food-segmentation-mobilenet

Food Segmentation Model (DeepLabV3+ with MobileNetV2)

A semantic segmentation model trained on the FoodSeg103 dataset for food image segmentation.

Model Description

• Architecture: DeepLabV3+ with MobileNetV2 encoder
• Backbone: MobileNetV2 (pretrained on ImageNet)
• Task: Semantic Segmentation
• Dataset: FoodSeg103 (103 food categories + background)
• Input Size: 512x512 RGB images
• Output: 104-class segmentation mask

Training Details

Usage

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