Hello @yosefyehoshua, thank you for your interest in our work! Please visit our Custom Training Tutorial to get started, and see our Jupyter Notebook , Docker Image, and Google Cloud Quickstart Guide for example environments.

If this is a bug report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

If this is a custom model or data training question, please note Ultralytics does not provide free personal support. As a leader in vision ML and AI, we do offer professional consulting, from simple expert advice up to delivery of fully customized, end-to-end production solutions for our clients, such as:

• Cloud-based AI systems operating on hundreds of HD video streams in realtime.
• Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
• Custom data training, hyperparameter evolution, and model exportation to any destination.

For more information please visit https://www.ultralytics.com.

@yosefyehoshua torch hub tutorial has some info on varying input channels: https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading

thanks for the answer. I'm now trying to train the model from torch.hub
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=False, classes=10, channels=6)

but while training:

`def train_m(model, criterion, optimizer, scheduler, dataloaders, datasets_size, num_epochs=25, phase='train'):
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    for epoch in range(num_epochs):
        model.train()
        i = 0    
        for imgs, annotations in dataloaders['train']:
            print("imgs type: {}".format(type(imgs)))
            i  = 1
            imgs = imgs.to(device, non_blocking=True).float() / 255.0
            annotations = [{k: v.to(device) for k, v in t.items()} for t in annotations]
            loss_dict = model(imgs, annotations)
            losses = sum(loss for loss in loss_dict.values())

            optimizer.zero_grad()
            losses.backward()
            optimizer.step()

            print(f'Iteration: {i}/{len_dataloader}, Loss: {losses}')`

my dataloader:

    train_ds = CustomCoco(root=train_coco_dir,
                          annotation=train_anno)
    val_ds = CustomCoco(root=val_coco_dir,
                          annotation=val_anno)

    
    def collate_fn(batch):
        return tuple(zip(*batch))

    train_batch_size = 4

    d_loader_train = torch.utils.data.DataLoader(train_ds,
                                            batch_size=train_batch_size,
                                            shuffle=True,
                                            num_workers=4,
                                            collate_fn=collate_fn)

    d_loader_val = torch.utils.data.DataLoader(val_ds,
                                            batch_size=train_batch_size,
                                            shuffle=True,
                                            num_workers=4,
                                            collate_fn=collate_fn)
    dataloaders = {'train': d_loader_train, 'val': d_loader_val}

    datasets_size = {'train': len(train_ds), 'val': len(val_ds)}

I'm gettting this error that my datakoader imgs is of 'tuple' type:

imgs = imgs.to(device, non_blocking=True).float() / 255.0
AttributeError: 'tuple' object has no attribute 'to'

what am I doing wrong?

@yosefyehoshua only torch tensors can be sent to a device. You've tried to do the same with a python tuple.

@yosefyehoshua only torch tensors can be sent to a device. You've tried to do the same with a python tuple.

yes because I need to use this function in my dataloader of else I get an error from collate.py
def collate_fn(batch): return tuple(zip(*batch))
can you think about another solution?

@yosefyehoshua only torch tensors can be sent to a device. You've tried to do the same with a python tuple.

yes because I need to use this function in my dataloader of else I get an error from collate.py
def collate_fn(batch): return tuple(zip(*batch))
can you think about another solution?

I'm basically trying to train this:
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=False, classes=10, channels=6)
on custom coco dataset with 6 channels and a fixed 300x300 size images

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.