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Last updated on 2025年2月25日

What are the best practices for choosing a pre-trained model for transfer learning?

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Transfer learning is a powerful technique for applying the knowledge learned from one domain to another. It can save you time, resources, and improve your performance when you have limited data or a complex task. But how do you choose the best pre-trained model for your needs? Here are some best practices to consider.

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Dave Frank

Leading and transforming organizations through data and technology
Maritza Carvalho Francisco

Head de Governan?a Digital, Dados e IA | Transforma??o Digital e Panejamento Estratégico | Gest?o de Portfólio | Alta…
Raed ALABBADY

#1 Engineering Project Management Expert On LinkedIn Yemen by Favikon | Supply Chain and SAP Professional | PMP| PMI |…

1 Domain similarity

The first factor to consider is how similar the domain of the pre-trained model is to your target domain. Ideally, you want a model that has been trained on a large and diverse dataset that covers the same or related topics, categories, and features as your data. For example, if you want to classify flowers, you might use a model that has been trained on ImageNet, a dataset that contains many images of natural objects, including flowers. However, if you want to detect faces, you might use a model that has been trained on a face recognition dataset, such as LFW or CelebA.

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Maritza Carvalho Francisco

Head de Governan?a Digital, Dados e IA | Transforma??o Digital e Panejamento Estratégico | Gest?o de Portfólio | Alta Performance com IA | Cultura Data Driven | OKR | CDOIQ | DMBOK | PMP | Agile
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A escolha do modelo pré-treinado para aprendizado por transferência depende da tarefa, tamanho do dataset e recursos computacionais. Algumas boas práticas incluem: ? Definir a tarefa: Para imagens, modelos como ResNet, EfficientNet; para NLP, BERT, GPT. ? Analisar o dataset original: Modelos treinados em grandes datasets (ex: ImageNet, C4) generalizam melhor. ? Balancear performance e eficiência: Modelos grandes s?o mais precisos, mas exigem mais computa??o. Modelos otimizados (ex: MobileNet, DistilBERT) s?o mais leves. ? Escolher entre fine-tuning e feature extraction: Para datasets pequenos, usar o modelo como extrator de features; para datasets maiores, fazer fine-tuning.

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Raed ALABBADY

#1 Engineering Project Management Expert On LinkedIn Yemen by Favikon | Supply Chain and SAP Professional | PMP| PMI | LeanPM/LPMF | CHL Certified-CILT (UK) | Onboarded 600+ Hands-on Certifications
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When choosing a pre-trained model for transfer learning, we prioritize task alignment by selecting models trained on data similar to your domain, like ImageNet for images. Opt for architectures our problem type, such as CNNs for images or transformers for NLP. Evaluate performance on relevant benchmarks and balance model complexity with our computational resources. Ensure the model supports easy fine-tuning on our dataset and has strong community support with clear documentation. Lastly, we need to verify the license aligns with our use case to ensure optimal performance and efficiency.

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Rémy Wehrung
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Pour choisir un modèle pré-formé pour l'apprentissage par transfert, il faut : Sélectionner un modèle entra?né sur une tache similaire évaluer ses performances sur des benchmarks pertinents Tester plusieurs modèles sur un sous-ensemble de vos données Analyser les courbes d'apprentissage

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2 Model architecture

The second factor to consider is the architecture of the pre-trained model. Different architectures have different strengths and weaknesses, depending on the task and the data. For example, convolutional neural networks (CNNs) are good for image processing, recurrent neural networks (RNNs) are good for sequential data, and transformers are good for natural language processing. You should choose a model that matches the type and complexity of your data and task. For example, if you want to generate captions for images, you might use a model that combines a CNN and an RNN, such as Show and Tell.

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Raed ALABBADY

#1 Engineering Project Management Expert On LinkedIn Yemen by Favikon | Supply Chain and SAP Professional | PMP| PMI | LeanPM/LPMF | CHL Certified-CILT (UK) | Onboarded 600+ Hands-on Certifications
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When choosing a pre-trained model for transfer learning, we prioritize task alignment by selecting models trained on data similar to our domain, like ImageNet for images. Opt for architectures our problem type, such as CNNs for images or transformers for NLP. Evaluate performance on relevant benchmarks and balance model complexity with our computational resources. Ensure the model supports easy fine-tuning on our dataset and has strong community support with clear documentation. Lastly, we need to verify the license aligns with our use case to ensure optimal performance and efficiency.

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3 Model size

The third factor to consider is the size of the pre-trained model. The size of a model depends on the number of parameters, layers, and neurons it has. Generally, larger models have more capacity to learn and generalize, but they also require more computational resources, memory, and training time. You should choose a model that fits your hardware and budget constraints, as well as your performance goals. For example, if you have a small dataset and a low-end device, you might use a model that has fewer parameters and layers, such as MobileNet. However, if you have a large dataset and a high-end device, you might use a model that has more parameters and layers, such as ResNet.

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4 Model availability

The fourth factor to consider is the availability of the pre-trained model. There are many sources where you can find and download pre-trained models, such as TensorFlow Hub, PyTorch Hub, Hugging Face, and Keras Applications. You should choose a model that is easy to access, compatible with your framework and environment, and well-documented and supported. For example, if you use TensorFlow and Python, you might use a model that is available on TensorFlow Hub and has a clear tutorial and code example.

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Dave Frank

Leading and transforming organizations through data and technology
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Don't just check if you can download it; check how you can use it. Is it open-source (e.g., MIT, Apache)? Does it have commercial restrictions? Understanding the licensing is crucial for avoiding legal issues. Consider the license's implications for modifications and redistribution. Can you adapt the model for your specific needs, and can you share those adaptations?

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5 Model adaptability

The fifth factor to consider is the adaptability of the pre-trained model. Depending on your data and task, you might need to fine-tune, modify, or extend the pre-trained model to fit your specific needs. You should choose a model that is flexible and customizable, and that allows you to change its parameters, layers, or outputs. For example, if you want to add a new class to your classification task, you might use a model that has a softmax layer as its output, and replace it with a new layer with the appropriate number of neurons.

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6 Model evaluation

The sixth factor to consider is the evaluation of the pre-trained model. Before you use the pre-trained model for your task, you should test its performance on your data and compare it with other models or baselines. You should choose a model that achieves high accuracy, precision, recall, or other relevant metrics for your task. You should also check for potential biases, errors, or limitations of the pre-trained model, and address them accordingly. For example, if you find that the pre-trained model is overfitting or underfitting your data, you might adjust its hyperparameters, regularization, or data augmentation techniques.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Dave Frank

Leading and transforming organizations through data and technology
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Pre-trained models are trained on massive datasets, which can inadvertently contain and amplify societal biases. Therefore, consider the dataset the model was trained on. Does it reflect genera population or your target group (echo-chamber)?

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Rafa Sanchez

Software Development Engineer @Amazon | MSCS Graduate @ University of Southern California | Fulbrighter 21/22
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Bear in mind that, the task for which the pretrained model was different from ours, so a new training phase will be needed so that the model is able to see our data and change its parameters to fit the new task. When doing this, be careful as an aggressive training may mess up pretraining knowledge. My recommendation is that, the new training phase is done in two stages. In the first one, change the model header (usually last few layers) to adapt it to the new task. Freeze all parameters in the model but those from the newly added layers. Train the model for a few epochs with a normal learning rate. In the second, unfreeze all parameters and retrain for a few more epochs with a reduced learning rate.

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What are the best practices for choosing a pre-trained model for transfer learning?

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1 Domain similarity

2 Model architecture

3 Model size

4 Model availability

5 Model adaptability

6 Model evaluation

7 Here’s what else to consider

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