Eku127/swiftvln-satnav-3b-1ep-f32s4-overlap0-pf-h8-random-pool-s2-noembed

SwiftVLN for SatNav — Random History Sampling

Related Repositories

• —SwiftVLN: training and evaluation code for these checkpoints.
• —SatNav: satellite-image navigation environments, datasets, and evaluation tools.

This checkpoint is designed for SwiftVLN on SatNav, the continuous-state vision-and-language navigation benchmark over satellite imagery. It studies random sampling of earlier observations for long-term visual memory.

Model

• —Starting checkpoint: Qwen/Qwen2.5-VL-3B-Instruct
• —Training data: SatNav-v0.1 offline expert trajectories
• —Training: 1 epoch, full-parameter fine-tuning, learning rate 2e-5
• —Context: 32 RGB frames, up to 8 randomly sampled history frames
• —Prediction horizon: 4 actions
• —Memory: per-frame average pooling with stride 2

Repository name

• —swiftvln-satnav: SwiftVLN trained and evaluated on SatNav
• —3b: Qwen2.5-VL 3B backbone
• —1ep: trained for one epoch
• —f32s4: uses a 32-frame window and predicts four actions
• —overlap0: uses non-overlapping training windows
• —pf-h8-random: randomly samples up to eight history frames
• —pool-s2: applies average pooling with stride 2
• —noembed: uses no embedding enhancement

Reference Results

Results reported in the SatNav paper, memory-design ablation, row Random sampling.

SR, SPL, and OS are percentages. Changes in SR and OS are percentage points relative to the SwiftVLN reference model.

Usage

Use this checkpoint with the SwiftVLN evaluation guide. Keep the full repository name unchanged because SwiftVLN derives the evaluation configuration from it.

This checkpoint is a history-sampling ablation in the SwiftVLN SatNav Ablation Model Zoo.