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YOLOv12 Custom Object Detection Tutorial: Brain Tumor Detection

• 0:00:02 Introduction: The video tutorial will cover training a YOLOv12 model for custom object detection, specifically focusing on a brain tumor dataset.

• 0:00:54 Data Collection and Annotation: The first steps are collecting images of the objects and then annotating them (drawing bounding boxes and labeling). LabelImg tool will be used for this.

• 0:01:41 Annotation Format: YOLO models require annotations in a specific .txt format, with one text file per image, and the text file name corresponding to the image name.

• 0:03:10 Dataset Used: The presenter is using a brain tumor detection dataset, with "positive" (tumor present) and "negative" (no tumor) classes.

• 0:03:31 data.yaml File: This file contains the paths to the training and validation images, and the class names, informing the model about the data. The link will be located in the description.

• 0:04:59 Dataset Access: The dataset can be downloaded from a link provided in the data.yaml file (and will also be in the video description).

• 0:05:13 Training Setup: The tutorial uses a Colab notebook for the coding process.

• 0:06:02 GPU Setup: Instructions are provided for setting up the Colab environment to use a GPU.

• 0:06:15 Mounting Drive: The Google Drive is mounted to access the dataset, which is stored in a folder on the presenter's Drive.

• 0:06:42 Cloning YOLOv12 Repo: The official YOLOv12 GitHub repository is cloned to obtain the necessary code and files.

• 0:07:33 Installing Requirements: Dependencies are installed, such as a flash attention module, and requirements.txt file is used to get everything running.

• 0:09:35 Model Testing: The model's functionality is tested before custom training.

• 0:10:08 Custom Training: The model is fine-tuned on the custom brain tumor dataset using a pre-trained YOLOv12 model (.pt file). The model.train() function is used, specifying the data.yaml path, epochs, batch size, image size, and GPU usage.

• 0:11:59 Trained Model: The training process saves the best performing model weights (best.pt) and the last saved weights (last.pt).

• 0:12:21 Testing the Trained Model: The trained model (best.pt) is used to perform inference on test images (bt1.jpg and bt2.jpg). The presenter shows the model detecting (or not detecting) the tumor in the images.

• 0:13:11 Conclusion: The presenter summarizes the process and offers the code link in the description.