datasets/detect/open-images-v7/

[giscus[bot]]

datasets/detect/open-images-v7/

Dive into Google's Open Images V7, a comprehensive dataset offering a broad scope for computer vision research. Understand its usage with deep learning models.

https://docs.ultralytics.com/datasets/detect/open-images-v7/

[glenn-jocher]

Hey Ultralytics Users!

Exciting news! 🎉 We've added the Open Images V7 dataset to our collection. It's perfect for enhancing your YOLO models across various applications.

What's in it for you?

🌍 Diverse range of categories.
🎯 Accurate annotations for better training.
💡 Ideal for AI/ML advancements in computer vision.
Check out our new Open Images V7 page in the docs for all the details.

Questions or need help? Just ask us! We're here to support your YOLO journey.

Happy modeling! 🤖👀

[naga08krishna]

I only want to train 2 classes of images. Then how can I access OpenImagesV7.yaml for only two classes?

[glenn-jocher]

@naga08krishna to train a YOLO model on only two classes from the Open Images V7 dataset, you can modify the open-images-v7.yaml file to include only the classes you're interested in. Here’s how you can do it:

1. Edit the YAML file: Create a custom YAML file based on the original open-images-v7.yaml and include only the two classes you want to train on.

# Custom Open Images V7 dataset for two classes
path: ../datasets/open-images-v7
train: images/train
val: images/val

# Classes
names:
  0: Airplane
  1: Banana

2. Train the model: Use this custom YAML file to train your model.

Python Example

from ultralytics import YOLO

# Load a COCO-pretrained YOLOv8n model
model = YOLO("yolov8n.pt")

# Train the model on the custom Open Images V7 dataset
results = model.train(data="path/to/custom-open-images-v7.yaml", epochs=100, imgsz=640)

CLI Example

# Train a COCO-pretrained YOLOv8n model on the custom Open Images V7 dataset
yolo detect train data=path/to/custom-open-images-v7.yaml model=yolov8n.pt epochs=100 imgsz=640

This approach ensures that your training process focuses only on the specified classes. For more details, you can refer to the Open Images V7 documentation.

Feel free to reach out if you have any more questions! 😊

[naga08krishna]

Please tell me how to download the openimages.yaml file for only different types of vegetable images and how to download train and valid datasets? Tell me how to do this in colab.

[glenn-jocher]

Hi @naga08krishna,

To train a YOLO model on only vegetable images from the Open Images V7 dataset, you can create a custom YAML file that includes only the classes you're interested in. Here's how you can do it in Google Colab:

1. Create a Custom YAML File: Modify the open-images-v7.yaml to include only the vegetable classes you need. For example, if you want to include "Broccoli" and "Carrot", your YAML file would look like this:

# Custom Open Images V7 dataset for vegetable classes
path: ../datasets/open-images-v7
train: images/train
val: images/val

# Classes
names:
  0: Broccoli
  1: Carrot

2. Download and Prepare the Dataset: Use the fiftyone library to download and filter the dataset for your specific classes. Here’s a Colab script to help you get started:

# Install necessary libraries
!pip install fiftyone

import fiftyone as fo
import fiftyone.zoo as foz

# Define the classes you want to include
classes = ["Broccoli", "Carrot"]

# Load the Open Images V7 dataset
dataset = foz.load_zoo_dataset(
    "open-images-v7",
    split="train",
    label_types=["detections"],
    classes=classes,
    max_samples=1000,  # Adjust this number based on your needs
    dataset_dir="/content/open-images-v7"
)

# Export the dataset to YOLO format
dataset.export(
    export_dir="/content/open-images-v7-yolo",
    dataset_type=fo.types.YOLOv5Dataset,
    label_field="detections",
    classes=classes,
    split="train"
)

3. Train the Model: Use the custom YAML file to train your model.

Python Example

from ultralytics import YOLO

# Load a COCO-pretrained YOLOv8n model
model = YOLO("yolov8n.pt")

# Train the model on the custom Open Images V7 dataset
results = model.train(data="/content/open-images-v7-yolo/custom-open-images-v7.yaml", epochs=100, imgsz=640)

CLI Example

# Train a COCO-pretrained YOLOv8n model on the custom Open Images V7 dataset
yolo detect train data=/content/open-images-v7-yolo/custom-open-images-v7.yaml model=yolov8n.pt epochs=100 imgsz=640

This approach ensures that your training process focuses only on the specified vegetable classes. For more details, you can refer to the Open Images V7 documentation.

