datasets/detect/coco/

[giscus[bot]]

datasets/detect/coco/

Learn how COCO, a leading dataset for object detection and segmentation, integrates with Ultralytics. Discover ways to use it for training YOLO models.

https://docs.ultralytics.com/datasets/detect/coco/

[FRAOTIAC]

Thanks for the detailed instructions. Could you please clarify the exact directory structure for the train, val, and annotations folders? Specifically, where should these be placed within the dataset root directory?

[pderrenger]

Hello!

For setting up the COCO dataset with the correct directory structure, here's a concise guide:

1. Dataset Root Directory: This is your main folder where the dataset will reside, typically named coco or similar.
2. Train, Val, and Test Directories: Inside the root directory, you should have separate subdirectories for train2017, val2017, and test2017. Each of these will contain the respective images.
3. Annotations Folder: This should also be directly inside the root directory. It will contain the annotation files like instances_train2017.json, instances_val2017.json, etc.

Here’s how it should look:

coco/
├── annotations/              # Annotation files
│   ├── instances_train2017.json
│   ├── instances_val2017.json
│   └── ...
├── train2017/                # Training images
├── val2017/                  # Validation images
└── test2017/                 # Test images (optional)

Make sure the paths in your coco.yaml file correctly point to these directories. If you have any more questions or need further assistance, feel free to ask!

[casperking666]

where do you put the coco.yaml file though?

[pderrenger]

Hi there!

Great question! The coco.yaml file should be placed in a directory where your training script can easily access it. Typically, you would place it in the root directory of your project or in a dedicated configs or datasets folder within your project structure.

Here’s an example of how your project directory might look:

my_yolo_project/
├── coco.yaml
├── datasets/
│   ├── coco/
│   │   ├── annotations/
│   │   │   ├── instances_train2017.json
│   │   │   ├── instances_val2017.json
│   │   │   └── ...
│   │   ├── train2017/
│   │   ├── val2017/
│   │   └── test2017/
├── models/
│   ├── yolov8n.pt
│   └── ...
├── scripts/
│   ├── train.py
│   └── ...
└── ...

In your training script, you would reference the coco.yaml file like this:

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="coco.yaml", epochs=100, imgsz=640)

Or via the command line:

yolo detect train data=coco.yaml model=yolov8n.pt epochs=100 imgsz=640

This setup ensures that the coco.yaml file is easily accessible and correctly referenced in your training scripts.

If you have any more questions or need further assistance, feel free to ask! 😊

[aasharma90]

Hello,

What is the pre-trained model yolov8n.pt trained on? Since if it's already COCO dataset, I wonder why the new training is required.

[glenn-jocher]

Hello,

Great question! The pre-trained model yolov8n.pt is indeed trained on the COCO dataset. This means it has already learned to detect the 80 object categories included in COCO, such as people, cars, animals, and more.

However, there are several reasons why you might want to retrain or fine-tune this model:

1. Custom Dataset: If you have a custom dataset with different object categories or specific variations of the COCO categories, retraining the model will help it adapt to your specific data.

2. Improved Performance: Fine-tuning the model on your dataset can improve its performance, especially if your dataset has different characteristics (e.g., different lighting conditions, object sizes, or backgrounds) compared to the original COCO dataset.

3. Additional Classes: If you need to detect additional classes that are not included in the COCO dataset, you will need to retrain the model with your extended dataset.

4. Domain Adaptation: If your application involves a specific domain (e.g., medical imaging, industrial inspection), retraining the model on domain-specific data can significantly enhance its accuracy and reliability.

Here’s a quick example of how you can fine-tune the yolov8n.pt model on your custom dataset using Python:

from ultralytics import YOLO

# Load the pre-trained model
model = YOLO("yolov8n.pt")

# Train the model on your custom dataset
results = model.train(data="path/to/your/custom_dataset.yaml", epochs=100, imgsz=640)

And using the CLI:

yolo detect train data=path/to/your/custom_dataset.yaml model=yolov8n.pt epochs=100 imgsz=640

For more details on the COCO dataset and how to use it, you can refer to our COCO Dataset Documentation.

If you have any more questions or need further assistance, feel free to ask! 😊

[aasharma90]

Hi @glenn-jocher

Thanks for your detailed explanation and reply.

I understand the fine-tuning requirement for a custom dataset, but my question was about the fine-tuning part mentioned for the COCO dataset itself in the Train Example segment above.

Never mind, I think I have my answers. Thanks so much again :)

[glenn-jocher]

Hi @aasharma90,

Thank you for your follow-up! I appreciate your understanding regarding the fine-tuning process.

