Datasets:
Formats:
imagefolder
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
1K - 10K
License:
File size: 2,573 Bytes
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from ultralytics import YOLO
from ultralytics.data import build_dataloader
from ultralytics.data.dataset import YOLODataset
import torch
import cv2
class CustomYOLODataset(YOLODataset):
def __init__(self, *args, **kwargs):
kwargs["data"] = dict(kwargs.get("data", {}), channels=4)
super().__init__(*args, **kwargs)
def __getitem__(self, index):
img_path = self.im_files[index]
img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
assert img.shape[-1] == 4, f"Image {img_path} has {img.shape[-1]} channels"
return super().__getitem__(index)
def build_dataloader_override(cfg, batch, img_size, stride, single_cls=False, hyp=None, augment=False, cache=False, pad=0.0, rect=False, rank=-1, workers=8, shuffle=False, data_info=None):
dataset = CustomYOLODataset(
data=data_info,
img_size=img_size,
batch_size=batch,
augment=augment,
hyp=hyp,
rect=rect,
cache=cache,
single_cls=single_cls,
stride=int(stride),
pad=pad,
rank=rank,
)
loader = torch.utils.data.DataLoader(
dataset=dataset,
batch_size=batch,
shuffle=shuffle,
num_workers=workers,
sampler=None,
pin_memory=True,
collate_fn=getattr(dataset, "collate_fn", None),
)
return loader
build_dataloader.build_dataloader = build_dataloader_override
# Initialize model
model = YOLO("yolo11_rgbd.yaml") # Ensure YAML has ch=4
# ---- Load Pretrained Weights ----
# pretrained = YOLO("yolo11l.pt").model.state_dict()
pretrained = YOLO("yolo11n.pt").model.state_dict()
model_state = model.model.state_dict()
filtered_pretrained = {k: v for k, v in pretrained.items() if not k.startswith(("model.23", "model.0.conv"))}
model_state.update(filtered_pretrained)
with torch.no_grad():
rgb_weights = pretrained["model.0.conv.weight"][:, :3]
depth_weights = torch.randn(64, 1, 3, 3) * 0.1 # FOr Yolov11l model
# depth_weights = torch.randn(16, 1, 3, 3) * 0.1 # For Yolov11n model
model_state["model.0.conv.weight"] = torch.cat([rgb_weights, depth_weights], dim=1)
model.model.load_state_dict(model_state, strict=False)
# ---- Critical Warmup Fix ----
def custom_warmup(self, imgsz=(1, 4, 640, 640)): # Force 4-channel input
self.forward(torch.zeros(imgsz).to(self.device))
model.model.warmup = custom_warmup.__get__(model.model)
# Train
model.train(
data="usplf_rgbd_dataset.yaml",
epochs=200,
imgsz=640,
batch=10,
device="0",
name="yolov11_rgbd_pretrained"
)
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