README.md

---
pipeline_tag: image-classification
library_name: pytorch
tags:
- fine-grained-image-recognition
- image-classification
- counterfactual-attention-learning
- timm
---

# FGIR-Backbones: backbones × training and evaluation settings on 17 fine-grained datasets

These are the checkpoints behind *A Large-Scale Study on the Accuracy vs Cost Trade-offs of
Training and Evaluation Settings in Fine-Grained Image Recognition*
([arXiv:2605.18700](https://arxiv.org/abs/2605.18700)), presented at the FGVC13 workshop at CVPR
2026. The study trains 16 ImageNet-pretrained backbones (CNNs and vision transformers) on 17
fine-grained image recognition (FGIR) datasets under four training strategies, plus a 448 px
set of runs, and compares accuracy against training and inference cost. Training code:
[arkel23/FGIR-Backbones](https://github.com/arkel23/FGIR-Backbones). Loader:
[arkel23/fgir-zoo](https://github.com/arkel23/fgir-zoo).

825 checkpoints, one per (dataset, backbone, strategy, image size), each from one training seed.
The collection [FGIR-Backbones (FGVC13 @ CVPR 2026)](https://huggingface.co/collections/ERISLab/fgir-backbones-fgvc13-cvpr-2026-6ab234cc19fcfd2de1f796a4) groups this repo with the
paper.

## Strategies

| Strategy | Meaning |
|---|---|
| `ft` | full fine-tune |
| `fz` | frozen backbone, linear head |
| `cal` | CAL (counterfactual attention learning) |
| `cal_cm` | CALMix (CAL with cross-image discriminative-region mixing) |

CAL-NC and CALMix-NC, the paper's no-crop evaluation variants, are not separate files. They
evaluate a `cal` or `cal_cm` checkpoint without the second forward pass on the
attention-guided crop (`cal_ap_only=True` in the config).

## Layout

One folder per strategy and image size, files named `{dataset}_{model_name}_{strategy}.pth`.
`manifest.csv` lists every file with its dataset, backbone, strategy, image size, training serial
and seed, class count, accuracy, SHA-256 and size.

| Folder | Strategy | Image size | Serial | Backbones | Datasets | Files |
|---|---|---|---|---|---|---|
| `ft_224` | `ft` | 224 | 1, 5 | 16 | 17 | 181 |
| `fz_224` | `fz` | 224 | 1, 5 | 16 | 17 | 181 |
| `cal_224` | `cal` | 224 | 1, 5 | 16 | 17 | 181 |
| `cal_cm_224` | `cal_cm` | 224 | 8 | 16 | 4 | 64 |
| `ft_384` | `ft` | 384 | 3, 6 | 16 | 4 | 64 |
| `fz_384` | `fz` | 384 | 3, 6 | 16 | 4 | 64 |
| `cal_384` | `cal` | 384 | 3, 6 | 16 | 4 | 64 |
| `cal_448` | `cal` | 448 | 15 | 5 | 3 | 13 |
| `cal_cm_448` | `cal_cm` | 448 | 11 | 5 | 3 | 13 |

Serial is the run group in the paper's experiment log. The 9 core backbones (VGG-19,
ResNet-101, ResNetV2-101, BiT-M ResNetV2-101x3, ViT-B/16, BEiTv2-B/16, Swin-B, ConvNeXt-B,
VAN-B3) are trained on all 17 datasets at 224 px; the other 7 on aircraft, cub, soygene and
soylocal. At 384 px the Swin backbones use their 384 px variants
(`swin_*_window12_384*`). The 448 px folders hold ViT-B/16 and four torchvision ResNets
(`resnet18`, `tv_resnet34`, `tv_resnet50`, `tv_resnet101`) on aircraft, cars and cub.

Each file is a `torch.save` dict with four keys: `config` (the full training configuration, an
`argparse.Namespace`), `model` (the state dict), `accuracy` and `epoch`. There is no optimizer
state. The config drives the rebuild, so a file loads without the training repository.

