| --- |
| license: mit |
| pipeline_tag: image-segmentation |
| tags: |
| - wsss |
| - boundary-refinement |
| - pytorch |
| --- |
| |
| # HGA Pretrained Checkpoints (PASCAL VOC 2012 & MS COCO 2014) |
|
|
| This repository hosts the official best-performing model checkpoints for the **HGA (Hierarchical-Geometric Alignment)** weakly supervised semantic segmentation (WSSS) paradigm. |
|
|
| Both checkpoints leverage the high-performance **EfficientViT-SAM-XL0** backbone, but employ different decoder architectures optimized for their respective dataset scales. |
|
|
| ## 📊 Benchmark Checklist |
|
|
| | Checkpoint Filename | Target Dataset | Backbone | Decoder Type | Reached mIoU | |
| |:---|:---:|:---:|:---:|:---:| |
| | **`best_model_voc.pth`** | PASCAL VOC 2012 Val | EfficientViT-SAM-XL0 | **RC (Resize-Conv) Decoder** | **84.91%** | |
| | **`best_model_coco.pth`** | MS COCO 2014 Val | EfficientViT-SAM-XL0 | **CT (Transpose-Conv) Decoder** | **59.31%** | |
|
|
| --- |
|
|
| ## ⚠️ Critical Setup Instructions (Decoder Mismatch Prevention) |
|
|
| To prevent PyTorch `state_dict` loading errors (such as `KeyError` or missing key warnings), you must configure the decoder type in your `config.py` to match the downloaded checkpoint exactly: |
|
|
| ### 1. For PASCAL VOC 2012 (`best_model_voc.pth`): |
| Ensure your decoder configuration is set to use the patched **Resize-Convolution (RC) Decoder**: |
| ```python |
| # Inside your config.py for VOC evaluation: |
| decoder_type = "RC" # Ensure this matches the RC-Decoder setup |
| ``` |
|
|
| ### 2. For MS COCO 2014 (`best_model_coco.pth`): |
| Ensure your decoder configuration is set to use the standard **Transposed Convolution (CT) Decoder**: |
| ```python |
| # Inside your config.py for COCO evaluation: |
| decoder_type = "CT" # Ensure this matches the CT-Decoder setup |
| ``` |
|
|
| --- |
|
|
| ## 🚀 Evaluation & Reproducibility Guide |
|
|
| The evaluation scripts are designed to automatically reconstruct the correct network architecture based on your configuration files. |
|
|
| 1. Download the target checkpoint file (`.pth`) and place it under your local `checkpoints/` folder. |
| 2. Clone our official codebase and configure your directory paths: |
| 👉 **[Uncertainty-42/HGA GitHub Repository](https://github.com/Uncertainty-42/HGA)** |
| 3. Run the evaluation shell scripts directly from the repository root: |
|
|
| ```bash |
| # To evaluate PASCAL VOC: |
| bash val_voc.sh |
| |
| # To evaluate MS COCO: |
| bash val_coco.sh |
| ``` |
|
|
| ## 📝 Citation |
| Our paper is currently under review/pre-print preparation. The official BibTeX citation and arXiv link will be updated here as soon as the preprint is publicly released. |
