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"Fine Tuning Similar Cars Search - Qdrant",
"Fine Tuning Similar Cars Search"
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[
"Fine Tuning Similar Cars Search - Qdrant",
"Fine Tuning Similar Cars Search"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(1) > li:nth-of-type(3) | [
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"articles",
"articles/cars-recognition"
] | 4,673 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Fine Tuning Similar Cars Search"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(1) > li:nth-of-type(4) | [
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"articles",
"articles/cars-recognition"
] | 4,674 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Fine Tuning Similar Cars Search"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2) | [
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[
"Fine Tuning Similar Cars Search - Qdrant",
"Fine Tuning Similar Cars Search"
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[
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[
"Fine Tuning Similar Cars Search - Qdrant",
"Fine Tuning Similar Cars Search"
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"articles/cars-recognition"
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[
"Fine Tuning Similar Cars Search - Qdrant",
"Fine Tuning Similar Cars Search"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(2) > li:nth-of-type(2) | [
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[
"Fine Tuning Similar Cars Search - Qdrant",
"Fine Tuning Similar Cars Search"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(2) > li:nth-of-type(3) | [
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"Fine Tuning Similar Cars Search"
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[
"Fine Tuning Similar Cars Search - Qdrant",
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[
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"Fine Tuning Similar Cars Search"
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"articles",
"articles/cars-recognition"
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[
"Fine Tuning Similar Cars Search - Qdrant",
"Fine Tuning Similar Cars Search"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(4) | [
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"A closer look at Quaterion"
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] | li | TrainableModel: A subclass of pl.LightNingModule that has additional hook methods such as configure_encoders, configure_head, configure_metrics and others | [
"articles",
"articles/cars-recognition"
] | 4,692 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(1) | [
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] | li | to define objects needed for training and evaluation —see below to learn more on these. | [
"articles",
"articles/cars-recognition"
] | 4,693 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(2) | [
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[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(2) | [
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"articles",
"articles/cars-recognition"
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[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(2) > ol > li:nth-of-type(1) | [
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] | li | quaterion_models: package that you need for inference. | [
"articles",
"articles/cars-recognition"
] | 4,696 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(2) > ol > li:nth-of-type(2) | [
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"articles",
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[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(3) | [
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[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(3) | [
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] | li | In most of the cases, you may use a frozen pretrained encoder, e.g., ResNets from torchvision, or language modelling | [
"articles",
"articles/cars-recognition"
] | 4,699 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(3) | [
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] | li | models from transformers, with a trainable EncoderHead stacked on top of it. | [
"articles",
"articles/cars-recognition"
] | 4,700 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(3) | [
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] | li | quaterion_models offers several ready-to-use EncoderHead implementations, | [
"articles",
"articles/cars-recognition"
] | 4,701 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(3) | [
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"articles",
"articles/cars-recognition"
] | 4,702 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7) | [
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"articles",
"articles/cars-recognition"
] | 4,703 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7) | [
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[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
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[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
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] | p | This will also help us better understand how the abovementioned objects fit together in a real project. | [
"articles",
"articles/cars-recognition"
] | 4,707 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(8) | [
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] | p | Let’s start walking through some of the important parts of the code. | [
"articles",
"articles/cars-recognition"
] | 4,708 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9) | [
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] | p | If you are looking for the complete source code instead, you can find it under the examples | [
"articles",
"articles/cars-recognition"
] | 4,709 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"A closer look at Quaterion"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9) | [
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] | p | In this tutorial, we will use the Stanford Cars | [
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"Fine Tuning Similar Cars Search - Qdrant",
"Dataset"
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"Fine Tuning Similar Cars Search - Qdrant",
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] | p | and it is split into training and testing subsets with almost a 50-50% split. | [
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"articles/cars-recognition"
] | 4,716 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Dataset"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11) | [
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] | p | To make things even more interesting, however, we will first merge training and testing subsets, | [
"articles",
"articles/cars-recognition"
] | 4,717 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Dataset"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11) | [
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] | p | In the following code borrowed from data.py: | [
"articles",
"articles/cars-recognition"
] | 4,721 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Dataset"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(3) > li:nth-of-type(1) | [
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] | li | get_datasets() function performs the splitting task described above. | [
"articles",
"articles/cars-recognition"
] | 4,722 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Dataset"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(3) > li:nth-of-type(2) | [
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] | li | get_dataloaders() function creates GroupSimilarityDataLoader instances from training and testing datasets. | [
"articles",
"articles/cars-recognition"
] | 4,723 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Dataset"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(3) > li:nth-of-type(3) | [
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] | li | Datasets are regular PyTorch datasets that emit SimilarityGroupSample instances. | [
"articles",
"articles/cars-recognition"
] | 4,724 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Dataset"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13) | [
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] | p | N.B.: Currently, Quaterion has two data types to represent samples in a dataset. | [
"articles",
"articles/cars-recognition"