## Load a checkpoint and classify an image

`fgir_zoo` holds a frozen copy of the model code and pins `timm==0.9.12`.

```python
import torch
from PIL import Image
from torchvision import transforms
from fgir_zoo import backbones

model = backbones.create_model('cal_224/cub_vit_b16_cal')  # or model_name='vit_b16', dataset='cub', strategy='cal'
cfg = model.config
tf = transforms.Compose([
    transforms.Resize((cfg.test_resize_size, cfg.test_resize_size),
                      interpolation=transforms.InterpolationMode.BICUBIC),
    transforms.CenterCrop(cfg.image_size),
    transforms.ToTensor(),
    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
# a CUB-200-2011 test image, class index 50 (051.Horned_Grebe)
x = tf(Image.open('Horned_Grebe_0050_34561.jpg').convert('RGB')).unsqueeze(0)
with torch.no_grad():
    logits, _ = model(x)          # CAL returns (logits, attention crops)
    model.model.ap_only = True    # CAL-NC: skip the forward pass on the crop
    logits_nc = model(x)
print(logits.argmax(-1).item(), logits.softmax(-1).max().item())        # 50 0.9896
print(logits_nc.argmax(-1).item(), logits_nc.softmax(-1).max().item())  # 50 0.9902
```

The raw file is one Hub call away:

```python
import torch
from huggingface_hub import hf_hub_download

path = hf_hub_download('ERISLab/FGIR-Backbones', 'cal_224/cub_vit_b16_cal.pth')
ckpt = torch.load(path, map_location='cpu', weights_only=False)
ckpt['config'].model_name, ckpt['accuracy']
```

## Accuracy of the released checkpoints

Top-1 accuracy (%) stored in each file: the run's final accuracy on the dataset's test split,
as recorded in the experiment log. Each value is one seed; the paper reports means over two or
three seeds, so its tables differ slightly. The 17 files below 10% are all BEiTv2 or soyglobal
(1,938 classes) cells that fail to train on every seed; they are the study's results, not
damaged files.

