---
language: en
license: mit
tags:
  - clipseg
  - image-segmentation
  - text-conditioned-segmentation
  - drywall
  - quality-inspection
  - pytorch
base_model: CIDAS/clipseg-rd64-refined
datasets:
  - roboflow/drywall-join-detect
  - roboflow/cracks-3ii36
metrics:
  - iou
  - dice
---

# CLIPSeg — Fine-tuned for Drywall QA

Fine-tuned version of [CIDAS/clipseg-rd64-refined](https://huggingface.co/CIDAS/clipseg-rd64-refined)
for text-conditioned binary segmentation of drywall defects.

## Supported Prompts

| Prompt | Target Region | Val mIoU | Val Dice |
|--------|--------------|----------|----------|
| `segment crack` | Wall cracks | **0.7352** | **0.8336** |
| `segment taping area` | Joint / tape seam | **0.4985** | **0.6256** |

## Training Details

| Setting | Value |
|---------|-------|
| Base model | `CIDAS/clipseg-rd64-refined` |
| Epochs | 20 |
| Batch size | 4 |
| Learning rate | 1e-4 (AdamW) |
| Scheduler | CosineAnnealingLR |
| Loss | BCE 0.5 + Dice 0.5 |
| Image size | 352 × 352 |
| Threshold | 0.5 |
| Seed | 42 |
| Hardware | Tesla T4 (Google Colab) |
| Train time | ~65.3 min |
| Avg inference | 13.0 ms / image |

## Datasets

- **Dataset 1 — Taping area:** [Drywall-Join-Detect](https://universe.roboflow.com/objectdetect-pu6rn/drywall-join-detect)
- **Dataset 2 — Cracks:** [Cracks](https://universe.roboflow.com/fyp-ny1jt/cracks-3ii36)

## Quick Usage

```python
import torch
from PIL import Image
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation

processor = CLIPSegProcessor.from_pretrained("S-4-G-4-R/clipseg-drywall-qa")
model     = CLIPSegForImageSegmentation.from_pretrained("S-4-G-4-R/clipseg-drywall-qa")
model.eval()

image  = Image.open("your_image.jpg").convert("RGB")
prompt = "segment crack"   # or "segment taping area"

inputs = processor(
    text=prompt, images=image,
    return_tensors="pt", padding=True
)

with torch.no_grad():
    logits = model(**inputs).logits

mask = (torch.sigmoid(logits[0]) > 0.5).numpy()   # boolean H×W mask
```

## Test Results (best checkpoint — epoch 15)

| Metric | segment crack | segment taping area |
|--------|--------------|---------------------|
| mIoU | 0.6900 (test) / 0.7352 (val) | 0.4985 (val) |
| Dice | 0.7957 (test) / 0.8336 (val) | 0.6256 (val) |