| # Per-model run commands β `foldsrunner_simplified_after_ablation.py` |
| |
| Base config is already set in the file: `STRATEGIES=[2,3]`, 10 phases (`PHASE_EXECUTION_MODE="auto"`), |
| 100% data, `BATCH_SIZE=48`, `NUM_WORKERS=12`, `STRATEGY_2_MAX_EPOCHS=100`, `STRATEGY_3_MAX_EPOCHS=120`, |
| `RUN_OPTUNA=False`, `STRATEGY2_CHECKPOINT_MODE="best"` (strategy 3 uses the strategy-2 base trained in |
| the same run β no checkpoint wiring needed). |
|
|
| **Validated at 128px:** DPT+ViT-tiny and UPerNet+Swin-tiny were dropped (weights locked to 224px) and |
| replaced with **PVTv2 + UPerNet** (b1 and b2), which run natively at 128px. Re-run |
| `python validate_models.py` to confirm all 6 pass on your box before launching. |
|
|
| --- |
|
|
| ## 1. SegFormer-B0 (transformer β SegFormer params, proj 192) |
|
|
| ```bash |
| cd /workspace |
| sed -i -E \ |
| -e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "mit_b0"/' \ |
| -e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "Segformer"/' \ |
| -e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 192/' \ |
| -e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \ |
| -e 's/^MODEL_NAME = .*/MODEL_NAME = "Segformer_B0"/' \ |
| -e 's#"2:100": "[^"]*"#"2:100": "params/segformer_b0/best_params_strat2.json"#' \ |
| -e 's#"3:100": "[^"]*"#"3:100": "params/segformer_b0/best_params_strat3.json"#' \ |
| foldsrunner_simplified_after_ablation.py |
| python foldsrunner_simplified_after_ablation.py |
| ``` |
|
|
| ## 2. U-Net + EfficientNet-B0 (CNN β U-Net params, proj 256) |
|
|
| ```bash |
| cd /workspace |
| sed -i -E \ |
| -e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "efficientnet-b0"/' \ |
| -e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "Unet"/' \ |
| -e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 256/' \ |
| -e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \ |
| -e 's/^MODEL_NAME = .*/MODEL_NAME = "Unet_EffB0"/' \ |
| -e 's#"2:100": "[^"]*"#"2:100": "params/unet_effb0/best_params_strat2.json"#' \ |
| -e 's#"3:100": "[^"]*"#"3:100": "params/unet_effb0/best_params_strat3.json"#' \ |
| foldsrunner_simplified_after_ablation.py |
| python foldsrunner_simplified_after_ablation.py |
| ``` |
|
|
| ## 3. DeepLabV3+ + ResNet34 (CNN β U-Net params, proj 256) |
|
|
| ```bash |
| cd /workspace |
| sed -i -E \ |
| -e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "resnet34"/' \ |
| -e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "DeepLabV3Plus"/' \ |
| -e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 256/' \ |
| -e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \ |
| -e 's/^MODEL_NAME = .*/MODEL_NAME = "DeepLabV3Plus_ResNet34"/' \ |
| -e 's#"2:100": "[^"]*"#"2:100": "params/deeplabv3plus_r34/best_params_strat2.json"#' \ |
| -e 's#"3:100": "[^"]*"#"3:100": "params/deeplabv3plus_r34/best_params_strat3.json"#' \ |
| foldsrunner_simplified_after_ablation.py |
| python foldsrunner_simplified_after_ablation.py |
| ``` |
|
|
| ## 4. LinkNet + MobileNetV3-Large (CNN β U-Net params, proj 256) |
|
|
| ```bash |
| cd /workspace |
| sed -i -E \ |
| -e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "timm-mobilenetv3_large_100"/' \ |
| -e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "Linknet"/' \ |
| -e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 256/' \ |
| -e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \ |
| -e 's/^MODEL_NAME = .*/MODEL_NAME = "Linknet_MobileNetV3"/' \ |
| -e 's#"2:100": "[^"]*"#"2:100": "params/linknet_mbv3/best_params_strat2.json"#' \ |
| -e 's#"3:100": "[^"]*"#"3:100": "params/linknet_mbv3/best_params_strat3.json"#' \ |
| foldsrunner_simplified_after_ablation.py |
| python foldsrunner_simplified_after_ablation.py |
| ``` |
|
|
| ## 5. UPerNet + PVTv2-b1 (transformer β SegFormer params, proj 192) |
|
|
| ```bash |
| cd /workspace |
| sed -i -E \ |
| -e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "tu-pvt_v2_b1"/' \ |
| -e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "UPerNet"/' \ |
| -e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 192/' \ |
| -e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \ |
| -e 's/^MODEL_NAME = .*/MODEL_NAME = "UPerNet_PVTv2_b1"/' \ |
| -e 's#"2:100": "[^"]*"#"2:100": "params/upernet_pvtv2_b1/best_params_strat2.json"#' \ |
| -e 's#"3:100": "[^"]*"#"3:100": "params/upernet_pvtv2_b1/best_params_strat3.json"#' \ |
| foldsrunner_simplified_after_ablation.py |
| python foldsrunner_simplified_after_ablation.py |
| ``` |
|
|
| ## 6. UPerNet + PVTv2-b2 (transformer β SegFormer params, proj 192) |
|
|
| ```bash |
| cd /workspace |
| sed -i -E \ |
| -e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "tu-pvt_v2_b2"/' \ |
| -e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "UPerNet"/' \ |
| -e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 192/' \ |
| -e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \ |
| -e 's/^MODEL_NAME = .*/MODEL_NAME = "UPerNet_PVTv2_b2"/' \ |
| -e 's#"2:100": "[^"]*"#"2:100": "params/upernet_pvtv2_b2/best_params_strat2.json"#' \ |
| -e 's#"3:100": "[^"]*"#"3:100": "params/upernet_pvtv2_b2/best_params_strat3.json"#' \ |
| foldsrunner_simplified_after_ablation.py |
| python foldsrunner_simplified_after_ablation.py |
| ``` |
|
|
| --- |
|
|
| ## After the runs β plots |
|
|
| ```bash |
| cd /workspace |
| python plot_s2_vs_s3.py |
| ``` |
|
|
| One titled figure per model β `runs/_plots/<MODEL_NAME>__s2_vs_s3.png` (6 panels: BIoU band, BIoU |
| contour, inference time, total training time, time per epoch, time to best checkpoint β S2 vs S3, |
| mean Β± std across the 10 phases, Ξ annotated), plus `runs/_plots/summary_s2_vs_s3.csv`. |
|
|
