DenseNet and Image Classification

DenseNet (Densely Connected Convolutional Networks) is a deep learning architecture designed to address the vanishing gradient problem and improve feature reuse in convolutional neural networks. Introduced by Gao Huang and his team in 2017, DenseNet enhances traditional convolutional networks by connecting each layer to every other layer in a feed-forward fashion. This means that each layer receives inputs from all preceding layers and passes its own feature maps to all subsequent layers, creating a dense connectivity pattern.

This architecture leads to improved gradient flow, more efficient feature reuse, and a reduction in the number of parameters compared to traditional networks. DenseNet models have achieved state-of-the-art performance on various benchmark datasets and have become popular for tasks such as image classification and object detection.

Image Classification

Image classification is a fundamental problem in computer vision where the goal is to assign a label or category to an image based on its content. This task is critical for a variety of applications, including medical imaging, autonomous vehicles, content-based image retrieval, and social media tagging.

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Getting Started

Clone the Repository

To get started with this project, clone the repository using the following command:

git clone https://github.com/TruongNV-hut/AIcandy_DenseNet_ImageClassification_mexgtkug.git

Install Dependencies

Before running the scripts, you need to install the required libraries. You can do this using pip:

pip install -r requirements.txt

Training the Model

To train the model, use the following command:

python aicandy_densenet_train_sgxbapee.py --data_dir ../dataset --num_epochs 10 --batch_size 16 --model_path aicandy_model_out_ddmalncc/aicandy_model_pth_silsegko.pth

Testing the Model

After training, you can test the model using:

python aicandy_densenet_test_beyugdam.py --image_path ../aicandy_true_dog.jpg --model_path aicandy_model_out_ddmalncc/aicandy_model_pth_silsegko.pth

Converting to ONNX Format

To convert the model to ONNX format, run:

python aicandy_convert_to_onnx_dvicqkqc.py --model_path aicandy_model_out_ddmalncc/aicandy_model_pth_silsegko.pth --output_path aicandy_model_out_ddmalncc/aicandy_model_onnx_ptspsbyh.onnx

More Information

To learn more about this project, see here.

To learn more about knowledge and real-world projects on Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), visit the website aicandy.vn.

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