**Table of Contents**

- Evaluating Siamese Network Accuracy (F1-Score, Precision, and Recall) with Keras and TensorFlow
- Building the Face Recognition Application with Siamese Networks
- Introduction to Model Evaluation in Face Recognition
- Introduction to Siamese Networks in Facial Recognition Systems
- Utilizing Siamese Networks for Face Verification
- Overview of the Face Verification Pipeline with Siamese Networks
- Implementing Face Recognition and Verification
- The Role of Siamese Network Training in Enhancing Face Recognition Accuracy
- Evaluating the Accuracy of Siamese Network-Based Face Recognition
- Configuring Your Development Environment
- Need Help Configuring Your Development Environment?
- Project Structure
- Face Recognition Model Evaluation: Understanding Precision, Recall, and F1-Score
- The Importance of a Diverse Test Set in Model Evaluation
- Choosing Representative Subjects for Reliable Evaluation
- Deep Dive into the Model Evaluation Process
- Beyond Accuracy: Exploring Advanced Metrics for Model Evaluation
- Precision, Recall, and F1-Score: Understanding the Core Metrics
- Implementing Precision and Recall Calculations in Python
- Defining Key Evaluation Metrics: Precision and Recall
- Introducing the F1-Score for Balanced Model Assessment
- Importing Essential Libraries for Siamese Network Evaluation
- Detailed Explanation of Metric Calculation in Python
- Crafting a Custom Function for Precision, Recall, and F1-Score
- Defining the Metrics Function for Model Evaluation
- Binary Classification Approach in Metrics Calculation
- Computing True Positives, Negatives, and False Metrics
- Calculating Precision, Recall, and F1-Score in Practice
- Loading and Configuring the Siamese Model for Inference
- Loading the Trained Siamese Model for Evaluation
- Initializing the Siamese Model for Data Analysis
- Setting Up a Data Pipeline for Effective Model Testing
- Creating a Robust Data Pipeline for Model Testing
- Assembling the Test Dataset for Model Accuracy Assessment
- Constructing a Face Database for Comprehensive Testing
- Preparing the Face Database and Test Dataset for Evaluation
- Preparing Face Database Entries for Accurate Model Evaluation
- Structuring Data for Siamese Model Evaluation
- Implementing Predictive Analysis with the Siamese Model
- Making Predictions and Evaluating the Siamese Model
- Conducting Predictive Testing on the Dataset
- Calculating Distances for Facial Recognition
- Analyzing Embedding Distances for Accurate Identification
- Analyzing Distance-Based Predictions in Siamese Networks
- Preparing Data for Accuracy and Class-Wise Metric Evaluation
- Calculating Class-Specific Metrics for In-Depth Analysis
- Leveraging Confusion Matrix for Holistic Performance Insight
- Using Confusion Matrix for Comprehensive Model Evaluation
- Implementing Confusion Matrix in Python for Siamese Model
- Direct Calculation of Recall and Precision from Confusion Matrix
- Deriving Overall Precision, Recall, and F1-Score
- Summary and Conclusion

**Evaluating Siamese Network Accuracy (F1 Score, Precision, and Recall) with Keras and TensorFlow**

In this tutorial, we will learn to evaluate our trained Siamese network based face recognition application, which we built in the previous tutorials of this series. We will discuss and understand the different metrics (i.e., accuracy, precision, recall, F1 score) that we can use to evaluate our model and analyze its performance on novel unseen faces for face recognition.

**Building the Face Recognition Application with Siamese Networks**

In the previous tutorial of this series, we discussed how we could put together the modules that we developed in the initial parts of this series to build our end-to-end face recognition application.

Also, we implemented the procedure and code to train our Siamese network based model end-to-end. Furthermore, we discussed in detail the process and code involved in making predictions with our trained model in real-time.

