This checkpoint is designed for SwiftVLN on SatNav, the continuous-state vision-and-language navigation benchmark over satellite imagery. It applies the SwiftVLN reference memory design to a Qwen3-VL 2B backbone.

Model

Starting checkpoint: Qwen/Qwen3-VL-2B-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 uniformly 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
qwen3vl-2b: Qwen3-VL 2B backbone
1ep: trained for one epoch
f32s4: uses a 32-frame window and predicts four actions
overlap0: uses non-overlapping training windows
pf-h8: uses per-frame memory with up to eight history frames
pool-s2: applies average pooling with stride 2
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	52.64	79.49	67.49	66.73
`val_unseen`	8,756	71.94	70.25	57.01	56.34

Usage

Download the checkpoint to a local directory and pass that directory through MODEL_PATH:

bash

MODEL_NAME=swiftvln-satnav-qwen3vl-2b-1ep-f32s4-overlap0-pf-h8-pool-s2-noembed
MODEL_PATH=/path/to/${MODEL_NAME} \
bash scripts/eval/eval_by_name.sh "${MODEL_NAME}"

See the SwiftVLN evaluation guide for environment, data, and single- or multi-GPU setup. Keep the full repository name unchanged because SwiftVLN derives the evaluation configuration from it.

Eku127/swiftvln-satnav-qwen3vl-2b-1ep-f32s4-overlap0-pf-h8-pool-s2-noembed

SwiftVLN for SatNav — Qwen3-VL 2B

Related Repositories

Model

Repository name

Reference Results

Usage

Eku127/
swiftvln-satnav-qwen3vl-2b-1ep-f32s4-overlap0-pf-h8-pool-s2-noembed