wsffdg/uav-segformer-b2-finetune-in-lowalt
UAV SegFormer-B2 fine-tuned for low-altitude imagery
This repository provides a ZIP archive containing the two verified checkpoints from the uavseg_segformer_b2_v2 experiment: a SegFormer-B2 model fine-tuned for 9-class low-altitude aerial semantic segmentation.
Files
uavseg_segformer_b2_v2_checkpoints.zip— download this archive and extract it. It containsbest.pt,last.pt,config.yaml,README.md, andcheckpoint_manifest.jsonat its root.
Archive SHA-256: 3b656099c7d0b5940b313a99ba17527811920425268a3fef0c8eac78f253d1e0
Inside the archive, best.pt is selected by validation mIoU; last.pt is the complete epoch-40 checkpoint, including optimizer and scheduler state for resuming training. checkpoint_manifest.json contains the individual checkpoint hashes and provenance.
The checkpoint format is the project-specific uavseg PyTorch format. Load it with uavseg.utils.checkpoint.load_checkpoint, rather than directly with a generic Transformers from_pretrained call.
Inference
Use the reproduction code repository:
git clone https://github.com/wsffdg-123/uav-segformer-b2-finetune-in-lowalt.git
cd uav-segformer-b2-finetune-in-lowalt
# Install CUDA-compatible torch, then:
python -m pip install -r requirements/train.txtAfter extracting the archive, copy best.pt into outputs/experiments/uavseg_segformer_b2_v2/best.pt, then run:
python scripts/predict.py \
--config configs/optimized_v2.yaml \
--checkpoint outputs/experiments/uavseg_segformer_b2_v2/best.pt \
--image-dir /path/to/images \
--output-dir outputs/predictionsThe reproduction package deliberately provides only single-scale whole-image inference; it does not include TTA, sliding windows, or other inference post-processing. No dataset is distributed in this repository.
Provenance
- Source experiment:
uavseg_segformer_b2_v2 - Source code revision:
965610272584e3f285fc88531c48cfe9d2812764 - Backbone initializer:
nvidia/mit-b2revision3a609931044a9a83814802af9d861a47a1397636 - License: Apache-2.0
Please verify every downloaded checkpoint against checkpoint_manifest.json before use.