**Introduction to Model Evaluation in Face Recognition**

In this tutorial, we will take this further and learn how to evaluate our trained model using Keras and TensorFlow. This will allow us to get a sense of its performance on novel unseen data and quantify its generalization capabilities for real-world applications.

This lesson is the last in our 5-part series on Siamese networks and their application in face recognition:

*Face Recognition with Siamese Networks, Keras, and TensorFlow**Building a Dataset for Triplet Loss with Keras and TensorFlow**Triplet Loss with Keras and TensorFlow**Training and Making Predictions with Siamese Networks and Triplet Loss**Evaluating Siamese Network Accuracy (F1 Score, Precision, and Recall) with Keras and TensorFlow***(this tutorial)**

**To learn how to evaluate the performance of your Siamese Network model, just keep reading.**

**Introduction to Siamese Networks in Facial Recognition Systems**

In the first part of this series, we tried to understand how Siamese networks can be used to build effective facial recognition systems. Specifically, we discussed the various face recognition techniques and the difference between face identification and verification.

Furthermore, we discussed how verification can be used for identifying faces. We established that it is a much more efficient, scalable, and effective way of implementing face identification as it requires a simple similarity-based comparison approach at inference.

We recommend readers go through the first part of this series to better understand the concepts we will use in this post.

**Utilizing Siamese Networks for Face Verification**

In this tutorial, we will use our trained Siamese network as a verification system to recognize faces and evaluate their performance on the unseen test set.

**Overview of the Face Verification Pipeline with Siamese Networks**

Let us get a quick recap first of how the verification pipeline works and allows us to identify faces and how we can evaluate our pipeline on novel unseen faces at test time.

**Figure 1** shows the verification pipeline for Face Recognition. Let us discuss this in detail and understand how the performance evaluation can be implemented for such a set-up.

**Implementing Face Recognition and Verification**

Given that we want to identify people with `id-1021`

to `id-1024`

, we are given 1 image (or a few samples) of each person, which allows us to add the person to our face recognition database.

We use our model (shown as CNN (convolutional neural network) in **Figure 1**) to compute the feature embedding corresponding to each face in our database (i.e., , , , ) and store the embedding in our database as shown.

Now, given a novel unseen image from our test set (i.e., ), we compute its corresponding feature using our trained model as shown.

**The Role of Siamese Network Training in Enhancing Face Recognition Accuracy**

Note that our Siamese model has been trained so that the distance between the embedding of faces from the same person is lower than the distance between the embedding of faces from different people.

Given this fact, we can simply find the distance between the test image feature and the features in our face database (, , , ). Then, whichever feature has the minimum distance with our test feature is the identity of the test image.

For example, in **Figure 1**, the distance will be least compared to , , and . So we can simply identify the test face image with the `id`

of feature (i.e., `id-1021`

).

**Evaluating the Accuracy of Siamese Network-Based Face Recognition**

Once all of our test images have been assigned an `id`

using the process explained above, we can evaluate our model by comparing the predicted `id`

and the actual `id`

for a given face to check the correctness of our predictions.

**Configuring Your Development Environment**

To follow this guide, you need to have the `tensorflow`

and `sklearn`

libraries installed on your system.

Luckily, `tensorflow`

and `sklearn`

are pip-installable:

$ pip install tensorflow $ pip install scikit-learn

**If you need help configuring your development environment for OpenCV, we highly recommend that you read our **

**— it will have you up and running in minutes.**

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**Project Structure**

We first need to review our project directory structure.

├── crop_faces.py ├── face_crop_model │ ├── deploy.prototxt.txt │ └── res10_300x300_ssd_iter_140000.caffemodel ├── inference.py ├── pyimagesearch │ ├── config.py │ ├── dataset.py │ └── model.py └── train.py └── evaluate.py

In the previous tutorial, we discussed in detail the `train.py`

file, which implements the code to train our face recognition pipeline, and the `inference.py`

file, which helped us make predictions using our Siamese network based face recognition application.