Feel free to reach out if you have any more questions! 😊

[naga08krishna]

Thank you very much for answering my doubt.

…

On Fri, Jun 21, 2024, 23:39 Glenn Jocher ***@***.***> wrote: @naga08krishna <https://github.com/naga08krishna> to train a YOLO model on only two classes from the Open Images V7 dataset, you can modify the open-images-v7.yaml file to include only the classes you're interested in. Here’s how you can do it: 1. *Edit the YAML file*: Create a custom YAML file based on the original open-images-v7.yaml and include only the two classes you want to train on. # Custom Open Images V7 dataset for two classespath: ../datasets/open-images-v7train: images/trainval: images/val # Classesnames: 0: Airplane 1: Banana 2. *Train the model*: Use this custom YAML file to train your model. Python Example from ultralytics import YOLO # Load a COCO-pretrained YOLOv8n modelmodel = YOLO("yolov8n.pt") # Train the model on the custom Open Images V7 datasetresults = model.train(data="path/to/custom-open-images-v7.yaml", epochs=100, imgsz=640) CLI Example # Train a COCO-pretrained YOLOv8n model on the custom Open Images V7 dataset yolo detect train data=path/to/custom-open-images-v7.yaml model=yolov8n.pt epochs=100 imgsz=640 This approach ensures that your training process focuses only on the specified classes. For more details, you can refer to the Open Images V7 documentation <https://docs.ultralytics.com/datasets/detect/open-images-v7/>. Feel free to reach out if you have any more questions! 😊 — Reply to this email directly, view it on GitHub <#7912 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/BAV6QX2DCCC2MNMMXL4IJ2TZIRT45AVCNFSM6AAAAABCR3JU3KVHI2DSMVQWIX3LMV43SRDJONRXK43TNFXW4Q3PNVWWK3TUHM4TQNBSGY2DC> . You are receiving this because you were mentioned.Message ID: ***@***.*** com>

[pderrenger]

@naga08krishna you're welcome! 😊

If you encounter any issues or bugs while training your model on the Open Images V7 dataset, please ensure you provide a minimum reproducible code example. This helps us understand and replicate the problem on our end. You can find more details on how to create one here: Minimum Reproducible Example.

Additionally, make sure you are using the latest versions of torch and ultralytics. If not, please update your packages and try again. This ensures you benefit from the latest fixes and improvements.

If you have any more questions or run into any issues, feel free to ask. We're here to help! 🚀

[naga08krishna]

Hi sorry to disturb you but please tell help me in this doubt After downloading the images, when I try to convert the dataset into yolo format I am getting below error repeatedly. Please help me in this. [image: image.png] [image: image.png] And also tell me how to divide the dataset into train and validate datasets.

…

On Sat, Jun 22, 2024 at 11:35 PM Glenn Jocher ***@***.***> wrote: Hi @naga08krishna <https://github.com/naga08krishna>, To train a YOLO model on only vegetable images from the Open Images V7 dataset, you can create a custom YAML file that includes only the classes you're interested in. Here's how you can do it in Google Colab: 1. *Create a Custom YAML File*: Modify the open-images-v7.yaml to include only the vegetable classes you need. For example, if you want to include "Broccoli" and "Carrot", your YAML file would look like this: # Custom Open Images V7 dataset for vegetable classespath: ../datasets/open-images-v7train: images/trainval: images/val # Classesnames: 0: Broccoli 1: Carrot 2. *Download and Prepare the Dataset*: Use the fiftyone library to download and filter the dataset for your specific classes. Here’s a Colab script to help you get started: # Install necessary libraries !pip install fiftyone import fiftyone as foimport fiftyone.zoo as foz # Define the classes you want to includeclasses = ["Broccoli", "Carrot"] # Load the Open Images V7 datasetdataset = foz.load_zoo_dataset( "open-images-v7", split="train", label_types=["detections"], classes=classes, max_samples=1000, # Adjust this number based on your needs dataset_dir="/content/open-images-v7" ) # Export the dataset to YOLO formatdataset.export( export_dir="/content/open-images-v7-yolo", dataset_type=fo.types.YOLOv5Dataset, label_field="detections", classes=classes, split="train" ) 3. *Train the Model*: Use the custom YAML file to train your model. Python Example from ultralytics import YOLO # Load a COCO-pretrained YOLOv8n modelmodel = YOLO("yolov8n.pt") # Train the model on the custom Open Images V7 datasetresults = model.train(data="/content/open-images-v7-yolo/custom-open-images-v7.yaml", epochs=100, imgsz=640) CLI Example # Train a COCO-pretrained YOLOv8n model on the custom Open Images V7 dataset yolo detect train data=/content/open-images-v7-yolo/custom-open-images-v7.yaml model=yolov8n.pt epochs=100 imgsz=640 This approach ensures that your training process focuses only on the specified vegetable classes. For more details, you can refer to the Open Images V7 documentation <https://docs.ultralytics.com/datasets/detect/open-images-v7/>. Feel free to reach out if you have any more questions! 😊 — Reply to this email directly, view it on GitHub <#7912 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/BAV6QX2UTYJPQL6VRYD25T3ZIW4GJAVCNFSM6AAAAABCR3JU3KVHI2DSMVQWIX3LMV43SRDJONRXK43TNFXW4Q3PNVWWK3TUHM4TQNBYGIZTE> . You are receiving this because you were mentioned.Message ID: ***@***.*** com>

