| --- |
| 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 benchmark](https://huggingface.co/collections/ERISLab/fgir-backbones-fgvc13-cvpr-2026-6ab234cc19fcfd2de1f796a4) |
| (repo [`ERISLab/FGIR-Backbones`](https://huggingface.co/ERISLab/FGIR-Backbones)), which studies |
| backbones and pretraining recipes for fine-grained recognition. |
|
|