In this tutorial, we will understand the concepts behind different evaluation metrics and delve deeper into the `evaluate.py`

file, which implements the code to evaluate our trained Siamese network based face recognition model.

**Face Recognition Model Evaluation: Understanding Precision, Recall, and F1 Score**

Now that we have understood the process of making predictions and evaluating our Siamese network based face recognition model, let us start building our evaluation pipeline.

**The Importance of a Diverse Test Set in Model Evaluation**

Note that to evaluate our model and get a sense of its performance, we need a test set that is large and diverse enough to output credible metric scores that reflect the ability of our model.

**Choosing Representative Subjects for Reliable Evaluation**

Most subjects in the face recognition dataset we use for this blog post have very few instances of images, which makes the test set very small and gives unreliable evaluation scores. Thus, we will split our dataset so that our test set contains subjects with multiple image instances per subject.

Specifically, we choose 5 subjects (i.e., `Arnold_Schwarzenegger`

, `Hans_Blix`

, `Recep_Tayyip_Erdogan`

, `Kofi_Annan`

, and `John_Kerry`

) which consists of a large number of image instances per subject for our test set. We keep the rest of the subjects as our train set. Note that these subjects have been randomly chosen and can be replaced with other subjects if required.

**Deep Dive into the Model Evaluation Process**

Now that we have set up our trained model, face database, and test data, let us discuss the concepts behind the different evaluation metrics that we will use to evaluate the performance of our face recognition model.

**Beyond Accuracy: Exploring Advanced Metrics for Model Evaluation**

The most common metric to evaluate a model is accuracy, which is simply the fraction of samples whose label was correctly predicted by the model. However, accuracy is a metric computed at the dataset level and can sometimes be misleading, especially when our data is a class imbalance; some classes contain fewer samples than others, and the number of samples of different classes is not of the same scale.

For example, consider a problem where we have `100`

samples where `90`

belong to `Class 1`

, and `10`

belong to `Class 2`

. Given that we have a trivial model that does not learn anything from the data and simply predicts that all samples belong to `Class 1`

, it gets an accuracy of 90% in this setup. Note that, even though the model did not learn anything about the underlying data and simply predicted `Class 1`

every time, it still got a high accuracy of 90%, which is misleading.

**Precision, Recall, and F1 Score: Understanding the Core Metrics**

Thus, to comprehensively understand and evaluate the predictions of our model on the test set, we need metrics that can quantify the performance of our model for each class or person in the test set. As discussed, we use a test set with `5`

subjects for this tutorial.

Let us consider a subject, say `Arnold_Schwarzenegger`

, and look at how our model can be evaluated for this given class. **Figure 2** shows the overview of the predictions we encounter from our trained model.

Given that we are analyzing predictions for a given subject or class (e.g., `Arnold_Schwarzenegger`

) in our test set, we view our model outputs as binary predictions, that is, `Arnold_Schwarzenegger`

(`positive class, label=1`

) and not `Arnold_Schwarzenegger`

(`negative class, label !=1`

).

Here, `True Positives`

refers to the samples assigned as positive by the model (i.e., `label_pred =1`

), and their ground-truth label was also positive (`label_gt=1`

). Similarly, `True Negatives`

refers to the samples assigned as negative by the model (i.e., `label_pred !=1`

), and their ground-truth label was also positive (`label_gt!=1`

).

Furthermore, `False Positives`

refers to the samples assigned as positive by the model (i.e., `label_pred =1`

), and their ground-truth label was negative (`label_gt!=1`

). Similarly, `False Negatives`

refers to the samples assigned as negative by the model (i.e., `label_pred !=1`

), and their ground-truth label was positive (`label_gt =1`

).

**Implementing Precision and Recall Calculations in Python**

Now that we have defined and segregated our samples into `True Positives`

, `True Negatives`

, `False Positives`

, and `False Negatives`

, let us try to use them to compute specific metrics to evaluate our model.

