Resham2987/abaya-and-thobes-classifier

Abaya & Thobe Image Classifier

A fine-tuned MobileNetV2 model that classifies garment images as Abaya or Thobe.

Model Details

Property	Value
Base model	MobileNetV2 (ImageNet pretrained)
Task	Binary Image Classification
Framework	PyTorch
Input size	224 × 224 RGB
Output classes	Abaya, Thobe

Architecture

The backbone (MobileNetV2) was frozen. Only the custom classifier head was trained:

Dropout(0.3) → Linear(1280 → 128) → ReLU → Dropout(0.2) → Linear(128 → 2)

Training

Setting	Value
Epochs	15
Optimizer	Adam
Learning rate	1e-3
Weight decay	1e-4
Loss	CrossEntropyLoss
Dataset	~500 crawled garment images (Abaya & Thobe)

Labels

ID	Label
0	abaya
1	thobe

Usage

import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as transforms
from huggingface_hub import hf_hub_download
from PIL import Image

# Load model
weights = hf_hub_download("Resham2987/abaya-and-thobes-classifier", "pytorch_model.bin")

model = models.mobilenet_v2(weights=None)
model.classifier = nn.Sequential(
    nn.Dropout(0.3), nn.Linear(1280, 128),
    nn.ReLU(), nn.Dropout(0.2), nn.Linear(128, 2)
)
model.load_state_dict(torch.load(weights, map_location="cpu"))
model.eval()

# Preprocess
tf = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])

# Predict
img = Image.open("your_image.jpg").convert("RGB")
with torch.no_grad():
    probs = torch.softmax(model(tf(img).unsqueeze(0)), dim=1)[0]

labels = ["Abaya", "Thobe"]
print(f"{labels[probs.argmax()]}: {probs.max():.1%} confidence")