### `ft_224`: full fine-tune, 224 px

| Backbone | aircraft | cars | cotton | cub | dafb | dogs | flowers | food | inat17 | moe | nabirds | pets | soyageing | soygene | soyglobal | soylocal | vegfru |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| `beitv2_base_patch16_224_in22k` | 85.33 | 88.84 | 2.92 | 86.95 | 92.56 | 88.87 | 98.59 | 91.49 | 71.54 | 94.05 | 87.90 | 77.65 | 43.31 | 9.42 | 2.94 | 14.50 | 95.95 |
| `convnext_base` | 86.35 |  |  | 81.96 |  |  |  |  |  |  |  |  |  | 30.32 |  | 19.50 |  |
| `convnext_base_in22k` | 87.49 | 80.10 | 37.08 | 84.52 | 87.92 | 86.77 | 99.46 | 90.65 | 67.40 | 93.24 | 83.84 | 92.94 | 35.86 | 36.88 | 2.89 | 26.17 | 94.41 |
| `convnext_large_in22k` | 83.74 |  |  | 88.30 |  |  |  |  |  |  |  |  |  | 33.67 |  | 33.83 |  |
| `deit3_base_patch16_224` | 84.79 |  |  | 81.86 |  |  |  |  |  |  |  |  |  | 45.42 |  | 24.50 |  |
| `deit3_base_patch16_224_in21ft1k` | 84.82 |  |  | 87.76 |  |  |  |  |  |  |  |  |  | 22.93 |  | 27.50 |  |
| `deit3_large_patch16_224_in21ft1k` | 85.99 |  |  | 90.30 |  |  |  |  |  |  |  |  |  | 49.39 |  | 20.00 |  |
| `resnet101` | 81.85 | 85.97 | 12.50 | 76.89 | 86.52 | 91.06 | 90.91 | 84.49 | 59.22 | 89.16 | 74.86 | 92.64 | 53.43 | 26.64 | 7.28 | 16.17 | 85.07 |
| `resnetv2_101` | 82.60 | 86.94 | 16.25 | 76.91 | 89.37 | 90.31 | 92.36 | 85.32 | 59.79 | 92.80 | 74.38 | 92.04 | 52.93 | 30.81 | 8.26 | 13.67 | 86.30 |
| `resnetv2_101x3_bitm_in21k` | 86.08 | 88.57 | 44.17 | 88.78 | 91.56 | 88.86 | 99.37 | 90.61 | 69.90 | 95.45 | 86.20 | 93.95 | 68.93 | 50.79 | 23.05 | 38.00 | 95.69 |
| `swin_base_patch4_window7_224` | 85.42 |  |  | 84.47 |  |  |  |  |  |  |  |  |  | 25.71 |  | 24.67 |  |
| `swin_base_patch4_window7_224_in22k` | 87.73 | 90.40 | 49.17 | 90.56 | 92.35 | 88.22 | 99.63 | 92.37 | 73.54 | 95.72 | 88.86 | 94.74 | 50.97 | 31.80 | 7.41 | 26.33 | 96.11 |
| `swin_large_patch4_window7_224_in22k` | 88.45 |  |  | 91.15 |  |  |  |  |  |  |  |  |  | 36.64 |  | 30.33 |  |
| `van_b3` | 86.47 | 89.39 | 41.67 | 79.32 | 91.26 | 95.05 | 96.05 | 88.46 | 64.61 | 94.67 | 82.50 | 95.07 | 65.54 | 37.49 | 8.34 | 20.83 | 90.27 |
| `vgg19_bn` | 78.55 | 86.56 | 30.42 | 76.13 | 85.75 | 85.58 | 94.28 | 81.71 | 53.77 | 91.68 | 72.06 | 92.29 | 43.78 | 53.92 | 21.26 | 31.67 | 82.38 |
| `vit_b16` | 82.12 | 86.69 | 50.42 | 87.83 | 89.96 | 90.89 | 99.32 | 89.66 | 66.06 | 94.90 | 83.77 | 93.89 | 37.25 | 31.16 | 18.78 | 25.83 | 93.65 |

