You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
101 lines
2.2 KiB
101 lines
2.2 KiB
--- |
|
comments: true |
|
--- |
|
|
|
# Image Classification Datasets Overview |
|
|
|
## Dataset format |
|
|
|
The folder structure for classification datasets in torchvision typically follows a standard format: |
|
|
|
``` |
|
root/ |
|
|-- class1/ |
|
| |-- img1.jpg |
|
| |-- img2.jpg |
|
| |-- ... |
|
| |
|
|-- class2/ |
|
| |-- img1.jpg |
|
| |-- img2.jpg |
|
| |-- ... |
|
| |
|
|-- class3/ |
|
| |-- img1.jpg |
|
| |-- img2.jpg |
|
| |-- ... |
|
| |
|
|-- ... |
|
``` |
|
|
|
In this folder structure, the `root` directory contains one subdirectory for each class in the dataset. Each subdirectory is named after the corresponding class and contains all the images for that class. Each image file is named uniquely and is typically in a common image file format such as JPEG or PNG. |
|
|
|
** Example ** |
|
|
|
For example, in the CIFAR10 dataset, the folder structure would look like this: |
|
|
|
``` |
|
cifar-10-/ |
|
| |
|
|-- train/ |
|
| |-- airplane/ |
|
| | |-- 10008_airplane.png |
|
| | |-- 10009_airplane.png |
|
| | |-- ... |
|
| | |
|
| |-- automobile/ |
|
| | |-- 1000_automobile.png |
|
| | |-- 1001_automobile.png |
|
| | |-- ... |
|
| | |
|
| |-- bird/ |
|
| | |-- 10014_bird.png |
|
| | |-- 10015_bird.png |
|
| | |-- ... |
|
| | |
|
| |-- ... |
|
| |
|
|-- test/ |
|
| |-- airplane/ |
|
| | |-- 10_airplane.png |
|
| | |-- 11_airplane.png |
|
| | |-- ... |
|
| | |
|
| |-- automobile/ |
|
| | |-- 100_automobile.png |
|
| | |-- 101_automobile.png |
|
| | |-- ... |
|
| | |
|
| |-- bird/ |
|
| | |-- 1000_bird.png |
|
| | |-- 1001_bird.png |
|
| | |-- ... |
|
| | |
|
| |-- ... |
|
``` |
|
|
|
In this example, the `train` directory contains subdirectories for each class in the dataset, and each class subdirectory contains all the images for that class. The `test` directory has a similar structure. The `root` directory also contains other files that are part of the CIFAR10 dataset. |
|
|
|
## Usage |
|
!!! example "" |
|
|
|
=== "Python" |
|
|
|
```python |
|
from ultralytics import YOLO |
|
|
|
# Load a model |
|
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training) |
|
|
|
# Train the model |
|
model.train(data='path/to/dataset', epochs=100, imgsz=640) |
|
``` |
|
=== "CLI" |
|
|
|
```bash |
|
# Start training from a pretrained *.pt model |
|
yolo detect train data=path/to/data model=yolov8n-seg.pt epochs=100 imgsz=640 |
|
``` |
|
|
|
## Supported Datasets |
|
TODO |