この課題では、ディープラーニングを用いた**物体検出**の世界に踏み込みます。まず、**Keras**を使って**独自の物体検出モデル**を一から構築します。その後、**事前学習済みのYOLOv8モデル**を読み込み、同じデータセットに適用します。

この過程で、以下を行います：

- シンプルなKerasベースの物体検出器の学習
- 同じデータで学習したYOLOv8モデルの読み込みと予測実行
- 実際の検証画像での性能評価
- 結果の比較と、独自モデルと最先端モデルの差の理解

ノートブックの途中で、なぜ検出モデルを一から構築することが制約となるのかを考察し、実用的な応用における**転移学習**の重要性について簡単に触れます。

### [Colab Object Detection Project](https://colab.research.google.com/drive/1S9TTP01SHG20Ugm-hYuoCdqvq-tqUr36?usp=sharing)



データセットをダウンロード

コンピュータビジョンの包括的な入門講座であり、機械による視覚データの認識と解釈に焦点を当てています。画像の前処理、特徴抽出、物体検出、現代のビジョンシステムで使用されるディープラーニング技術を網羅します。

Computer vision enables machines to interpret and analyze visual data, mimicking human perception. This section covers the basics of image representation, color models, and mathematical foundations essential for understanding how computers process images. You'll explore real-world applications, from autonomous vehicles to medical imaging, and learn how Computer vision integrates with AI and machine learning. 

OpenCV is a powerful library for image manipulation and computer vision tasks. This section covers essential techniques like image filtering, transformations, edge detection, and segmentation. You'll learn how to perform blurring, thresholding, contour detection, and feature extraction to enhance and analyze images efficiently.

CNNs process visual data using convolution, pooling, and activation layers to extract features for tasks like image classification and object detection. Key components include padding, convolution for feature extraction, pooling for complexity reduction, and activation for non-linearity. Popular architectures like AlexNet, VGG, and ResNet power AI in healthcare, autonomy, and security.

Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image. Unlike image classification, which assigns a single label to an entire image, object detection not only classifies objects but also determines their positions using bounding boxes. This section covers key techniques and algorithms used in object detection, ranging from traditional methods to deep learning-based approaches like YOLO and U-Net.

Computer vision has significantly advanced over the years, shifting from basic image processing methods to complex deep learning techniques. This section delves into the latest innovations in computer vision, focusing on transfer learning, facial recognition, and image generation. We will explore the benefits of pre-trained models on performance, the principles of facial recognition technology, and the way AI creates images through deep learning.

チャレンジ：カスタムモデルとYOLOによる物体検出

課題

Colab Object Detection Project