ONNX port of microsoft/resnet-50.
3
4 commits
2 linked in READMEs
updated Jul 15, 2024
endpoints_compatible
image-classification
onnx
transformers
README
ONNX port of microsoft/resnet-50.
This model is intended to be used for image classification and similarity searches.
You can find the ONNX port implementation here
Usage
Here's an example of performing inference using the model with FastEmbed.
from fastembed import ImageEmbedding
images = [
"./path/to/image1.jpg",
"./path/to/image2.jpg",
]
model = ImageEmbedding(model_name="Qdrant/resnet50-onnx")
embeddings = list(model.embed(images))
# [
# array([-0.1115, 0.0097, 0.0052, 0.0195, ...], dtype=float32),
# array([-0.1019, 0.0635, -0.0332, 0.0522, ...], dtype=float32)
# ]
Contributors
Similar repos
ONNX port of microsoft/resnet-50.
3
4 commits
2 linked in READMEs
updated Jul 15, 2024
endpoints_compatible
image-classification
onnx
transformers
README
ONNX port of microsoft/resnet-50.
This model is intended to be used for image classification and similarity searches.
You can find the ONNX port implementation here
Usage
Here's an example of performing inference using the model with FastEmbed.
from fastembed import ImageEmbedding
images = [
"./path/to/image1.jpg",
"./path/to/image2.jpg",
]
model = ImageEmbedding(model_name="Qdrant/resnet50-onnx")
embeddings = list(model.embed(images))
# [
# array([-0.1115, 0.0097, 0.0052, 0.0195, ...], dtype=float32),
# array([-0.1019, 0.0635, -0.0332, 0.0522, ...], dtype=float32)
# ]