Update README.md

README.md

@@ -1,3 +1,87 @@
 ---
-pipeline_tag: Image-Classification
----
+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")
+```