Getting key error when training object detection model

Hi! I am getting the error while training my object detection model.
Epoch 1/1
Mini-batch: 1/1457 Loss: 0.1182
Mini-batch: 101/1457 Loss: 0.1295
Mini-batch: 201/1457 Loss: 0.1242
Mini-batch: 301/1457 Loss: 0.1200
Mini-batch: 401/1457 Loss: 0.1188
Mini-batch: 501/1457 Loss: 0.1164

KeyError Traceback (most recent call last)
in ()
8 print("Epoch %i/%i " % (epoch + 1, num_epochs) )
9 average_loss = 0
—> 10 for batch_id, (images, targets) in enumerate(train_data_loader):
11 # Prepare the batch data
12 images, targets = move_batch_to_device(images, targets)

3 frames
/usr/local/lib/python3.6/dist-packages/torch/ in reraise(self)
393 # (, so we work around it.
394 msg = KeyErrorMessage(msg)
–> 395 raise self.exc_type(msg)

KeyError: Caught KeyError in DataLoader worker process 0.
Original Traceback (most recent call last):
File “/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/”, line 185, in _worker_loop
data = fetcher.fetch(index)
File “/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/”, line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File “/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/”, line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File “”, line 43, in getitem
KeyError: ‘D0w0’

here is my damage dataset class
from import Dataset, DataLoader

Inherit from pytorch Dataset for convenience

class DamageDataset(Dataset):

def __init__(self, dataframe):


    self.filename = dataframe['filename'].unique()

    self.df = dataframe

def __len__(self) -> int:

    return len(self.filename)

def __getitem__(self, index: int):

    filename = self.filename[index]

    image = read_image_from_train_folder(filename).astype(np.float32)

    # Scale to [0,1] range expected by the pre-trained model

    image /= 255.0

    # Convert the shape from [h,w,c] to [c,h,w] as expected by pytorch

    image = torch.from_numpy(image).permute(2,0,1)


    records = self.df[self.df['filename'] == filename]


    boxes = records[['xmin', 'ymin', 'xmax', 'ymax']].values

    classes= records['class'].values



        'D00': 1,

        'D01': 2, 

        'D10': 3,

        'D11': 4,

        'D20': 5,

        'D40': 6,

        'D43': 7,

        'D44': 8,

        'D50': 9



    for label in classes:



    boxes = torch.as_tensor(boxes, dtype=torch.float32)

    n_boxes = boxes.shape[0]


    # there is only one foreground class, WHEAT

    labels = torch.as_tensor(damage_labels, dtype=torch.float32)


    target = {}

    target['boxes'] = boxes

    target['labels'] = labels


    return image, target

and here is my tarining code:
num_epochs = 1

Prepare the model for training

model =


for epoch in range(num_epochs):

print("Epoch %i/%i " % (epoch + 1, num_epochs) )

average_loss = 0

for batch_id, (images, targets) in enumerate(train_data_loader):

    # Prepare the batch data

    images, targets = move_batch_to_device(images, targets)

    # Calculate losses

    loss_dict = model(images, targets)

    batch_loss = sum(loss for loss in loss_dict.values()) / len(loss_dict)


    # Refresh accumulated optimiser state and minimise losses





    # Record stats

    loss_value = batch_loss.item()

    average_loss = average_loss + (loss_value - average_loss) / (batch_id + 1)

    print("Mini-batch: %i/%i Loss: %.4f" % ( batch_id + 1, len(train_data_loader), average_loss), end='\r')

    if batch_id % 100 == 0:

        print("Mini-batch: %i/%i Loss: %.4f" % ( batch_id + 1, len(train_data_loader), average_loss))

I would really appreciate some help into it, thank you.

Hy @Bisma_Akram

This happens inside your dataloader Because you don’t have a specific key for that.

The key generating error is this

hey, but that isnt a class in my dataset, do you think there is some error in the csv file of the training dataset that contain labels and bounding boxes,how do you think I can find out which index this class exist at so that I can delete it, I am iterating through my dataframe but cant find a class that contains D0w0.

Hy @Bisma_Akram

when you iterate over classes, your classes list contains the anomly either check this column or remove it from your dataset. It’s better if you use exception handling here.

great! I found out and removed it, could you please help me with the other question I posted too. thank you