Are you ready to embark on an adventure of real-time object detection using YOLOv2 with Keras? Buckle up! In this blog, we’ll walk you through the setup process, train various object detectors, and troubleshoot any bumps along the way.

Prerequisites

• You will need Python 2.7 or 3.6.
• Ensure you have Keras version 2.0.8 and imgaug installed.

Step 1: Data Preparation

To kick things off, you need to download the Raccoon dataset that we will use for training our object detector. Here’s how:

• Download the Raccoon dataset from here.
• Organize the dataset into these four folders:
• train_image_folder: Contains the training images.
• train_annot_folder: Contains the training annotations in VOC format.
• valid_image_folder: Contains the validation images.
• valid_annot_folder: Contains the validation annotations in VOC format.
• Note: Maintain a one-to-one correspondence between image and annotation file names.

Step 2: Configuration File Setup

The next step involves editing a configuration file, a JSON file that holds important parameters for our model:

{
    "model": {
        "architecture": "Full Yolo", 
        "input_size": 416,
        "anchors": [0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828],
        "max_box_per_image": 10,
        "labels": ["raccoon"],
        "train": {
            "train_image_folder": "homeandydata/raccoon_dataset/images",
            "train_annot_folder": "homeandydata/raccoon_dataset/anns",
            "train_times": 10,
            "pretrained_weights": "",
            "batch_size": 16,
            "learning_rate": 1e-4,
            "nb_epoch": 50,
            "warmup_epochs": 3,
            "object_scale": 5.0,
            "no_object_scale": 1.0,
            "coord_scale": 1.0,
            "class_scale": 1.0,
            "debug": true
        },
        "valid": {
            "valid_image_folder": "",
            "valid_annot_folder": "",
            "valid_times": 1
        }
    }
}

Here, the architecture defines how the YOLO model is structured, and each parameter acts like a setting on a gaming console – fine-tuning the experience for optimal performance.

Step 3: Managing Pre-trained Weights

To train your network, download pre-trained weights at this link. Place these weights in the root folder of your project. Remember, without these, the code will not run!

Step 4: Training Your Model

Once the data and configuration are ready, it’s time for the main event!

• Run the training process with the following command:

python train.py -c config.json

• Your trained model’s weights will save as best_weights.h5 after training completes.

Step 5: Perform Detection

Finally, it’s time to see what your model can do!

• Run the detection with:

python predict.py -c config.json -w path_to_best_weights.h5 -i path_to_image_or_video

• The output will include bounding boxes around detected objects in the specified image or video.

Troubleshooting

In case you encounter issues during your implementation, consider the following suggestions:

• Double-check your folder structure; all paths must be correct.
• Ensure the pre-trained weights are properly downloaded and placed in the right directory.
• If you run into errors regarding mismatched shapes, revisit your configuration file and input sizes.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.