**Defining Key Evaluation Metrics: Precision and Recall**

`Precision`

can be defined as the fraction of samples where our model correctly predicted `label=1`

out of all samples where `label = 1`

was predicted.

Mathematically, this can be formulated as

`Precision = TP/TP+FP`

`Recall`

can be defined as the fraction of samples where our model predicted `label=0`

out of all samples where the `label_gt=0`

.

Mathematically, this can be formulated as

`Recall = TP/FN+TP`

**Introducing the F1 Score for Balanced Model Assessment**

Next, we define the F1 score, which is simply the harmonic mean of the precision and recall and gives us a holistic overview of the performance of our model by taking into account both the precision and recall abilities of the model.

`F1 = 2*precision*recall / precision+recall`

**Importing Essential Libraries for Siamese Network Evaluation**

# import the necessary packages from pyimagesearch.model import SiameseModel from matplotlib import pyplot as plt from pyimagesearch import config from sklearn.metrics import confusion_matrix from tensorflow import keras import tensorflow as tf import numpy as np import os

**Detailed Explanation of Metric Calculation in Python **

We start by importing the important packages on **Lines 2-9**, which include `SiameseModel`

(**Line 2**), the `matplotlib`

library for plotting visualizations (**Line 3**), the `config`

file (**Line 4**), and the `confusion_matrix`

function from the metrics module of `sklearn`

(**Line 5**).

Furthermore, we also import the `keras`

library (**Line 6**), `tensorflow`

library (**Line 7**), `numpy`

(**Line 8**), and `os`

module (**Line 9**) for various deep learning or matrix or manipulation functionalities, as always.

**Crafting a Custom Function for Precision, Recall, and F1 Score**

### define function to compute metrics def metrics(predictions, label_gt): TP_binary = np.logical_and(predictions, label_gt) FP_binary = np.logical_and(predictions, np.logical_not(label_gt)) TN_binary = np.logical_and(np.logical_not(predictions), np.logical_not(label_gt)) FN_binary = np.logical_and(np.logical_not(predictions), label_gt) TP = sum(TP_binary ) FP = sum(FP_binary ) TN = sum(TN_binary ) FN = sum(FN_binary ) precision = TP/(TP+FP) recall = TP/(FN+TP) F1 = (2*precision*recall)/(precision+recall) return precision, recall, F1

**Defining the Metrics Function for Model Evaluation**

We first define our `metrics`

function (**Lines 12-27**), which will be used to compute the values of the metrics to evaluate our trained Siamese network model. The function inputs `predictions`

from our trained model and the corresponding ground-truth label (i.e., `label_gt`

).

**Binary Classification Approach in Metrics Calculation**

Let us understand how we will compute the metrics we defined above for a given class. Given that we are analyzing predictions for a given subject, say, subject 1 or `Class 1`

(as discussed above), we will create two binary arrays.

One, whose entries state whether a sample in our test set belongs to `Class 1`

as per the model predictions, and the other, whose entries state whether a sample in our test set actually belongs to `Class 1`

as per ground truth.

Note that the `predictions`

input argument is the binary array stating whether the model predicts that a given sample in our test set belongs to the class under consideration (`1`

if yes, `0`

if no). Similarly, the `label_gt`

argument is a binary array stating whether the ground-truth label of a given sample in our test set belongs to the class under consideration (`1`

if yes, `0`

if no).

**Computing True Positives, Negatives, and False Metrics**

Now, we are ready to compute the true positives (`TP`

), false positives (`FP`

), true negatives (`TN`

), and false negatives (`FN`

).

We can compute the true positives by simply taking the logical AND operation of the binary predictions and `label_gt`

as shown on **Line 13** (since the output will be `1`

when the model prediction and their ground-truth label are both positive).

Similarly, we can compute the false positives as (predictions) AND (NOT `label_gt`

), the true negatives as (NOT predictions) AND (NOT `label_gt`

), and the false negatives as (NOT `predictions`

) AND (`label_gt`

), as shown on **Lines 14-16**.