] | 4,725 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Dataset"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13) | [
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"articles",
"articles/cars-recognition"
] | 4,726 |
[
"Fine Tuning Similar Cars Search - Qdrant"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(3) | [
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"articles",
"articles/cars-recognition"
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[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
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] | p | Now it’s time to review one of the most exciting building blocks of Quaterion: TrainableModel. | [
"articles",
"articles/cars-recognition"
] | 4,728 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14) | [
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"articles",
"articles/cars-recognition"
] | 4,729 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14) | [
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"articles",
"articles/cars-recognition"
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[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
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"articles",
"articles/cars-recognition"
] | 4,731 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14) | [
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] | p | It is central to fine tuning with Quaterion, so we will break down this essential code in models.py | [
"articles",
"articles/cars-recognition"
] | 4,732 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14) | [
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[
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"Trainable Model"
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] | p | Let’s begin with the imports: | [
"articles",
"articles/cars-recognition"
] | 4,734 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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] | p | In the following code snippet, we subclass TrainableModel. | [
"articles",
"articles/cars-recognition"
] | 4,735 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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] | p | You may use __init__() to store some attributes to be used in various configure_* methods later on. | [
"articles",
"articles/cars-recognition"
] | 4,736 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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] | p | The more interesting part is, however, in the configure_encoders() method. | [
"articles",
"articles/cars-recognition"
] | 4,737 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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] | p | We need to return an instance of Encoder (or a dictionary with Encoder instances as values) from this method. | [
"articles",
"articles/cars-recognition"
] | 4,738 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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"articles",
"articles/cars-recognition"
] | 4,739 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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] | p | Notice now how it is created with a pretrained ResNet152 model whose classification layer is replaced by an identity function. | [
"articles",
"articles/cars-recognition"
] | 4,740 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16) | [
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] | p | In Quaterion, a SimilarityModel is composed of one or more Encoders | [
"articles",
"articles/cars-recognition"
] | 4,741 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16) | [
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[
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[
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"Trainable Model"
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] | p | with a unified API such as a configurable dropout value. | [
"articles",
"articles/cars-recognition"
] | 4,744 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16) | [
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] | p | You may use one of them or create your own subclass of EncoderHead. | [
"articles",
"articles/cars-recognition"
] | 4,745 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16) | [
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] | p | In either case, you need to return an instance of it from configure_head | [
"articles",
"articles/cars-recognition"
] | 4,746 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16) | [
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] | p | In this example, we will use a SkipConnectionHead, which is lightweight and more resistant to overfitting. | [
"articles",
"articles/cars-recognition"
] | 4,747 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17) | [
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] | p | Quaterion has implementations of some popular loss functions for similarity learning, all of which subclass either GroupLoss | [
"articles",
"articles/cars-recognition"
] | 4,748 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17) | [
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] | p | In this example, we will use TripletLoss, | [
"articles",
"articles/cars-recognition"
] | 4,750 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17) | [
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] | p | which is a subclass of GroupLoss. | [
"articles",
"articles/cars-recognition"
] | 4,751 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17) | [
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"articles",
"articles/cars-recognition"
] | 4,752 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17) | [
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"articles",
"articles/cars-recognition"
] | 4,753 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17) | [
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] | p | In our example label is a make of the car. | [
"articles",
"articles/cars-recognition"
] | 4,754 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17) | [
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] | p | Those datasets should emit SimilarityGroupSample. | [
"articles",
"articles/cars-recognition"
] | 4,755 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17) | [
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] | p | Other alternatives are implementations of PairwiseLoss, which consume SimilarityPairSample - pair of objects for which similarity is specified individually. | [
"articles",
"articles/cars-recognition"
] | 4,756 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17) | [
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] | p | To see an example of the latter, you may need to check out the NLP Tutorial | [
"articles",
"articles/cars-recognition"
] | 4,757 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18) | [
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"articles",
"articles/cars-recognition"
] | 4,758 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
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"articles",
"articles/cars-recognition"
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[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18) | [
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[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19) | [
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[
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"Trainable Model"
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] | p | When it is configured, outputs will be computed once and cached in the preferred device for direct usage later on. | [
"articles",
"articles/cars-recognition"
] | 4,762 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19) | [
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"articles",
"articles/cars-recognition"
] | 4,765 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19) | [
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] | li | save_dir, i.e., where to persist caches for subsequent runs, | [
"articles",
"articles/cars-recognition"
] | 4,770 |
[
"Fine Tuning Similar Cars Search - Qdrant",
"Trainable Model"
] | /articles/cars-recognition/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(4) > li:nth-of-type(3) | [
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] | li | batch_size, i.e., batch size to be used only when creating caches - the batch size to be used during the actual training might be different. | [
"articles",
"articles/cars-recognition"
] | 4,771 |
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