card: 689 checkpoints, FGIRFT split to its own repo

README.md

@@ -19,15 +19,17 @@ visual margins and samples per class are scarce. Every checkpoint is an ImageNet
 the backbone frozen**; only a small set of adapter modules is trained, so a model is a ~340 MB file
 of which ~2 M parameters were actually learned.
 
-The repository gathers the checkpoints behind three lines of work from the ERIS Lab:
+The repository gathers the checkpoints behind two lines of work from the ERIS Lab:
 
 - **ILA** — the down-sampling inter-layer adapter, and **SAW**, a semantic-adaptation warmup added on
   top of it (the `ila_*` groups). ILA was introduced in the ECCV 2024 EFM workshop paper below.
 - **AAA** — the attention-aggregation adapter, which replaces ILA's convolutional down-sampler with a
   bottleneck attention that fixes the adapter's misplaced attention (the `aaa_*` groups).
-- **FGIRFT** — a full fine-tuning FGIR benchmark on 21 datasets, for reference (the `ft_*` groups).
 
-931 checkpoints, one seed per configuration, last epoch only. Everything is described in
+The full fine-tuning FGIR benchmark (FGIRFT) is now a separate repository,
+[ERISLab/FGIRFT](https://huggingface.co/ERISLab/FGIRFT).
+
+689 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-aaa-and-fgirft-6ab2dc3a730388cfe7a5f7b4).
@@ -57,7 +59,6 @@ spans several resolutions.
 | `aaa_main_224` / `aaa_main_448` / `aaa_main_672` | AAA | vit_b16 | The AAA finals (frozen / adapter / ILA++ / AAA arms), by resolution |
 | `aaa_fungi` | AAA (breadth) | vit_b16 | FungiTastic breadth probe — kept for completeness, not a paper result |
 | `medical_idrid`, `medical_petl` | AAA (medical) | vit_b16 | Medical-imaging PETL and IDRiD resolution runs |
-| `ft_224` / `ft_448` | FGIRFT | vit_b16 | Full fine-tunes with FGIR heads (GLSim, MAWS, PSM, CAL, MPNCOV, …) on 21 datasets |
 
 The ILA/SAW paper evaluates the `ila_main` and `ila_saw` serials; the AAA paper evaluates the
 `aaa_main_*` serials. `manifest.csv` maps every file to its dataset, method, serial, resolution,