ila_saw_resnet_448: add the soyageingr4/r5/r6 FZ linear probes (661); manifest and card

README.md

@@ -31,7 +31,7 @@ The repository gathers the checkpoints behind two lines of work from the ERIS La
 The full fine-tuning FGIR benchmark (FGIRFT) is now a separate repository,
 [ERISLab/FGIRFT](https://huggingface.co/ERISLab/FGIRFT).
 
-738 checkpoints, one seed per configuration, last epoch only. Everything is described in
+741 checkpoints, one seed per configuration, last epoch only. Everything is described in
 `manifest.csv` and loadable by name with the standalone `fgir_zoo` library (no training repo needed).
 
 Part of the [ERISLab FGIR-ViT collection](https://huggingface.co/collections/ERISLab/fgir-vit-ila-saw-and-aaa-6ab2dc3a730388cfe7a5f7b4).
@@ -81,7 +81,7 @@ soyageingr1/r3/r4/r5/r6, soygene, soyglobal and soylocal.
 | 295 | Method: adapter + ILA + SAW | `ila_dso_cls_adapter_saw_fz` | 10 |
 | 290 | Baseline: adapter | `cls_adapter_fz` | 10 |
 | 293 | Baseline: adapter + ILA | `ila_dso_cls_adapter_fz` | 10 |
-| 661 | Baseline: FZ, frozen linear probe | `fz` | 7 |
+| 661 | Baseline: FZ, frozen linear probe | `fz` | 10 |
 | 291 | SAW stage-1 warmup for 292 | `cls_adapter_supcon_fz` | 1 |
 | 294 | SAW stage-1 warmup for 295 | `ila_dso_cls_adapter_supcon_fz` | 1 |
 
@@ -89,8 +89,8 @@ soyageingr1/r3/r4/r5/r6, soygene, soyglobal and soylocal.
 - **295** does the same with the ILA down-sampler, starting from the 294 warmup.
 - **290** trains the same adapter from a fresh initialisation, without SAW.
 - **293** trains adapter + ILA from a fresh initialisation, without SAW.
-- **661** (FZ) keeps the whole backbone frozen and trains only a linear head. Seven of the ten
-  datasets are on disk (no soyageingr4, r5 or r6); wandb `nycu_pcs/AAA` holds all 30 runs.
+- **661** (FZ) keeps the whole backbone frozen and trains only a linear head. All ten datasets
+  are released; wandb `nycu_pcs/AAA` holds all 30 runs.
 - **291** and **294** are the supervised-contrastive warmups on the leaves set (100 epochs). Their
   head is a projection, so the forward returns a 2048-d embedding and the stored `accuracy`
   (0.03 and 0.00) is not a classification score.

ila_saw_resnet_448/soyageingr4_peresnet50_fz_661.pth

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ila_saw_resnet_448/soyageingr5_peresnet50_fz_661.pth

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ila_saw_resnet_448/soyageingr6_peresnet50_fz_661.pth

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manifest.csv

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