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Error while training the dataset #8090
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@cyndyNKCM this is user error on your part. Your data yaml has no |
Thank you @glenn-jocher. |
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? |
@cyndyNKCM 👋 Hello! Thanks for asking about YOLOv5 🚀 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: 1.1 Create dataset.yamlCOCO128 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 # 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 LabelsAfter using a tool like Roboflow Annotate to label your images, export your labels to YOLO format, with one
The label file corresponding to the above image contains 2 persons (class 1.3 Organize DirectoriesOrganize your train and val images and labels according to the example below. YOLOv5 assumes ../datasets/coco128/images/im0.jpg # image
../datasets/coco128/labels/im0.txt # label Good luck 🍀 and let us know if you have any other questions! |
* Improved dataset error introspection Intended to help #8090 * Update general.py
@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. |
@cyndyNKCM 👋 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.yamlCOCO128 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 # 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 LabelsAfter using a tool like Roboflow Annotate to label your images, export your labels to YOLO format, with one
The label file corresponding to the above image contains 2 persons (class 1.3 Organize DirectoriesOrganize your train and val images and labels according to the example below. YOLOv5 assumes ../datasets/coco128/images/im0.jpg # image
../datasets/coco128/labels/im0.txt # label Good luck 🍀 and let us know if you have any other questions! |
@glenn-jocher Thank you the training process works well now. |
* Improved dataset error introspection Intended to help ultralytics#8090 * Update general.py
👋 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. Access additional YOLOv5 🚀 resources:
Access additional Ultralytics ⚡ resources:
Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed! Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐! |
* Improved dataset error introspection Intended to help ultralytics#8090 * Update general.py
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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