Finally, we compute the total number of `TP`

, `FP`

, `TN`

, and `FN`

by simply summing over the computed binary arrays `TP_binary`

, `FP_binary`

, `TN_binary`

, and `FN_binary`

, as shown on **Lines 18-21**.

**Calculating Precision, Recall, and F1 Score in Practice**

We are now ready to compute our precision, recall, and F1 score. As shown on **Lines 23-25**, we calculate these metrics using the formulas that we discussed above and return their values on **Line 27**.

**Loading and Configuring the Siamese Model for Inference**

modelPath = config.MODEL_PATH ### Loading pre-trained siamese model for inference print(f"[INFO] loading the siamese network from {modelPath}...") siameseNetwork = keras.models.load_model(filepath=modelPath) siameseModel = SiameseModel( siameseNetwork=siameseNetwork, margin=0.5, lossTracker=keras.metrics.Mean(name="loss"), )

**Loading the Trained Siamese Model for Evaluation**

Now that we have completed the definition of our `metrics`

function, let us load our trained Siamese model and evaluate its performance.

On **Line 29**, we get our `modelPath`

, where we save our trained Siamese model after training. On **Line 33**, we loaded our trained Siamese model, which we had saved at the `modelPath`

location. As we had discussed in detail in the inference section of the previous tutorial, this can be done using the `load_model`

function of `keras`

, as shown.

**Initializing the Siamese Model for Data Analysis**

Next, we create our `siameseModel`

using the `SiameseModel`

class as we had done during inference in the previous tutorial.

**Setting Up a Data Pipeline for Effective Model Testing**

faceDatabasePath = 'cropped_face_database' img_height, img_width = config.IMAGE_SIZE print(f"[INFO] Setting-up Data Pipeline...") test_ds = tf.keras.utils.image_dataset_from_directory( config.TEST_DATASET, seed=123, image_size=(img_height, img_width), batch_size=1) face_ds = tf.keras.utils.image_dataset_from_directory( faceDatabasePath, seed=123, image_size=(img_height, img_width), batch_size=1)

**Creating a Robust Data Pipeline for Model Testing**

Now that we have loaded our trained model, let us create our data pipeline for conducting the evaluation. On **Lines 40 and 41**, we define the path to our face database (i.e., `faceDatabasePath`

) and get the image’s dimensions, respectively.

**Assembling the Test Dataset for Model Accuracy Assessment**

On **Lines 44-48**, we use the `tf.keras.utils.image_dataset_from_directory`

function to create our test dataset. This function takes as arguments the path to the test data (i.e., `config.TEST_DATASET`

), `seed`

to ensure reproducibility, the image’s dimensions to output, and the `batch_size`

as shown.

**Constructing a Face Database for Comprehensive Testing**

Similarly, on **Lines 50-54**, we use the `tf.keras.utils.image_dataset_from_directory`

function to create a dataset for our face database entries. This function takes as arguments the path to the `faceDatabasePath`

, used to ensure reproducibility, the image’s dimensions to output, and the `batch_size`

as shown.

**Preparing the Face Database and Test Dataset for Evaluation**

faces = [] faceLabels = [] for entry in face_ds: face, faceLabel = entry face_image = face/255 faces.append(face_image) faceLabels.append(faceLabel)

**Preparing Face Database Entries for Accurate Model Evaluation**

Now that we have created our data pipeline for evaluation, let us prepare our database entries. As discussed above, our face database contains 1 image corresponding to each of the 5 classes in our test set.

**Structuring Data for Siamese Model Evaluation**

We create two lists to store the faces in our database and their corresponding labels (i.e., `faces`

and `faceLabels`

). Next, we loop over the face or entries in our face database (**Line 59**), and for each `entry`

, we first get the corresponding `face`

and `faceLabel`

(**Line 60**). We then divide our face image by `255`

to normalize the pixels from `0-1`

. Finally, we append the `face`

and `faceLabel`

to the corresponding lists, as shown on **Lines 62 and 63**.

