Metric	Value
Best Validation Loss	0.9726
Best Validation mIoU	23.33%
Training Epochs	44
Optimizer	AdamW (lr=1e-4, weight_decay=1e-2)
Scheduler	CosineAnnealingWarmRestarts
Loss Function	Dice Loss + Cross Entropy
Batch Size	16

Usage

python

import torch
import segmentation_models_pytorch as smp
from huggingface_hub import hf_hub_download

# Download model weights
model_path = hf_hub_download(
    repo_id="mawiie/food-segmentation-mobilenet",
    filename="best_model.pth"
)

# Create model architecture
model = smp.DeepLabV3Plus(
    encoder_name="mobilenet_v2",
    encoder_weights=None,  # We'll load our own weights
    in_channels=3,
    classes=104,
)

# Load trained weights
model.load_state_dict(torch.load(model_path, map_location="cpu"))
model.eval()

# Inference
# Normalize with ImageNet stats: mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)
# Input shape: (B, 3, 512, 512)
# Output shape: (B, 104, 512, 512)

Data Augmentation

Training:

Horizontal flip (p=0.5)
Vertical flip (p=0.1)
Affine transforms (scale, rotate, shear)
Random crop to 512x512
Color augmentations (brightness, contrast, HSV)
Gaussian blur

Validation:

Center crop to 512x512

Framework

PyTorch
segmentation-models-pytorch
Albumentations

License

MIT License