[pderrenger]

Hi @naga08krishna,

No worries, I'm here to help! 😊

Error During Conversion

To assist you better with the error you're encountering while converting the dataset to YOLO format, could you please provide more details or a reproducible example? This will help us diagnose the issue more effectively. You can refer to our guide on creating a minimum reproducible example.

Dataset Division

To divide your dataset into training and validation sets, you can use the fiftyone library, which simplifies the process. Here's a quick example to get you started:

import fiftyone as fo
import fiftyone.zoo as foz

# Load the Open Images V7 dataset
dataset = foz.load_zoo_dataset(
    "open-images-v7",
    split="train",
    label_types=["detections"],
    dataset_dir="/path/to/open-images-v7",
    max_samples=1000  # Adjust this number based on your needs
)

# Split the dataset into train and validation sets
train_dataset = dataset.take(800)  # 80% for training
val_dataset = dataset.exclude(train_dataset)  # 20% for validation

# Export to YOLO format
train_dataset.export(
    export_dir="/path/to/open-images-v7-yolo/train",
    dataset_type=fo.types.YOLOv5Dataset,
    label_field="detections"
)
val_dataset.export(
    export_dir="/path/to/open-images-v7-yolo/val",
    dataset_type=fo.types.YOLOv5Dataset,
    label_field="detections"
)

Training Example

Once your dataset is divided and converted, you can train your model using the following command:

yolo detect train data=open-images-v7.yaml model=yolov8n.pt epochs=100 imgsz=640

For more detailed information, you can visit our Open Images V7 documentation.

Feel free to reach out if you have any more questions or need further assistance!

[naga08krishna]

I have converted the dataset into yolo format but all the images are in the training set only. Please tell me how to divide the dataset into training and validate datasets On Mon, Jun 24, 2024, 22:56 Naga Krishna Reddy ***@***.***> wrote:

…

Hi sorry to disturb you but please tell help me in this doubt After downloading the images, when I try to convert the dataset into yolo format I am getting below error repeatedly. Please help me in this. [image: image.png] [image: image.png] And also tell me how to divide the dataset into train and validate datasets. On Sat, Jun 22, 2024 at 11:35 PM Glenn Jocher ***@***.***> wrote: > Hi @naga08krishna <https://github.com/naga08krishna>, > > To train a YOLO model on only vegetable images from the Open Images V7 > dataset, you can create a custom YAML file that includes only the classes > you're interested in. Here's how you can do it in Google Colab: > > 1. *Create a Custom YAML File*: Modify the open-images-v7.yaml to > include only the vegetable classes you need. For example, if you want to > include "Broccoli" and "Carrot", your YAML file would look like this: > > # Custom Open Images V7 dataset for vegetable classespath: ../datasets/open-images-v7train: images/trainval: images/val > # Classesnames: > 0: Broccoli > 1: Carrot > > > 2. *Download and Prepare the Dataset*: Use the fiftyone library to > download and filter the dataset for your specific classes. Here’s a Colab > script to help you get started: > > # Install necessary libraries > !pip install fiftyone > import fiftyone as foimport fiftyone.zoo as foz > # Define the classes you want to includeclasses = ["Broccoli", "Carrot"] > # Load the Open Images V7 datasetdataset = foz.load_zoo_dataset( > "open-images-v7", > split="train", > label_types=["detections"], > classes=classes, > max_samples=1000, # Adjust this number based on your needs > dataset_dir="/content/open-images-v7" > ) > # Export the dataset to YOLO formatdataset.export( > export_dir="/content/open-images-v7-yolo", > dataset_type=fo.types.YOLOv5Dataset, > label_field="detections", > classes=classes, > split="train" > ) > > > 3. *Train the Model*: Use the custom YAML file to train your model. > > Python Example > > from ultralytics import YOLO > # Load a COCO-pretrained YOLOv8n modelmodel = YOLO("yolov8n.pt") > # Train the model on the custom Open Images V7 datasetresults = model.train(data="/content/open-images-v7-yolo/custom-open-images-v7.yaml", epochs=100, imgsz=640) > > CLI Example > > # Train a COCO-pretrained YOLOv8n model on the custom Open Images V7 dataset > yolo detect train data=/content/open-images-v7-yolo/custom-open-images-v7.yaml model=yolov8n.pt epochs=100 imgsz=640 > > This approach ensures that your training process focuses only on the > specified vegetable classes. For more details, you can refer to the Open > Images V7 documentation > <https://docs.ultralytics.com/datasets/detect/open-images-v7/>. > > Feel free to reach out if you have any more questions! 😊 > > — > Reply to this email directly, view it on GitHub > <#7912 (reply in thread)>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/BAV6QX2UTYJPQL6VRYD25T3ZIW4GJAVCNFSM6AAAAABCR3JU3KVHI2DSMVQWIX3LMV43SRDJONRXK43TNFXW4Q3PNVWWK3TUHM4TQNBYGIZTE> > . > You are receiving this because you were mentioned.Message ID: > ***@***.*** > com> >

[pderrenger]

Hi @naga08krishna,

Thank you for reaching out! To split your dataset into training and validation sets, you can use the fiftyone library, which provides a convenient way to handle and manipulate datasets. Here's a step-by-step guide to help you achieve this:

1. Install FiftyOne:
   Ensure you have the fiftyone library installed. You can install it using pip:

   pip install fiftyone

2. Load and Split the Dataset:
   Use the following script to load your dataset and split it into training and validation sets. This example assumes you have already converted the dataset into YOLO format and all images are currently in the training set.

   import fiftyone as fo
   import fiftyone.zoo as foz
   from sklearn.model_selection import train_test_split
   
   # Load your dataset
   dataset_dir = "/path/to/your/yolo/dataset"
   dataset = fo.Dataset.from_dir(
       dataset_type=fo.types.YOLOv5Dataset,
       data_path=dataset_dir,
       labels_path=dataset_dir,
       include_id=True
   )
   
   # Split the dataset into training and validation sets
   train_view, val_view = dataset.take(0.8), dataset.take(0.2)
   
   # Export the training set
   train_view.export(
       export_dir="/path/to/save/train",
       dataset_type=fo.types.YOLOv5Dataset,
       label_field="ground_truth",
       classes=dataset.default_classes,
       split="train"
   )
   
   # Export the validation set
   val_view.export(
       export_dir="/path/to/save/val",
       dataset_type=fo.types.YOLOv5Dataset,
       label_field="ground_truth",
       classes=dataset.default_classes,
       split="val"
   )

3. Update Your YAML File:
   Ensure your open-images-v7.yaml file points to the correct directories for training and validation images:

   path: ../datasets/open-images-v7
   train: /path/to/save/train/images
   val: /path/to/save/val/images

4. Train Your Model:
   Now you can train your model using the updated YAML file:

   from ultralytics import YOLO
   
   # Load a COCO-pretrained YOLOv8n model
   model = YOLO("yolov8n.pt")
   
   # Train the model on the Open Images V7 dataset
   results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)

   Or via CLI:

   yolo detect train data=open-images-v7.yaml model=yolov8n.pt epochs=100 imgsz=640

This should help you split your dataset and proceed with training. If you encounter any issues or need further assistance, feel free to ask! 😊

For more detailed information, you can refer to the Open Images V7 documentation.

[kaymanas]

I have downloaded and used the Ultralytics Open Images V7 Pretrained Models and they work great. However when I export to CoreML to use with XCode, the results are very different and much worse. There are many many more predictions and they're way off, the image attached predicted Snake, Treadmill, dumbell, Tent and many more at 99-100% confidence.

