segformer-b2-finetuned-ade-512-512

Performs pixel-level semantic segmentation on images using a SegFormer B2 transformer architecture with hierarchical self-attention and efficient linear decoder. The model processes 512x512 RGB images and outputs per-pixel class predictions across 150 ADE20K scene categories using a lightweight decoder that reduces computational overhead compared to dense convolutional decoders. Architecture uses a mix-transformer encoder with progressive downsampling stages (4x, 8x, 16x, 32x) followed by a simple linear projection decoder that fuses multi-scale features.

Implements SegFormer's lightweight linear decoder that fuses features from 4 hierarchical transformer encoder stages (4x, 8x, 16x, 32x spatial resolutions) using simple linear projections and concatenation rather than expensive upsampling convolutions. Each encoder stage output is projected to a common channel dimension (256), upsampled to 1/4 resolution via bilinear interpolation, concatenated, and passed through a final linear classifier to produce per-pixel predictions. This design eliminates the computational bottleneck of dense decoder networks while preserving spatial detail through early-stage features.

Classifies each pixel into one of 150 semantic categories from the ADE20K dataset, covering diverse indoor and outdoor scene elements including furniture, architectural features, vegetation, and human-made objects. The model outputs a probability distribution over 150 classes per pixel, enabling fine-grained scene understanding. Categories span hierarchical levels from broad (e.g., 'building', 'tree') to specific (e.g., 'door', 'window', 'potted plant'), allowing both coarse and detailed scene parsing depending on downstream application needs.

Processes multiple images in parallel using GPU-accelerated tensor operations, supporting batch sizes up to 32+ depending on available VRAM. Implements efficient batching through PyTorch DataLoader or TensorFlow Dataset APIs, with automatic mixed precision (AMP) to reduce memory footprint by 40-50% while maintaining accuracy. Supports both synchronous inference (blocking until all results ready) and asynchronous batching for streaming applications, with configurable batch accumulation for throughput optimization.

Enables transfer learning by freezing or unfreezing transformer encoder weights and retraining the linear decoder (or full model) on custom segmentation datasets. Supports standard PyTorch training loops with cross-entropy loss, focal loss, or dice loss; integrates with Hugging Face Trainer API for distributed training across multiple GPUs/TPUs. Provides pre-computed ImageNet-pretrained encoder weights as initialization, reducing training time by 10-50x compared to training from scratch. Includes utilities for handling class imbalance, custom class counts, and dataset-specific augmentation strategies.

Provides model quantization, pruning, and distillation techniques to reduce model size and inference latency for edge deployment. Supports INT8 quantization (4x size reduction, 2-3x speedup with <1% accuracy loss), dynamic quantization for PyTorch, and TensorFlow Lite conversion for mobile devices. Includes ONNX export for cross-platform inference, TensorRT optimization for NVIDIA hardware, and CoreML conversion for Apple devices. Enables inference on devices with <500MB memory and <100ms latency budgets through aggressive quantization and pruning.

Extracts per-pixel confidence scores by computing softmax probabilities over 150 classes, enabling uncertainty quantification for downstream decision-making. Provides maximum softmax probability as point estimate, entropy of class distribution as uncertainty measure, and margin (difference between top-2 probabilities) for ambiguity detection. Supports Monte Carlo dropout for Bayesian uncertainty estimation by running inference multiple times with dropout enabled, computing predictive variance across runs. Enables filtering low-confidence predictions, identifying ambiguous regions, and triggering human review for uncertain pixels.

Exports trained model to multiple inference frameworks (PyTorch, TensorFlow, ONNX, TensorRT, TFLite, CoreML) enabling deployment across diverse hardware and software stacks. Provides unified inference API that abstracts framework differences, allowing same code to run on PyTorch, TensorFlow, or ONNX backends. Handles automatic input preprocessing (resizing, normalization) and output postprocessing (argmax, softmax) across frameworks. Supports both eager execution (PyTorch) and graph-based execution (TensorFlow, TensorRT) with automatic optimization for each backend.

Processes video streams frame-by-frame with configurable buffering and batching strategies to maintain consistent throughput and minimize latency variance. Implements frame queue with configurable buffer size (1-30 frames), automatic frame dropping under load to prevent memory overflow, and optional temporal smoothing to reduce flickering across consecutive frames. Supports multiple input sources (video files, camera feeds, RTSP streams) with automatic frame rate detection and adaptive processing to match input FPS. Provides metrics tracking (FPS, latency percentiles, dropped frames) for monitoring real-time performance.

Extracts and visualizes transformer attention maps from intermediate encoder layers to understand which image regions influence segmentation decisions. Provides layer-wise attention visualization showing spatial attention patterns at different scales (4x, 8x, 16x, 32x), enabling diagnosis of failure cases and model behavior understanding. Supports gradient-based saliency maps (input gradients w.r.t. output) and attention rollout (aggregating attention across layers) for pixel-level importance estimation. Enables interactive visualization tools for exploring model decisions and building trust in predictions.