card: FGIRFT full fine-tuning benchmark

README.md

@@ -0,0 +1,68 @@
+---
+pipeline_tag: image-classification
+library_name: fgir-zoo
+tags:
+- fine-grained
+- image-classification
+- vision-transformer
+- full-fine-tuning
+---
+
+# FGIRFT
+
+A full fine-tuning benchmark for fine-grained image recognition (FGIR). Every checkpoint is an
+ImageNet-21k **ViT-B/16 fine-tuned end to end** — the whole backbone is trained, not frozen — with a
+fine-grained classification head, across 21 fine-grained datasets. FGIRFT is the full fine-tuning
+counterpart to the ERIS Lab's parameter-efficient works (the ILA/SAW and AAA adapters in
+`ERISLab/FGIR-ViT`, which keep the backbone frozen and train only small adapters).
+
+242 checkpoints, one seed per configuration, last epoch only, grouped by input resolution: `ft_224`
+(111) and `ft_448` (131). Everything is described in `manifest.csv` and loadable by name with the
+standalone `fgir_zoo` library (no training repo needed).
+
+## What it holds
+
+ViT-B/16 fully fine-tuned at 224 or 448 px, each with one FGIR classification head: a plain linear
+classifier (`cls`), GLSim, MAWS (the FFVT head), PSM (the TransFG head), CAL, MPN-COV, and
+attention-rollout heads. The 21 datasets span general fine-grained sets (CUB, NABirds, Aircraft,
+Stanford Cars, Stanford Dogs, Oxford Flowers, Oxford Pets, Food-101, VegFru, DAFB, Moe) and the
+ultra-fine-grained leaf sets (Cotton, SoyAgeing and its five single-season subsets, SoyGene,
+SoyGlobal, SoyLocal). `num_classes` ranges from 37 to 3263. `manifest.csv` maps every file to its
+dataset, head, serial, resolution, `num_classes`, top-1 accuracy and sha256.
+
+## Load a checkpoint
+
+Standalone, no `fgir_vit` install — the model code is vendored inside `fgir_zoo`:
+
+```bash
+pip install git+https://github.com/arkel23/fgir-zoo.git
+```
+
+```python
+import fgir_zoo
+model = fgir_zoo.load('fgirvit/ft_448/aircraft_vit_b16_avg_cls_rollout_3')
+model.eval()
+# fgir_zoo.list_models(family='fgirvit', group='ft_448') lists every name; model.config holds the run's settings.
+```
+
+Or fetch the file directly and load it yourself:
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+path = hf_hub_download('ERISLab/FGIRFT', 'ft_448/aircraft_vit_b16_avg_cls_rollout_3.pth')
+ckpt = torch.load(path, map_location='cpu', weights_only=False)   # dict: config, model, accuracy, epoch
+```
+
+## Requirements
+
+- `torch>=2.5`, `timm==0.9.12` (the checkpoints were trained on it and the vendored model code forks
+  that version), `huggingface_hub`, `safetensors`, `einops`, `ml_collections`.
+
+## Related
+
+Part of the [ERISLab FGIRFT collection](https://huggingface.co/collections/ERISLab/fgirft-full-fine-tuning-benchmark-for-fgir-6ab2e8940f3a28ec7ba0bfbf).
+For the parameter-efficient adapter works (ILA, SAW, AAA) on a frozen ViT-B/16, see
+[`ERISLab/FGIR-ViT`](https://huggingface.co/ERISLab/FGIR-ViT). As a full fine-tuning line, FGIRFT sits
+close to the [`ERISLab/FGIR-Backbones`](https://huggingface.co/ERISLab/FGIR-Backbones) benchmark,
+which studies backbones and pretraining recipes for fine-grained recognition.