README.md

metadata

library_name: keras
tags:
  - image-classification
  - spam-detectio
metrics:
  - accuracy
base_model:
  - keras-io/VGG19

Image Spam Detection with VGG19

This is a model for detecting spam in images using transfer learning, with a VGG19 base model fine-tuned on the Image Spam Hunter Dataset.

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Model Description

This model is built for binary classification, detecting whether an image is spam or not. The VGG19 architecture was used as the base model for transfer learning, leveraging pre-trained weights from ImageNet. The final layers were customised to include a GlobalAveragePooling layer, followed by fully connected layers, and a softmax activation for classification.

• Base Model: VGG19
• Task: Binary classification (Spam / Not Spam)
• Framework: Keras (TensorFlow backend)

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Training and Evaluation Data

Dataset

The model was trained and evaluated on the Image Spam Hunter Dataset, a publicly available dataset that contains spam and non-spam images:

• Training set: 1731 images
• Validation set: 1385 images

You can find more about the dataset here.

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Training Procedure

Preprocessing

• Images were resized to 256x256.
• Normalisation was applied to scale pixel values between 0 and 1.

Optimizer

• Optimizer: Adam
• Learning Rate: 0.001

Training Parameters

• Batch Size: 128
• Epochs: 50

Loss Function

• Binary Cross-Entropy Loss

Augmentations

• Random augmentation using Keras CV RandAugment layer.

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Training Metrics

Below is a summary of the model's performance over the training process:

FN	FP	TN	TP	accuracy	loss	val_FN	val_FP	val_TN	val_TP	val_accuracy	val_loss
187	140	499	559	0.763899	1.93435	19	2	169	156	0.939306	0.378412
16	35	604	730	0.963177	0.242954	6	3	168	169	0.973988	0.186523
29	19	620	717	0.965343	0.174301	3	12	159	172	0.956647	0.149353
17	26	613	729	0.968953	0.116907	3	7	164	172	0.971098	0.0973678
14	17	622	732	0.977617	0.0807728	2	12	159	173	0.959538	0.0991694
13	18	621	733	0.977617	0.0713564	3	6	165	172	0.973988	0.0604228
10	17	622	736	0.980505	0.0604733	3	5	166	172	0.976879	0.0584633
9	13	626	737	0.984116	0.0478682	4	4	167	171	0.976879	0.0745383
5	13	626	741	0.987004	0.0376507	3	7	164	172	0.971098	0.0773135
7	6	633	739	0.990614	0.0248621	3	2	169	172	0.985549	0.0628484
7	10	629	739	0.987726	0.0298408	2	2	169	173	0.988439	0.0531744
4	10	629	742	0.989892	0.0242986	3	2	169	172	0.985549	0.0675473
6	7	632	740	0.990614	0.0326418	2	3	168	173	0.985549	0.0671533
6	9	630	740	0.98917	0.0279278	2	5	166	173	0.979769	0.06897
7	7	632	739	0.989892	0.0244297	2	3	168	173	0.985549	0.0550619
5	4	635	741	0.993502	0.0228877	3	2	169	172	0.985549	0.0558347
4	10	629	742	0.989892	0.0277857	4	0	171	171	0.988439	0.0401906
2	7	632	744	0.993502	0.0193213	9	1	170	166	0.971098	0.0959367
7	3	636	739	0.99278	0.0151094	4	6	165	171	0.971098	0.0873069
7	3	636	739	0.99278	0.017344	1	6	165	174	0.979769	0.0964724
5	4	635	741	0.993502	0.024251	1	4	167	174	0.985549	0.0580115
3	9	630	743	0.991336	0.0207464	1	3	168	174	0.988439	0.0510422
2	6	633	744	0.994224	0.0122992	1	1	170	174	0.99422	0.0439666
3	2	637	743	0.99639	0.0105987	4	2	169	171	0.982659	0.0502425
3	6	633	743	0.993502	0.0164827	6	1	170	169	0.979769	0.0865423
5	7	632	741	0.991336	0.0245302	6	3	168	169	0.973988	0.0918282
9	8	631	737	0.987726	0.0296158	5	1	170	170	0.982659	0.0991676

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Model Plot

The model architecture can be visualised in the image below:

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Validation Accuracy

The final validation accuracy achieved by the model was 98%.

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How to Use the Model

Loading the Model

To load this model in your application, use the following:

from tensorflow.keras.models import load_model
model = load_model("path/to/saved_model")