See the images below for example of the output with the model as is and then converted to mlpackage format.

https://imgur.com/a/bK2yc9a

Is there something wrong with the conversion process?

My python code here:

from ultralytics import YOLO

model = YOLO("yolov8x-oiv7.pt") # load model

model.export(format="coreml",nms=True) # convert model

[pderrenger]

Thanks for updating to the latest version and testing the export options. The error message you're encountering indicates that the current implementation only supports non-pipelined CoreML models with nms=False. This limitation might be causing the poor predictions in XCode. To address this, ensure that the preprocessing steps in XCode match those used during training. If the issue persists, consider reporting it on our GitHub issues page for further investigation.

[kaymanas]

How do I ensure that the preprocessing steps in XCode match those used during training? Sorry I'm new to this, I tried googling but couldn't find any info...

[pderrenger]

@kaymanas to ensure preprocessing steps in XCode match those used during training, you need to replicate the same image resizing, normalization, and other preprocessing steps. Typically, this involves resizing images to the same dimensions used during training (e.g., 640x640), normalizing pixel values to the same range (e.g., 0-1), and applying any other transformations like mean subtraction or standard deviation scaling. You can find the exact preprocessing steps in the training script or model documentation. If you need further assistance, please refer to the Ultralytics documentation.

[kaymanas]

Thanks for the help! where do I find the training script or model documentation? This page (the one you linked) doesn't have that information....

[pderrenger]

You can find the training script and model documentation in the Ultralytics YOLO repository on GitHub. Specifically, the Ultralytics documentation provides comprehensive details on training, validation, and exporting models. For the Open Images V7 dataset, refer to the dataset's YAML configuration and the training section in the documentation. If you need further assistance, please check the repository's README or the relevant sections in the documentation.

[davidhuangal]

For the pretrained Oi7 models, what hyperparameters were used? Was it the default.yaml parameters? Or were things changed, like the number of epochs, etc.? I would like to train some YOLOv9 and YOLOv10 models on the dataset and wanted to make sure I was following the same training design as the YOLOv8 pretrained Oi7 models.

[glenn-jocher]

@davidhuangal the pretrained YOLOv8 models for the Open Images V7 dataset were trained using the default.yaml parameters. This includes standard hyperparameters and training settings. For your YOLOv9 and YOLOv10 models, you can start with these default settings and adjust as needed based on your specific requirements. If you encounter any issues or need further customization, please refer to the Ultralytics documentation for detailed guidance.

[glenn-jocher]

@davidhuangal ah one small change, epochs was lowered to 30 since OIv7 is so much larger than COCO. All else should be identical.

[nandiniigarg]

I am trying to train the YOLOv8m model using python for selected classes. Here's my YAML file:

# Ultralytics YOLO 🚀, AGPL-3.0 license
# Open Images v7 dataset https://storage.googleapis.com/openimages/web/index.html by Google
# Documentation: https://docs.ultralytics.com/datasets/detect/open-images-v7/
# Example usage: yolo train data=open-images-v7.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── open-images-v7  ← downloads here (561 GB)

# 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: fiftyone/open-images-v7 # dataset root dir
train: train # train images (relative to 'path') 1743042 images
val: validation # val images (relative to 'path') 41620 images
test: # test images (optional)

# Classes
names:
  0: Bathroom accessory
  1: Bathroom cabinet
  2: Bathtub
  3: Bed
  4: Bench
.....
  159: Wine glass
  160: Wine rack
  161: Wood-burning stove

# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
  from ultralytics.utils import LOGGER, SETTINGS, Path, is_ubuntu, get_ubuntu_version
  from ultralytics.utils.checks import check_requirements, check_version

  check_requirements('fiftyone')
  if is_ubuntu() and check_version(get_ubuntu_version(), '>=22.04'):
      # Ubuntu>=22.04 patch https://github.com/voxel51/fiftyone/issues/2961#issuecomment-1666519347
      check_requirements('fiftyone-db-ubuntu2204')

  import fiftyone as fo
  import fiftyone.zoo as foz
  import warnings
  classes_list = ["Bathroom accessory","Bathroom cabinet","Bathtub","Bed", "Bench", "Bidet", "Blender", "Book", "Bookcase", "Bowl", "Box", "Bronze sculpture>

  name = 'open-images-v7'
  fraction = 1.0  # fraction of full dataset to use
  LOGGER.warning('WARNING ⚠️ Open Images V7 dataset requires at least **561 GB of free space. Starting download...')
  for split in 'train', 'validation':  # 1743042 train, 41620 val images
      train = split == 'train'

