Image-to-Image Search via ResNet50#

Open In Colab

Searching visually similar images with image queries is a very popular use case. However, using pre-trained models does not deliver the best results – the models are trained on general data that lack the particularities of your specific task. Here’s where Finetuner comes in! It enables you to accomplish this easily.

This guide will demonstrate how to fine-tune a ResNet model for image-to-image retrieval.

Note, please consider switching to GPU/TPU Runtime for faster inference.


!pip install 'finetuner[full]'


More specifically, we will fine-tune ResNet50 on Totally Looks Like Dataset. The dataset consists of 6016 pairs of images (12032 in total).

The dataset consists of pairs of images, these are the positive pairs. Negative pairs are constructed by taking two different images, i.e. images that are not in the same pair initially. Following this approach, we construct triplets and use the TripletLoss. You can find more in the how Finetuner works section.

After fine-tuning, the embeddings of positive pairs are expected to be pulled closer, while the embeddings for negative pairs are expected to be pushed away.


Our journey starts locally. We have to prepare the data and push it to the Jina AI Cloud and Finetuner will be able to get the dataset by its name. For this example, we already prepared the data, and we’ll provide the names of training data (tll-train-data) directly to Finetuner.


We don’t require you to push data to the Jina AI Cloud by yourself. Instead of a name, you can provide a DocumentArray and Finetuner will do the job for you. When working with documents where images are stored locally, please call doc.load_uri_to_blob() to reduce network transmission and speed up training.

import finetuner
from docarray import DocumentArray, Document

train_data = DocumentArray.pull('finetuner/tll-train-data', show_progress=True)
query_data = DocumentArray.pull('finetuner/tll-test-query-data', show_progress=True)
index_data = DocumentArray.pull('finetuner/tll-test-index-data', show_progress=True)


Backbone model#

Now let’s see which backbone models we can use. You can see available models by calling finetuner.describe_models().

For this example, we’re gonna go with resnet50.


Now that we have the training and evaluation datasets loaded as DocumentArrays and selected our model, we can start our fine-tuning run.

from finetuner.callback import EvaluationCallback

run =

Let’s understand what this piece of code does:

  • As you can see, we have to provide the model which we picked before.

  • We also set run_name and description, which are optional, but recommended in order to retrieve your run easily and have some context about it.

  • Furthermore, we had to provide names of the train_data.

  • We set TripletMarginLoss.

  • Additionally, we use finetuner.callback.EvaluationCallback for evaluation.

  • Lastly, we set the number of epochs and provide a learning_rate.


Now that we’ve created a run, let’s see its status. You can monitor the run by checking the status - run.status() - and the logs - run.logs() or run.stream_logs().

# note, the fine-tuning might takes 30~ minutes
for entry in run.stream_logs():

Since some runs might take up to several hours, it’s important to know how to reconnect to Finetuner and retrieve your runs.

import finetuner

run = finetuner.get_run(

You can continue monitoring the runs by checking the status - or the logs -


Currently, we don’t have a user-friendly way to get evaluation metrics from the finetuner.callback.EvaluationCallback we initialized previously. What you can do for now is to call run.logs() after the end of the run and see the evaluation results:

  Training [5/5] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 76/76 0:00:00 0:03:15  loss: 0.003
[16:39:13] DEBUG    Metric: 'model_average_precision' Value: 0.19598                           
           DEBUG    Metric: 'model_dcg_at_k' Value: 0.28571                                    
           DEBUG    Metric: 'model_f1_score_at_k' Value: 0.04382                               
           DEBUG    Metric: 'model_hit_at_k' Value: 0.46013                                    
           DEBUG    Metric: 'model_ndcg_at_k' Value: 0.28571                                   
           DEBUG    Metric: 'model_precision_at_k' Value: 0.02301                              
           DEBUG    Metric: 'model_r_precision' Value: 0.19598                                 
           DEBUG    Metric: 'model_recall_at_k' Value: 0.46013                                 
           DEBUG    Metric: 'model_reciprocal_rank' Value: 0.19598                             
           INFO     Done                                                                     
           INFO     Saving fine-tuned models ...                                               
           INFO     Saving model 'model' in /usr/src/app/tuned-models/model ...                
           INFO     Pushing saved model to Jina AI Cloud ...                                   
[16:39:41] INFO     Pushed model artifact ID: '62b33cb0037ad91ca7f20530'                       
           INFO     Finished 🚀                                                                                 


After the run has finished successfully, you can download the tuned model on your local machine:

artifact = run.save_artifact('resnet-model')


Now you saved the artifact into your host machine, let’s use the fine-tuned model to encode a new Document:

Inference with ONNX

In case you set to_onnx=True when calling function, please use model = finetuner.get_model(artifact, is_onnx=True)

query = DocumentArray([query_data[0]])

model = finetuner.get_model(artifact=artifact, device='cuda')

finetuner.encode(model=model, data=query)
finetuner.encode(model=model, data=index_data)

assert query.embeddings.shape == (1, 2048)

And finally, you can use the embedded query to find top-k visually related images within index_data as follows:

query.match(index_data, limit=10, metric='cosine')

Before and after#

We can directly compare the results of our fine-tuned model with its zero-shot counterpart to get a better idea of how finetuning affects the results of a search. While the differences between the two models may be subtle for some queries, some of the examples below (such as the second example) show that the model after fine-tuning is able to better match similar images.

To save you some time, we have plotted some examples where the model’s ability to return similar images has clearly improved:


On the other hand, there are also cases where the fine-tuned model performs worse, and fails to correctly match images that it previously could. This case is much rarer than the previous case. For this dataset, there were 108 occasions where the fine-tuned model returned the correct pair where it couldn’t before and only 33 occasions where the finetuned model returned an incorrect image after fine-tuning but returned a correct one before. Nevertheless, it still can happen: