Create README.md

README.md

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+---
+datasets:
+- QCRI/CrisisMMD
+language:
+- en
+metrics:
+- accuracy
+- f1
+- recall
+- precision
+base_model:
+- google-bert/bert-base-uncased
+- microsoft/resnet-50
+---
+Source: CrisisMMD dataset (Alam et al., 2017)
+
+✅Original Labels (8 classes from annotations):
+
+Infrastructure and utility damage
+
+Vehicle damage
+
+Rescue, volunteering, or donation efforts
+
+Affected individuals
+
+Injured or dead people
+
+Missing or found people
+
+Other relevant information
+
+Not humanitarian
+
+✅Label Preprocessing (Class Merging):
+
+Vehicle damage merged into Infrastructure and utility damage
+
+Missing or found people merged into Affected individuals
+
+Not humanitarian retained as a separate class
+
+Removed very low-frequency categories (e.g., "Missing or found people" as a separate class)
+
+✅Final Label Set (5 classes total):
+
+Infrastructure and utility damage
+
+Rescue, volunteering, or donation efforts
+
+Affected individuals
+
+Injured or dead people
+
+Not humanitarian
+
+✅Multimodal Consistency:
+
+Selected only those posts where text and image annotations matched
+
+Resulted in a total of 8,219 consistent samples:
+
+Train set: 6,574 posts
+
+Test set: 1,644 posts
+
+✅ Preprocessing Done
+Text:
+
+Tokenized using BERT tokenizer (bert-base-uncased)
+
+Extracted input_ids and attention_mask
+
+Image:
+
+Processed using ResNet-50
+
+Extracted 2048-dimensional image features
+
+The preprocessed data was saved in PyTorch .pt format:
+
+train_human.pt and test_human.pt
+
+Each contains: input_ids, attention_mask, image_vector, and label
+
+✅ Model Architecture
+A custom multimodal classifier that combines BERT and ResNet-50 outputs:
+
+Component	Details
+Text Encoder	BERT base (bert-base-uncased) – outputs pooler_output (768-d)
+Image Encoder	Pre-extracted ResNet-50 image features (2048-d)
+Fusion	Concatenation → FC layers → Softmax over 5 classes
+Classifier	Fully connected layers with BatchNorm, ReLU, Dropout
+
+✅ Training Setup
+Loss Function: CrossEntropyLoss
+
+Optimizer: AdamW
+
+Scheduler: StepLR (γ = 0.9)
+
+Epochs Tried: 1, 3, 5, 8, 10
+
+Batch Size: 16
+
+Runtime: ~2 minutes 20 seconds per epoch on Google Colab (T4 GPU)
+
+✅ Evaluation Metrics
+Accuracy
+
+Precision
+
+Recall
+
+F1 Score
+
+✅ Metrics(epoch 3 with highest accuracy)
+ 
+✅ Test Accuracy : 0.8820
+✅ Precision     : 0.6854
+✅ Recall        : 0.7176
+✅ F1 Score      : 0.7005