      # Load Open Images dataset
      dataset = foz.load_zoo_dataset(name,
                                     split=split,
                                     label_types=['detections'],
                                     classes = classes_list,
                                     dataset_dir=Path(SETTINGS['datasets_dir']) / 'fiftyone' / name,
                                     max_samples=round((1743042 if train else 41620) * fraction))

      # Define classes
      if train:
          classes = dataset.default_classes  # all classes
          # classes = dataset.distinct('ground_truth.detections.label')  # only observed classes

      # Export to YOLO format
      with warnings.catch_warnings():
          warnings.filterwarnings("ignore", category=UserWarning, module="fiftyone.utils.yolo")
          dataset.export(export_dir=str(Path(SETTINGS['datasets_dir']) / name),
                         dataset_type=fo.types.YOLOv5Dataset,
                         label_field='ground_truth',
                         split='val' if split == 'validation' else split,
                         classes=classes,
                         overwrite=train)

I am running into this error after the first epoch:

Starting training for 100 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      1/100      7.34G          0      83.18          0          0        640:   7%|▋         | 1794/27205 [13:28<1:51:55,  3.78it/s]^[[B^[[B^[[B^[[B^[[B^[[B
      1/100      7.34G          0      5.485          0          0        640: 100%|██████████| 27205/27205 [1:52:47<00:00,  4.02it/s]  
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 376/376 [02:20<00:00,  2.67it/s]
Traceback (most recent call last):
  File "/home/nirwana/nandini/open_images_test/ultralyticsTrial/train.py", line 7, in <module>
    results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640, device=0, patience=5, save=True, verbose=True, plots=True)
  File "/home/nirwana/.local/lib/python3.10/site-packages/ultralytics/engine/model.py", line 813, in train
    self.trainer.train()
  File "/home/nirwana/.local/lib/python3.10/site-packages/ultralytics/engine/trainer.py", line 206, in train
    self._do_train(world_size)
  File "/home/nirwana/.local/lib/python3.10/site-packages/ultralytics/engine/trainer.py", line 430, in _do_train
    self.metrics, self.fitness = self.validate()
  File "/home/nirwana/.local/lib/python3.10/site-packages/ultralytics/engine/trainer.py", line 575, in validate
    metrics = self.validator(self)
  File "/home/nirwana/.local/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
  File "/home/nirwana/.local/lib/python3.10/site-packages/ultralytics/engine/validator.py", line 193, in __call__
    stats = self.get_stats()
  File "/home/nirwana/.local/lib/python3.10/site-packages/ultralytics/models/yolo/detect/val.py", line 181, in get_stats
    stats = {k: torch.cat(v, 0).cpu().numpy() for k, v in self.stats.items()}  # to numpy
  File "/home/nirwana/.local/lib/python3.10/site-packages/ultralytics/models/yolo/detect/val.py", line 181, in <dictcomp>
    stats = {k: torch.cat(v, 0).cpu().numpy() for k, v in self.stats.items()}  # to numpy
RuntimeError: torch.cat(): expected a non-empty list of Tensors

I cannot figure out how to fix this error.

[glenn-jocher]

@nandiniigarg it looks like the error might be due to an empty list of tensors during validation. Please ensure that your dataset and annotations are correctly formatted and that the selected classes have sufficient instances. Also, verify if the issue persists with the latest version of the Ultralytics package. For more details, you can refer to the Open Images V7 documentation.

[safwene-bahroun]

HELLO @glenn-jocher, i have the same error . Can you help me to fix it ?

[glenn-jocher]

@safwene-bahroun please provide a https://docs.ultralytics.com/help/minimum-reproducible-example/ to allow us to help you better.