To clarify, the Train Example segment for the COCO dataset is primarily included to demonstrate how to train a YOLO model from scratch or fine-tune it, even if it has already been pre-trained on COCO. This can be useful for several reasons:

1. Hyperparameter Tuning: Fine-tuning allows you to adjust hyperparameters specific to your use case, potentially improving model performance.
2. Extended Training: Additional training can help the model refine its weights and improve accuracy, especially if you have more computational resources or time.
3. Custom Augmentations: You might want to apply custom data augmentations that are more suited to your specific application or environment.

If you have any more questions or need further assistance, feel free to ask! 😊

[konstantinos-p]

It is very unclear from your docs how to finetune your models. In particular, I would expect an example in the quickstart for the case of finetuning on a custom dataset. It is still unclear to me what should be the format of the labels of the finetuning dataset. I've seen some answers in the comments about the labels being "like COCO" but what does that even mean?

[konstantinos-p]

Ok, I've found the relevant page in the docs. I still think that it takes way to long to reach this page. Please consider adding some link in the quickstart.

[pderrenger]

@konstantinos-p thank you for your feedback! We appreciate your suggestion to make the documentation more accessible, especially regarding finetuning on custom datasets.

To clarify, finetuning a YOLO model on a custom dataset involves a few key steps:

1. Dataset Preparation: Your custom dataset should be formatted similarly to the COCO dataset. This means having images and corresponding annotation files. The annotations should be in JSON format, containing bounding boxes, segmentation masks, or keypoints, depending on your task.

2. Dataset YAML Configuration: Create a YAML file that specifies the paths to your training, validation, and test datasets, as well as the class names. Here's an example of what this might look like:

   path: ../datasets/my_custom_dataset
   train: images/train
   val: images/val
   test: images/test
   
   # Classes
   names:
     0: class1
     1: class2
     ...

3. Training the Model: Use the train command to start training your model on the custom dataset. Here’s an example in Python and CLI:

   from ultralytics import YOLO
   
   # Load a pretrained model
   model = YOLO("yolov8n.pt")
   
   # Train the model on your custom dataset
   results = model.train(data="path/to/your_custom_dataset.yaml", epochs=100, imgsz=640)

   yolo detect train data=path/to/your_custom_dataset.yaml model=yolov8n.pt epochs=100 imgsz=640

For more detailed information on the COCO dataset format and how to structure your custom dataset, please refer to our COCO Dataset Documentation.

We will consider your suggestion to add more direct links and examples in the quickstart guide to improve accessibility. Thank you for helping us enhance our documentation! If you have any more questions or need further assistance, feel free to ask. 😊

[TanWeeKang]

Hi, i used cvat to annotate my images and saved it as coco format. Then i need to convert it to yolov8 format for training. My question is what structure is the dataset format after converting to yolov8 format? From what i see, there will be images, annotations, labels, and yaml file. I dont understand what is that labels file, is it necessary to have in order to train the dataset using yolov8? Can you explain to me what is the labels file?

[pderrenger]

@TanWeeKang hi there!

Great to hear that you're working with CVAT and YOLOv8! When converting your COCO annotations to the YOLOv8 format, the dataset structure will indeed include images, annotations, labels, and a YAML file. Here's a brief overview of each component:

1. Images: This folder contains all the images used for training, validation, and testing.
2. Annotations: This folder typically holds the original COCO annotations in JSON format.
3. Labels: This is a crucial component for YOLO training. The labels folder contains text files where each file corresponds to an image and includes the bounding box coordinates and class labels in YOLO format.
4. YAML File: This file (e.g., coco.yaml) defines the dataset configuration, including paths to the images and labels, and the class names.

Understanding the Labels File

The labels file is essential for training YOLO models. Each text file in the labels directory corresponds to an image and contains lines of annotations in the following format:

class_id x_center y_center width height

• class_id: The class index of the object.
• x_center, y_center: The normalized center coordinates of the bounding box (values between 0 and 1).
• width, height: The normalized width and height of the bounding box (values between 0 and 1).

Example

For an image image1.jpg, the corresponding label file image1.txt might look like this:

0 0.5 0.5 0.2 0.4
1 0.3 0.7 0.1 0.2

This indicates two objects: one of class 0 and another of class 1, with their respective bounding box coordinates.

Conversion Tools

To convert COCO annotations to YOLO format, you can use various tools and scripts available online. One such tool is the coco2yolo script, which can help automate this process.

Training Example

Once your dataset is structured correctly, you can train your YOLOv8 model using the following command:

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="coco.yaml", epochs=100, imgsz=640)

For more detailed information, you can refer to the Ultralytics COCO Dataset Documentation.

If you have any further questions or need additional assistance, feel free to ask. Happy training! 🚀