline with explicit inputs/outputs name specifications:
from towhee import dc
dc.glob['path']('./dog.jpg') \
.image_decode.cv2['path', 'img']() \
.image_embedding.timm['img', 'vec'](model_name='resnet50') \
.select('vec') \
.to_list()
[array([0. , 0. , 0. , ..., 0. , 0.01748613,
0. ], dtype=float32)]
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 Timm Docs to get a full list of supported models.
num_classes: 1000
The number of classes. The default value is 1000. It is related to model and dataset.
skip_preprocess: bool Flag to control whether to skip image preprocess, defaults to False. If set to True, image preprocess steps such as transform, normalization will be skipped. In this case, the user should guarantee that all the input images are already reprocessed properly, and thus can be fed to model directly.
Interface
An image embedding operator takes an image in ndarray as input. It uses the pre-trained model specified by model name to generate an image embedding in ndarray.
Parameters:
img: numpy.ndarray
The decoded image data in numpy.ndarray.
Returns: numpy.ndarray
The image embedding extracted by model.
2.3 KiB
Image Embedding with Timm
author: Jael Gu, Filip
Desription
An image embedding operator generates a vector given an image. This operator extracts features for image with pretrained models provided by Timm. Timm is a deep-learning library developed by Ross Wightman, which maintains SOTA deep-learning models and tools in computer vision.
Code Example
Load an image from path './dog.jpg' and use the pretrained ResNet50 model ('resnet50') to generate an image embedding.
Write the pipeline in simplified style:
from towhee import dc
dc.glob('./dog.jpg') \
.image_decode.cv2() \
.image_embedding.timm(model_name='resnet50') \
.show()
Write a same pipeline with explicit inputs/outputs name specifications:
from towhee import dc
dc.glob['path']('./dog.jpg') \
.image_decode.cv2['path', 'img']() \
.image_embedding.timm['img', 'vec'](model_name='resnet50') \
.select('vec') \
.to_list()
[array([0. , 0. , 0. , ..., 0. , 0.01748613,
0. ], dtype=float32)]
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 Timm Docs to get a full list of supported models.
num_classes: 1000
The number of classes. The default value is 1000. It is related to model and dataset.
skip_preprocess: bool Flag to control whether to skip image preprocess, defaults to False. If set to True, image preprocess steps such as transform, normalization will be skipped. In this case, the user should guarantee that all the input images are already reprocessed properly, and thus can be fed to model directly.
Interface
An image embedding operator takes an image in ndarray as input. It uses the pre-trained model specified by model name to generate an image embedding in ndarray.
Parameters:
img: numpy.ndarray
The decoded image data in numpy.ndarray.
Returns: numpy.ndarray
The image embedding extracted by model.