Closed cyndyNKCM closed 2 years ago
@cyndyNKCM this is user error on your part. Your data yaml has no train:
field.
Thank you @glenn-jocher. It works well now.
The training process starts working but i have this error but i have a folder of images and a folder of labels. They are stored in a folder test. Same for the test.
AssertionError: train: No labels in /content/yolov5/Dataset_TrainTest/Train/Images.cache. Can not train without labels. See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
How can i fix this issue please? @glenn-jocher
@cyndyNKCM π Hello! Thanks for asking about YOLOv5 π dataset formatting. Your error message means YOLOv5 can't find your labels correctly. 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:
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
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:
class x_center y_center width height
format.x_center
and width
by image width, and y_center
and height
by image height.The label file corresponding to the above image contains 2 persons (class 0
) and a tie (class 27
):
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 π and let us know if you have any other questions!
I have followed all the instructionsbut i still have the error
@cyndyNKCM maybe try to delete your .cache file mentioned there and try again. If this doesn't work the problem is user error, you have not configured your dataset correctly.
i have deleted the cache file but i still have the same problem.
It is said in the picture 0 found 120 but the file of images isn't empty.
@cyndyNKCM yes exactly, no labels are found, 120 are missing.
But the labels' folder is not empty.
Hello, i have created a function that can split the images and labels into train_images folder , train_labels folder, test_images folder, test_labels folder. The labels' folder aren't empty but i don't understand why after execution of the training cell, i have the error : no labels in folder.
Here is an illustration of my problem. The partition of the dataset works well but the training doesn't despite the fact that the labels' folders are not empty.
@cyndyNKCM π Hello! Thanks for asking about YOLOv5 π 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:
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
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:
class x_center y_center width height
format.x_center
and width
by image width, and y_center
and height
by image height.The label file corresponding to the above image contains 2 persons (class 0
) and a tie (class 27
):
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 π and let us know if you have any other questions!
@glenn-jocher Thank you the training process works well now.
π 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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Thank you for your contributions to YOLOv5 π and Vision AI β!
You're welcome, @cyndyNKCM! I'm glad to hear that the training process is working well for you. If you have any more questions or need further assistance, feel free to ask anytime! Good luck with your training!
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I am using google colab and i want to train the model but i have this error. I didn't modify anything in the code. How can i fix this issue please ?
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