How can I count the number of label class in my training dataset?

Ala1412 commented 2 years ago

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Hi, I am working on yolov5 with my custom dataset. After the training, it only shows the count of label class of the validation dataset. How can I get the counting of label class in my training dataset?

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github-actions[bot] commented 2 years ago

👋 Hello @Ala1412, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

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glenn-jocher commented 2 years ago

@Ala1412 you can run val.py on your train set with python val.py --task train

You can also look at labels.jpg to see a histogram of training set class counts:

Ala1412 commented 2 years ago

@Ala1412 you can run val.py on your train set with python val.py --task train

You can also look at labels.jpg to see a histogram of training set class counts:

Thanks for the reply, but I want to know if the "train" is a folder or what? Is it contain both the training images and txt file?

glenn-jocher commented 2 years ago

@Ala1412 👋 Hello! Thanks for asking about YOLOv5 🚀 dataset formatting. To train correctly your data must be in YOLOv5 format. Please see our Train Custom Data tutorial for full documentation on dataset setup and all steps required to start training your first model. A few excerpts from the tutorial:

1.1 Create dataset.yaml

COCO128 is an example small tutorial dataset composed of the first 128 images in COCO train2017. These same 128 images are used for both training and validation to verify our training pipeline is capable of overfitting. data/coco128.yaml, shown below, is the dataset config file that defines 1) the dataset root directory path and relative paths to train / val / test image directories (or *.txt files with image paths), 2) the number of classes nc and 3) a list of class names:

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/coco128  # dataset root dir
train: images/train2017  # train images (relative to 'path') 128 images
val: images/train2017  # val images (relative to 'path') 128 images
test:  # test images (optional)

# Classes
nc: 80  # number of classes
names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
         'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
         'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
         'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
         'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
         'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
         'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
         'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
         'hair drier', 'toothbrush' ]  # class names

1.2 Create Labels

After using a tool like Roboflow Annotate to label your images, export your labels to YOLO format, with one *.txt file per image (if no objects in image, no *.txt file is required). The *.txt file specifications are:

One row per object
Each row is class x_center y_center width height format.
Box coordinates must be in normalized xywh format (from 0 - 1). If your boxes are in pixels, divide x_center and width by image width, and y_center and height by image height.
Class numbers are zero-indexed (start from 0).

The label file corresponding to the above image contains 2 persons (class 0) and a tie (class 27):

1.3 Organize Directories

Organize your train and val images and labels according to the example below. YOLOv5 assumes /coco128 is inside a /datasets directory next to the /yolov5 directory. YOLOv5 locates labels automatically for each image by replacing the last instance of /images/ in each image path with /labels/. For example:

../datasets/coco128/images/im0.jpg  # image
../datasets/coco128/labels/im0.txt  # label

Good luck 🍀 and let us know if you have any other questions!

renzodamgo commented 2 years ago

Is there a way to get the actual label numbers, not only the bar plot?

glenn-jocher commented 2 years ago

@renzodamgo yes, val.py prints these analytics. To run val.py on a train set:

python val.py --task train --data DATA.yaml

renzodamgo commented 2 years ago

I have a custom dataset so i need to put my weights too:

!python val.py --task train --weights ./runs/train/exp9/weights/best.pt --data facemask.yaml

Captura de Pantalla 2022-05-25 a la(s) 6 14 17 p m

And It worked! 🥳 Thank you @glenn-jocher

renzodamgo commented 2 years ago

Sorry to bother again but is there a way to get this data from every label from the validation dataset?

True Positive(TP)
False Positive(FP)
False Negative(FN)

glenn-jocher commented 2 years ago

@renzodamgo val.py outputs tp and fp here: https://github.com/ultralytics/yolov5/blob/1dcb77499869d64e5dea7c5bd38357c92f5eff29/val.py#L266

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