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license: mit
tags:
- image-classification
- pytorch
- mobilenet
- fashion
- abaya
- thobe
pipeline_tag: image-classification
library_name: pytorch
---
# 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
```python
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")
``` |