**Implementing Predictive Analysis with the Siamese Model**

print(f"[INFO] Making Predictions on Test Set...") predictions = [] labels = [] for batch in test_ds: batch_img, label = batch image = batch_img/255

**Making Predictions and Evaluating the Siamese Model**

Let us now make predictions with the help of our trained Siamese model.

**Conducting Predictive Testing on the Dataset**

We iterate over the batches in the test dataset (**Line 69**) and unpack the `batch`

to get the `batch_img`

and `label`

, as shown on **Line 70**. Next, we normalize the `batch_img`

by dividing the pixel values by `255`

.

**Calculating Distances for Facial Recognition**

pred_distances = []

We then create the preds list to store the predictions of our trained model (**Line 72**). Then, we iterate over the face images in our face database. For each anchor in our database, we use the `siameseModel`

to get the distances between the embeddings, as we did during the inference stage in our previous tutorial. Let us discuss this process in detail.

**Analyzing Embedding Distances for Accurate Identification**

for anchor in faces: (apDistance, anDistance) = siameseModel((image , anchor, image)) pred_distances.append(apDistance.numpy())

Note that the `siameseModel`

takes as input `3`

images and outputs the distances between the embeddings of the first and second images (i.e., `apDistance`

) and the first and the third images (i.e., `anDistance`

). Here, the `apDistance`

(**Line 75**) is the distance between the embeddings of our test image and the face in our face database (i.e., `anchor`

). Note that the `anDistance`

will be `0`

here as it is simply the distance between the embeddings of the same image.

We convert the `apDistance`

to `numpy`

and append it to our `pred_distances`

list (**Line 76**). Note that this implies that the `pred_distances`

list will contain the distance between the embeddings of the image in our test set and the embeddings of the `5`

anchors in our face database.

**Analyzing Distance-Based Predictions in Siamese Networks**

predictions.append(faceLabels[np.argmin(pred_distances)][0]) labels.append(label.numpy()[0])

Now that we have the distances of our test image from the embeddings of the `5`

anchors in our face database, we get the label corresponding to the anchors, which has the minimum distance, and append it to the `predictions`

list (**Line 77**). We also append the ground-truth label of the image in our `test_Set`

to the `labels`

list (**Line 78**).

**Preparing Data for Accuracy and Class-Wise Metric Evaluation**

labels = np.asarray(labels) predictions = np.asarray(predictions) print(f"[INFO] Evaluating the model...") ##Computing Metrics N = labels.shape[0] accuracy = (labels == predictions).sum() / N

Finally, we convert our `labels`

and `predictions`

lists to `numpy`

arrays to prepare them for computing the metrics (**Lines 81 and 82**).

On **Line 87**, we get the total number of samples in our test set (i.e., `N`

) and compute the `accuracy`

, as shown on **Line 88**.

**Calculating Class-Specific Metrics for In-Depth Analysis**

for pos_label in [0,1,2,3,4]: pred = (predictions == pos_label) label_gt = (labels == pos_label) precision, recall, F1_score = metrics(pred,label)

Furthermore, to compute the class-wise metrics as we had discussed above in this tutorial, we iterate through all unique class labels in our test set (**Line 90**) and compute the binary `pred`

and `label_gt`

arrays for each class and pass them to our `metrics`

function that we defined above (**Lines 91 and 92**).

This function outputs each class’s corresponding `precision`

, `recall`

, and `F1_score`

(**Line 94**).

With this, we complete computing the metrics for each class in our test set.

**Leveraging Confusion Matrix for Holistic Performance Insight**

Now that we have discussed in detail the concept behind precision and recall and understood how to compute them for each class in the face recognition problem, let us look at another concept that makes it easier to get a sense of the performance of our model and allows us to directly compute precision and recall for all classes at once making the process more efficient.