[safwene-bahroun]

path: ../datasets/open-images-v7
train: C:\Users\GIGABYTE\datasets\open-images-v7\images\open-images-v7\train
val: C:\Users\GIGABYTE\datasets\open-images-v7\images\open-images-v7\validation
nc: 36
names:
0: Person
1: Man
2: Woman
3: Boy
4: Belt
5: Clothing
6: Coat
7: Cowboy hat
8: Dress
9: Fedora
10: Footwear
11: Glasses
12: Glove
13: Goggles
14: Hat
15: High heels
16: Jeans
17: Miniskirt
18: Scarf
19: Shirt
20: Shorts
21: Skirt
22: Sock
23: Sombrero
24: Suit
25: Sun hat
26: Sunglasses
27: Swim cap
28: Swimwear
29: Tie
30: Trousers
31: Cosmetics
32: Face powder
33: Hair spray
34: Lipstick
35: Perfume
download: |
from ultralytics.utils import LOGGER, SETTINGS, Path, is_ubuntu, get_ubuntu_version
from ultralytics.utils.checks import check_requirements, check_version
import fiftyone as fo
import fiftyone.zoo as foz
from fiftyone import ViewField as F # Import F for filtering
import warnings

Define the list of classes

classes = [
"Person", "Man", "Woman", "Boy", "Belt", "Clothing", "Coat", "Cowboy hat", "Dress", "Fedora",
"Footwear", "Glasses", "Glove", "Goggles", "Hat", "High heels", "Jeans", "Miniskirt", "Scarf",
"Shirt", "Shorts", "Skirt", "Sock", "Sombrero", "Suit", "Sun hat", "Sunglasses", "Swim cap",
"Swimwear", "Tie", "Trousers", "Cosmetics", "Face powder", "Hair spray", "Lipstick", "Perfume"
]

Dataset name and directory setup

name = "open-images-v7"
fo.config.dataset_zoo_dir = Path(SETTINGS["datasets_dir"]) / name / "images"

Log the start of the download process

LOGGER.warning('Open Images V7 dataset: Starting download...')

Loop through train and validation splits

for split in ['train', 'validation']:
# Load Open Images dataset
dataset = foz.load_zoo_dataset(
name,
split=split,
classes=classes,
label_types=['detections'],
max_samples=5000
)

  # Define export directory
  export_dir = Path(SETTINGS['datasets_dir']) / name / "images" / ('train' if split == 'train' else 'validation')

  # Filter dataset to include only the specified classes
  filtered_dataset = dataset.match(F("ground_truth.detections.label").is_in(classes))

  # Export to YOLO format
  with warnings.catch_warnings():
      warnings.filterwarnings("ignore", category=UserWarning, module="fiftyone.utils.yolo")
      filtered_dataset.export(
          export_dir=str(export_dir),
          dataset_type=fo.types.YOLOv5Dataset,
          label_field='ground_truth',
          split=split,
          classes=classes,
          overwrite=True
      )

[glenn-jocher]

The RuntimeError: torch.cat(): expected a non-empty list of Tensors typically occurs when no validation predictions are generated. For Open Images V7 training with custom classes, please verify:

1. Class Consistency: Ensure your 36 selected classes exist in both train/val splits. Some fine-grained categories like "Man"/"Woman" might have limited val instances.
2. Path Formatting: Use forward slashes in paths: train: datasets/open-images-v7/images/train
3. Annotations: Confirm your filtered dataset contains labels by inspecting labels/val folder

For reference, see the Open Images V7 dataset setup guide in our documentation. Let us know if these checks resolve the validation issue.

[laila-e]

Install necessary libraries

!pip install fiftyone

import fiftyone as fo
import fiftyone.zoo as foz

Define the classes you want to include

classes = ["Broccoli", "Carrot"]

Load the Open Images V7 dataset

dataset = foz.load_zoo_dataset(
"open-images-v7",
split="train",
label_types=["detections"],
classes=classes,
max_samples=1000, # Adjust this number based on your needs
dataset_dir="/content/open-images-v7"
)

Export the dataset to YOLO format

dataset.export(
export_dir="/content/open-images-v7-yolo",
dataset_type=fo.types.YOLOv5Dataset,
label_field="detections",
classes=classes,
split="train"
)
I try this code and it works well but I need labels for detection and for visual relationship and I do not know how do that

[glenn-jocher]

@laila-e to include labels for both detection and visual relationships in your dataset, you'll need to adjust the label_types parameter in the load_zoo_dataset function to include both "detections" and "relationships". For more detailed guidance, please refer to the Open Images V7 documentation.

[ABINAYA200264]

Hello I'm Abinaya I want selective classes in Open Images v7 dataset I use class Id to extract spacific classes but it not work it detect all the the defult classes I want only selected class, I don't have training spcilities please can some one help me