This checkpoint is designed for SwiftVLN on SatNav, the continuous-state vision-and-language navigation benchmark over satellite imagery. It replaces historical RGB observations with rendered global and local explored-map 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 plus global and local explored maps
Prediction horizon: 4 actions
Map memory: 1,000 m global view, 400 m local view, rendered at 448 px
Map processing: 20-pixel mask dilation and 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
map-g1000-l400-r448-d20-s2: encodes the map extent, rendering, mask, and pooling settings
noembed: uses no embedding enhancement

Reference Results

Evaluated in SatSim on the full splits with a 500-step cap.

Split	Episodes	NE	OS	SR	SPL
`val_seen`	4,574	44.93	73.35	59.88	59.61
`val_unseen`	8,756	66.75	63.11	48.66	48.40

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 long-term-memory ablation in the SwiftVLN SatNav Ablation Model Zoo.

Eku127/swiftvln-satnav-3b-1ep-f32s4-overlap0-map-g1000-l400-r448-d20-s2-noembed

SwiftVLN for SatNav — Map Memory

Related Repositories

Model

Repository name

Reference Results

Usage

Eku127/
swiftvln-satnav-3b-1ep-f32s4-overlap0-map-g1000-l400-r448-d20-s2-noembed