COCO Dataset

Hello,

Great question! The pre-trained model yolo26n.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 yolo26n.pt model on your custom dataset using Python:

from ultralytics import YOLO

# Load the pre-trained model
model = YOLO("yolo26n.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=yolo26n.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! 😊

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 :)

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! 😊

I have the same question. If retraining is for domain adaptation and learning new categories, the knowledge from learned model from COCO dataset will be catastrophic forgetting. This means it is the same between retraining with pretrained network and training from scratch.

Thank you for your insights. While catastrophic forgetting is a valid concern, fine-tuning with pre-trained weights often accelerates convergence and improves performance, especially when the new dataset shares similarities with COCO. Training from scratch may be suitable for entirely different domains, but pre-trained models retain valuable feature representations that are not fully overwritten during fine-tuning. Let me know if you'd like more resources or examples to explore this further.

So right now I need to run YOLO on real world application, but I only have coco dataset. Is it good if I train on coco and test on my application?

Running YOLO on real-world applications after training on the COCO dataset can work well if your application involves detecting objects similar to COCO's 80 categories. However, for domain-specific or unseen objects, fine-tuning on a custom dataset is recommended to improve performance. Let me know if you'd like guidance on fine-tuning.

Yes. Could you please give me the fine-tuning guidance?

To fine-tune the model for your application, load the pretrained model (e.g., yolo26n.pt) and train it on your dataset using the train mode. Ensure your dataset is formatted correctly (e.g., COCO format or YAML-configured). For example:

from ultralytics import YOLO

model = YOLO("yolo26n.pt")  # Load the pretrained model
results = model.train(data="your_dataset.yaml", epochs=50, imgsz=640)

This process adapts the pretrained weights to your specific use case. For more, refer to the Ultralytics training documentation. Let me know if you need further assistance!

@xd775 YOLO does not read COCO JSON annotations directly. The provided coco.yaml downloads COCO already converted to YOLO format (images plus .txt label files), so no JSON paths are needed in the YAML. For your own COCO-format JSON, convert it to YOLO labels first with ultralytics.data.converter.convert_coco, then point your dataset YAML at the resulting images/ and labels/ folders, as shown in the COCO dataset documentation.

To add a new class like "Goat" to the COCO dataset, you'll need to modify the coco.yaml file by adding the class under the names section with the desired ID. Additionally, ensure your dataset includes the corresponding labels and annotations for this new class. For detailed guidance on dataset preparation, please refer to the Ultralytics documentation.

I have added to goat and deer into coco.yaml file also have added them in annotated files both instances_train2017.json and instances_val2017.json

{
        "supercategory": "animal",
        "id": 91,
        "name": "goat"
    },
    {
        "supercategory": "animal",
        "id": 92,
        "name": "deer"
    },

this is a python code to check Classes in Training JSON: ID: 91, Name: goat, Supercategory: animal ID: 92, Name: deer, Supercategory: animal

Classes in Validation JSON: ID: 91, Name: goat, Supercategory: animal ID: 92, Name: deer, Supercategory: animal

Classes in COCO YAML: ID: 91, Name: goat ID: 92, Name: deer

Additional Information for Goat (ID 91): Annotation for ID 91: BBox: [100, 200, 50, 50], Area: 2500 Annotation for ID 91: BBox: [30.5, 65.3, 30.0, 30.0], Area: 2000.3

Additional Information for Deer (ID 92): Annotation for ID 92: BBox: [150, 250, 60, 60], Area: 3600 Annotation for ID 92: BBox: [45.0, 30.5, 35.5, 40.0], Area: 2500.5

after all these i cant see goat when i test and check my model. as 91: goat

can u please help me? if u want i give give files so u can have a llok is possible

i want to use coco.yaml file class as well as only to add goat in there nothing else

To add "Goat" to the COCO dataset, ensure you've updated both the coco.yaml file and the YOLO-format .txt label files correctly. If the class isn't appearing during testing, double-check that the model is trained with the updated dataset and verify the annotations are correctly formatted. If issues persist, consider reviewing the dataset preparation steps or consulting the Ultralytics documentation for further guidance.

Hello glenn,

thanks for your reply.

