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primus29/
crackseg

image-segmentation

semantic-segmentation

CIDAS/clipseg-rd64-refined

finetune

mit

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CrackSeg

Fine-tuned CLIPSeg for pixel-wise surface crack detection. Given an image of any surface, the model returns a binary segmentation mask highlighting crack regions.

Model Performance

Metric	Score
Dice Score	0.612
mIoU	0.716

Live Demo

Try it on HuggingFace Spaces.

Training Details

Dataset: 14,000+ crack images (Roboflow, COCO format)
Fine-tuning: Partial — decoder fully unfrozen + last 2 layers of CLIP vision encoder + last 1 layer of CLIP text encoder
Loss: Focal Loss (α=0.75, γ=2.0)
Optimizer: AdamW with differential learning rates
Scheduler: CosineAnnealingLR
Early stopping: patience = 5

Usage

python

import torch
from huggingface_hub import hf_hub_download
from transformers import AutoProcessor, CLIPSegForImageSegmentation
from PIL import Image

processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")

path = hf_hub_download(repo_id="primus29/crackseg", filename="best_model.pth")
checkpoint = torch.load(path, map_location="cpu", weights_only=False)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

image = Image.open("your_image.jpg")
inputs = processor(text="segment crack", images=image, return_tensors="pt", padding=True)

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

mask = torch.sigmoid(outputs.logits).squeeze()
mask = (mask > 0.5).float()

Limitations

Shadow regions can be misidentified as cracks
Performance degrades on very thin hairline cracks
Trained primarily on surface/concrete crack data; may not generalize to all materials