**Using Confusion Matrix for Comprehensive Model Evaluation**

The confusion matrix allows us to summarize the predictions of our recognition model and makes it easier to evaluate the holistic and class-wise performance of our trained system. Specifically, the confusion matrix simply conveys how our trained model classified the samples of a given ground-truth class.

**Figure 3** shows an example of a typical confusion matrix for three classes and depicts how the different rows and columns of the matrix are filled. Each entry in the th row and th column of the confusion matrix denotes the number of samples with the ground-truth label as th class and predicted label as th class as shown in the figure.

**Implementing Confusion Matrix in Python for Siamese Model**

Let’s go ahead and compute the confusion matrix for our trained Siamese model to understand this better.

**Direct Calculation of Recall and Precision from Confusion Matrix**

cm = confusion_matrix(labels, predictions) recall = np.diag(cm) / np.sum(cm, axis = 1) precision = np.diag(cm) / np.sum(cm, axis = 0)

Note that the confusion matrix can be simply computed using the `confusion_matrix`

function from `sklearn`

(**Line 96**). Now that we have the confusion matrix, we can easily directly compute the `recall`

and `precision`

for all classes together (**Lines 97 and 98**).

Notice that the `precision`

and `recall`

computed from the confusion matrix match the ones we computed before using the mathematical formula.

**Deriving Overall Precision, Recall, and F1 Score**

recallOverall = np.mean(recall) precisionOverall = np.mean(precision) F1_overall = 2*recallOverall*precisionOverall/(recallOverall+precisionOverall)

Finally, to get the overall `precision`

and `recall`

, we simply average the `precision`

and `recall`

values over all classes (**Lines 100 and 101**).

We can also compute the overall F1 score by taking the harmonic mean of our computed values (**Line 102**).

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**Summary and Conclusion**

In this tutorial, we discussed how to evaluate our trained Siamese network based face recognition model using Keras and TensorFlow.

Specifically, we tried to understand how we could evaluate a face verification pipeline during the inference stage and delved deeper into the concepts behind different dataset level and class level metrics which can be used to evaluate the performance of our trained model.

Furthermore, we discussed and implemented the code to build our evaluation pipeline and discussed the confusion matrix, which allows us to get a holistic sense of the predictions of the model and its performance.

**Citation Information**

**Chandhok, S. **“Evaluating Siamese Network Accuracy (F1 Score, Precision, and Recall) with Keras and TensorFlow,” *PyImageSearch*, P. Chugh, A. R. Gosthipaty, S. Huot, K. Kidriavsteva, and R. Raha, eds., 2024, https://pyimg.co/dq1w5

@incollection{Chandhok_2024_Evaluating_Siamese, author = {Shivam Chandhok}, title = {Evaluating Siamese Network Accuracy (F1 Score, Precision, and Recall) with Keras and TensorFlow}, booktitle = {PyImageSearch}, editor = {Puneet Chugh and Aritra Roy Gosthipaty and Susan Huot and Kseniia Kidriavsteva and Ritwik Raha}, year = {2024}, url = {https://pyimg.co/dq1w5}, }

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## Comment section

Hey, Adrian Rosebrock here, author and creator of PyImageSearch. While I love hearing from readers, a couple years ago I made the tough decision to no longer offer 1:1 help over blog post comments.

At the time I was receiving 200+ emails per day and another 100+ blog post comments. I simply did not have the time to moderate and respond to them all, and the sheer volume of requests was taking a toll on me.

Instead, my goal is to

do the most goodfor the computer vision, deep learning, and OpenCV community at large by focusing my time on authoring high-quality blog posts, tutorials, and books/courses.If you need help learning computer vision and deep learning, I suggest you refer to my full catalog of books and courses— they have helped tens of thousands of developers, students, and researchersjust like yourselflearn Computer Vision, Deep Learning, and OpenCV.Click here to browse my full catalog.