AssertionError: train: No labels in /Users/xxx/documents/train_data//labels.cache. Can not train without labels.

ryan-ching commented 2 years ago

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Question

Hello, I was having an issue with finding the labels for training images:

I used the labelImg tool for annotating labels. My images are in .jpg format and my annotations are in .txt format. This is an example of an annotation txt file:

Here is what my file structure looks like. My annotations are in train_data/val and my images are in train_data/images:

For specifying the path, I have tried using the relative path to the yolov5 folder and the absolute path from the root directory. Both resulted in the same error. custom_data.yaml screenshot (relative path):

custom_data.yaml screenshot (absolute path):

The command I ran which produced the error: python3 train.py --img 416 --batch 16 --epochs 3 --data custom_data.yaml --weights yolov5s.pt

I have also tried running with --nosave --cache which gives the same error.

Additional

No response

github-actions[bot] commented 2 years ago

👋 Hello @ryan-ching, 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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cd yolov5
pip install -r requirements.txt  # install

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glenn-jocher commented 2 years ago

@ryan-ching 👋 Hello! Thanks for asking about YOLOv5 🚀 dataset formatting. To train correctly your data must be in YOLOv5 format. You can run python train.py for a COCO128 demo. 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!

mxxws commented 2 years ago

Modify the path file, and then you can proceed smoothly, be careful not to change some files in train.py.

github-actions[bot] commented 2 years ago

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

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zhitkoalina commented 1 year ago

!chmod +w /content/gdrive/My\ Drive/{your path here}/data/labels solved it for me

glenn-jocher commented 1 year ago

@zhitkoalina glad to hear that you were able to solve the issue! Remember that chmod +w makes the file writable, which was necessary for you to modify it. Be careful when changing file permissions and try to avoid changing them unless necessary. Also, remember to replace {your path here} with the actual path to your labels directory.

If you have any other questions or issues, feel free to ask for help anytime!

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