I have change the annotation files instances_train2017.json and instances_val2017.json to assign annotation filed for goat 80 to following fileds

Annotation ID: Image ID: Segmentation: Area: Iscrowd: Category ID:

also add 80: goat at coco.yaml file

now what i should do next to recognize its goat by coco.yaml or yolov5? thanks

I have change the annotation files instances_train2017.json and instances_val2017.json to assign annotation filed for goat 80 to following fileds

Annotation ID: Image ID: Segmentation: Area: Iscrowd: Category ID:

this assigned is done using pythong code. is it ok

its really too hard to incorporate goat into existing coco.yaml file. i have spend long time to do but no luck. Is there any way to add goat only one object into coco.yaml. coz i have to use defauly cooc.yaml file no custom file. but its really hard. Is there a way out?

from ultralytics import YOLO

Load a pretrained model

model = YOLO("yolov5x.pt")

Train the model with your custom data

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

this code is not works for me. i m tired. please help

I'm sorry to hear you're having trouble. Please ensure your dataset paths and annotations are correctly set up in the coco.yaml file and that your custom data is formatted correctly. If issues persist, reviewing the Ultralytics documentation or community forums may provide additional insights.

I have seen few google documentation as well as Ultralytics documentation but everywhere is talk about how to set or train your owned object detection. I can't find anywhere how to include my object into coco.yaml data set. can u give me link for that? thx

To add a new class like "Goat" to the COCO dataset, ensure you've updated the coco.yaml file and the YOLO-format .txt label files correctly. Unfortunately, there isn't a specific guide for adding classes to COCO, but you can refer to general dataset preparation tutorials. If you encounter issues, consider checking community forums for additional support.

If you encounter issues, consider checking community forums for additional support.

can u recommend few?

For community support, consider exploring forums like Stack Overflow, Reddit's r/computervision, or the Ultralytics YOLO GitHub Discussions page. These platforms can provide valuable insights and assistance from other users and developers.

@dordanin to train on only 5 classes from COCO, use the classes training argument with the COCO class ids you want, for example model.train(data="coco.yaml", classes=[0, 1, 2, 3, 5]), or yolo train data=coco.yaml classes=0,1,2,3,5. Editing only the names in coco.yaml would not work because the label files still use the original class ids. For more details, visit COCO Dataset Documentation.

This does not work anymore, the training script labels the images as corrupt and are ignored during training.

@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: 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. Ultralytics includes one: from ultralytics.data.converter import convert_coco; convert_coco(labels_dir="path/to/coco/annotations/"), which writes YOLO labels for you.

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("yolo26n.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! 🚀

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.

@konstantinos-p thank you for your feedback! Finetuning a YOLO model on a custom dataset involves a few key steps:

1. Dataset Preparation: Labels must be in YOLO format: one *.txt file per image, with one row per object in class x_center y_center width height format (normalized 0-1, zero-indexed classes). Images and labels go in sibling images/ and labels/ directories. If your annotations are in COCO JSON format, you can convert them with ultralytics.data.converter.convert_coco. See the dataset format guide.

2. Dataset YAML Configuration: Create a YAML file that specifies the paths to your training, validation, and test images, as well as the class names:

   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:

   from ultralytics import YOLO
   
   # Load a pretrained model
   model = YOLO("yolo26n.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=yolo26n.pt epochs=100 imgsz=640

For more on the COCO dataset itself, refer to our COCO Dataset Documentation. If you have any more questions, feel free to ask. 😊

Hello!

For training, the COCO dataset uses this layout under the dataset root (see coco.yaml):

coco/
├── images/
│   ├── train2017/        # Training images
│   ├── val2017/          # Validation images
│   └── test2017/         # Test images (optional)
├── labels/
│   ├── train2017/        # YOLO-format .txt labels, one per image
│   └── val2017/
├── train2017.txt         # List of training image paths
├── val2017.txt           # List of validation image paths
└── test-dev2017.txt      # List of test image paths (optional)

The original COCO annotations/*.json files are only needed if you convert the labels yourself with convert_coco. Training with data=coco.yaml downloads and prepares the dataset automatically. If you have any more questions, feel free to ask! 😊

where do you put the coco.yaml file though?

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/
│   ├── yolo26n.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("yolo26n.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=yolo26n.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! 😊