README.md

# Bag Classifier — Classification Head

This repository contains the classification head weights for the Bag Image Classifier, fine-tuned on luxury fashion e-commerce images.

## What is this?

This is the final classification layer (head) of a Vision Transformer (ViT) model fine-tuned to classify luxury bag images as **accept** or **reject** for e-commerce catalog use.

## Base Model

- **Architecture:** `google/vit-base-patch16-224`
- **Fine-tuning technique:** Linear Probing (only head was trained)
- **Task:** Binary classification (Accept / Reject)

## Labels

| Label | ID | Meaning |
|---|---|---|
| reject | 0 | Image not suitable for catalog |
| accept | 1 | Image suitable for catalog |

## Accept Categories
Images labeled as accept include:
- Front-idle
- side-idle
- Angle-idle
- outside-shoulder
- outside-hand
- outside-idle

## Reject Categories
Images labeled as reject include:
- inside
- Zoom-inside
- Zoom-outside
- Bottom-idle
- OOD (out of distribution — non-bag items)
- etc

## Training Data

- 3 datasets combined from different luxury fashion websites
- ~2,232 balanced images (1,116 accept + 1,116 reject)
- 80% train / 20% test split
- Final test accuracy: **98%**

## How to Use

### Step 1 — Install dependencies
```python
pip install transformers torch
```

### Step 2 — Load base model and head
```python
import torch
from transformers import AutoImageProcessor, AutoModelForImageClassification

# Load base ViT model
model = AutoModelForImageClassification.from_pretrained(
    "google/vit-base-patch16-224",
    num_labels=2,
    id2label={0: "reject", 1: "accept"},
    label2id={"reject": 0, "accept": 1},
    ignore_mismatched_sizes=True
)

# Load and apply head weights
head_weights = torch.load("head.pt", map_location="cpu")
model.classifier.weight = head_weights["classifier.weight"]
model.classifier.bias = head_weights["classifier.bias"]

model.eval()
print("Model ready!")
```

### Step 3 — Run inference
```python
from PIL import Image
from transformers import AutoImageProcessor

processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")

# Load your image
image = Image.open("your_bag_image.jpg").convert("RGB")

# Preprocess
inputs = processor(images=image, return_tensors="pt")

# Predict
import torch
with torch.no_grad():
    outputs = model(**inputs)
    predicted_id = outputs.logits.argmax(-1).item()
    label = {0: "reject", 1: "accept"}[predicted_id]
    confidence = torch.softmax(outputs.logits, dim=-1).max().item()

print(f"Prediction: {label}")
print(f"Confidence: {confidence:.2%}")
```

## Training Details

| Parameter | Value |
|---|---|
| Base model | google/vit-base-patch16-224 |
| Epochs | 5 |
| Batch size | 16 |
| Image size | 600x600 (thumbnail) |
| Optimizer | AdamW (default) |
| Test accuracy | 98% |

## Dataset

Private dataset hosted on HuggingFace — `malaika16/bag-labels`

Contains labeled images from 3 luxury fashion websites with 10 categories collapsed into binary accept/reject labels.