Divyanshu-Kumar19/aapdasetu-damage-assessment

AapdaSetu Damage Assessment — ResNet50

Post-disaster building damage classifier for the AapdaSetu platform. Given a photo of a disaster-affected building, the model classifies damage severity into one of three grades and drives automated compensation assessment in the AapdaSetu relief pipeline.

Part of the AapdaSetu project (Smart India Hackathon).

Model Details

Architecture	ResNet50 (ImageNet-1K V2 pretrained backbone) + custom FC head (Dropout → Linear(2048, 3))
Input	224×224 RGB image
Preprocessing	Resize(256) → CenterCrop(224) → ToTensor → Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
Classes	MINOR, MAJOR, DESTROYED
Checkpoint	best.pt (~97 MB, PyTorch dict — keys: model_state, class_to_idx, classes, epoch, val_acc, val_loss, optim_state)

Training Recipe

• Dataset: 2,400 post-disaster building images — MINOR (846), MAJOR (525), DESTROYED (1,029), split 75 / 15 / 10 per class with a leak check (image hashes verified disjoint across splits — see leak_check.json).
• Two-phase training:
  1. Backbone frozen — only the new FC head trains
  2. Full fine-tuning of the entire network
• Evaluation: 5-crop Test-Time Augmentation (TTA) on the held-out test set.

Evaluation Results

Held-out test set (244 images), with TTA:

Class	Precision	Recall	F1	Support
DESTROYED	0.990	0.981	0.986	104
MAJOR	0.982	1.000	0.991	54
MINOR	0.977	0.977	0.977	86
Overall accuracy				98.36%

Full metrics: eval_report.json · Confusion matrix: confusion_matrix.png · ROC curves: roc_curves.png · Training curves: training_curves.png · Misclassified samples: misclassified.png · Training history: history.json · Split leak audit: leak_check.json

Quick Start — Loading best.pt for Inference

import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 1. Build the exact architecture used during training
model = models.resnet50(weights=None)
model.fc = nn.Sequential(
    nn.Dropout(p=0.0),                    # training-only; inert at inference
    nn.Linear(model.fc.in_features, 3),
)

# 2. Load the checkpoint
ckpt = torch.load("best.pt", map_location=device, weights_only=True)
model.load_state_dict(ckpt["model_state"])
model.to(device).eval()

# 3. IMPORTANT — use the checkpoint's index->class mapping.
#    ImageFolder trained classes in ALPHABETICAL order
#    (DESTROYED=0, MAJOR=1, MINOR=2), NOT ["MINOR", "MAJOR", "DESTROYED"].
idx_to_class = {int(v): k for k, v in ckpt["class_to_idx"].items()}

# 4. Preprocess exactly like training validation
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# 5. Predict
img = Image.open("building.jpg").convert("RGB")
x = transform(img).unsqueeze(0).to(device)
with torch.no_grad():
    probs = torch.softmax(model(x), dim=1)[0]

scores = {idx_to_class[i]: round(float(p), 4) for i, p in enumerate(probs)}
print("Damage grade:", idx_to_class[int(probs.argmax())])
print("All scores:", scores)

Download the checkpoint with:

hf download Divyanshu-Kumar19/aapdasetu-damage-assessment --local-dir ./model

Intended Use

• Triage of post-disaster building damage photos in the AapdaSetu claim pipeline (maps grade → compensation under NDRF/SDRF norms).
• Not a substitute for a certified structural engineer's assessment.
• Out of scope: images that are not disaster-affected buildings, and safety-critical decisions without human review.

Files in This Repository

File	Description
best.pt	Trained model checkpoint (state dict + metadata)
eval_report.json	Per-class precision / recall / F1 on the test set
confusion_matrix.png	Confusion matrix visualization
roc_curves.png	One-vs-rest ROC curves
training_curves.png	Train/val loss & accuracy curves
confidence_distribution.png	Prediction confidence distribution
misclassified.png	Test samples the model got wrong
history.json	Per-epoch training history
leak_check.json	Train/val/test split leak audit

License

MIT