This paper presents PerceptionLab’s approach for the BLP-2025 Shared Task 1A on multiclass Bangla hate speech detection, addressing severe class imbalance and informal online discourse. We perform Domain-Adaptive Pretraining (DAPT) on BERT models using a curated corpus of over 315,000 social media comments to capture slang, non-standard spellings, and contextual nuances of online discourse. To enrich underrepresented categories, we align external resources and construct a novel Bangla sexism dataset of over 6,800 comments via weak supervision and manual verification. Two classification strategies are compared: a single-shot six-way classifier and a two-stage hierarchical model that first separates Hate from Non-hate before fine-grained categorization. Experimental results show that single-shot classification with DAPT-enhanced BUET-BERT achieves the highest micro-F1 score (0.7265), outperforming the hierarchical approach and benchmarked general-purpose Large Language Models. Error analysis reveals persistent challenges in detecting subtle sexism and context-dependent religious hate. Our findings highlight the value of domain adaptation, robust end-to-end modeling, and targeted dataset construction for improving fine-grained hate speech detection in low-resource settings.