### `fz_224`: frozen backbone, linear head, 224 px

| Backbone | aircraft | cars | cotton | cub | dafb | dogs | flowers | food | inat17 | moe | nabirds | pets | soyageing | soygene | soyglobal | soylocal | vegfru |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| `beitv2_base_patch16_224_in22k` | 55.36 | 64.36 | 29.58 | 90.49 | 41.45 | 89.95 | 99.48 | 90.38 | 66.58 | 73.80 | 87.18 | 93.73 | 26.16 | 15.92 | 6.66 | 22.50 | 94.86 |
| `convnext_base` | 53.92 |  |  | 66.36 |  |  |  |  |  |  |  |  |  | 26.42 |  | 17.50 |  |
| `convnext_base_in22k` | 62.20 | 67.43 | 40.00 | 87.73 | 55.28 | 89.07 | 99.53 | 90.00 | 61.27 | 84.54 | 81.24 | 93.89 | 38.99 | 32.52 | 14.93 | 36.33 | 95.21 |
| `convnext_large_in22k` | 62.89 |  |  | 89.28 |  |  |  |  |  |  |  |  |  | 36.77 |  | 37.17 |  |
| `deit3_base_patch16_224` | 61.36 |  |  | 69.81 |  |  |  |  |  |  |  |  |  | 23.68 |  | 21.83 |  |
| `deit3_base_patch16_224_in21ft1k` | 65.92 |  |  | 80.01 |  |  |  |  |  |  |  |  |  | 23.82 |  | 30.00 |  |
| `deit3_large_patch16_224_in21ft1k` | 70.87 |  |  | 82.34 |  |  |  |  |  |  |  |  |  | 26.61 |  | 26.17 |  |
| `resnet101` | 44.07 | 46.75 | 20.83 | 62.96 | 36.08 | 87.06 | 83.74 | 57.45 | 28.52 | 68.84 | 51.90 | 90.46 | 24.12 | 13.63 | 6.45 | 12.83 | 66.72 |
| `resnetv2_101` | 46.47 | 47.59 | 26.25 | 58.72 | 32.05 | 84.59 | 84.34 | 58.00 | 26.34 | 75.23 | 46.32 | 89.29 | 21.82 | 16.57 | 6.91 | 23.33 | 64.83 |
| `resnetv2_101x3_bitm_in21k` | 52.15 | 62.98 | 37.92 | 87.26 | 50.53 | 89.35 | 99.27 | 86.52 | 56.47 | 82.40 | 80.69 | 92.94 | 41.90 | 28.41 | 16.24 | 27.50 | 93.85 |
| `swin_base_patch4_window7_224` | 59.92 |  |  | 76.11 |  |  |  |  |  |  |  |  |  | 29.97 |  | 27.00 |  |
| `swin_base_patch4_window7_224_in22k` | 68.02 | 76.20 | 40.83 | 91.04 | 59.38 | 88.67 | 99.63 | 91.11 | 67.47 | 86.75 | 87.48 | 94.33 | 38.16 | 36.60 | 21.67 | 35.00 | 96.08 |
| `swin_large_patch4_window7_224_in22k` | 67.75 |  |  | 90.61 |  |  |  |  |  |  |  |  |  | 39.50 |  | 39.67 |  |
| `van_b3` | 56.02 | 61.85 | 36.25 | 70.02 | 41.19 | 95.34 | 92.29 | 70.04 | 31.61 | 80.12 | 58.61 | 92.70 | 32.24 | 21.92 | 10.47 | 24.00 | 76.92 |
| `vgg19_bn` | 47.58 | 47.28 | 28.33 | 62.00 | 31.83 | 85.45 | 86.66 | 60.66 | 29.33 | 76.08 | 51.45 | 89.48 | 32.18 | 20.68 | 10.32 | 24.33 | 71.06 |
| `vit_b16` | 58.21 | 62.37 | 31.67 | 86.19 | 65.44 | 87.75 | 98.70 | 82.78 | 53.24 | 88.21 | 79.13 | 91.47 | 40.55 | 17.31 | 13.52 | 23.17 | 90.24 |

### `cal_224`: CAL (counterfactual attention learning), 224 px

| Backbone | aircraft | cars | cotton | cub | dafb | dogs | flowers | food | inat17 | moe | nabirds | pets | soyageing | soygene | soyglobal | soylocal | vegfru |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| `beitv2_base_patch16_224_in22k` | 90.82 | 92.14 | 16.67 | 89.39 | 94.88 | 83.29 | 94.54 | 92.63 | 75.43 | 96.50 | 89.75 | 95.04 | 71.52 | 69.51 | 41.38 | 34.00 | 93.87 |
| `convnext_base` | 90.46 |  |  | 87.50 |  |  |  |  |  |  |  |  |  | 64.35 |  | 14.67 |  |
| `convnext_base_in22k` | 93.04 | 94.44 | 57.50 | 91.53 | 94.67 | 90.15 | 99.50 | 92.76 | 75.53 | 96.84 | 90.59 | 94.96 | 82.79 | 69.12 | 33.83 | 36.00 | 95.90 |
| `convnext_large_in22k` | 92.62 |  |  | 91.47 |  |  |  |  |  |  |  |  |  | 69.09 |  | 37.17 |  |
| `deit3_base_patch16_224` | 90.40 |  |  | 87.28 |  |  |  |  |  |  |  |  |  | 73.61 |  | 32.50 |  |
| `deit3_base_patch16_224_in21ft1k` | 91.06 |  |  | 89.18 |  |  |  |  |  |  |  |  |  | 54.68 |  | 34.17 |  |
| `deit3_large_patch16_224_in21ft1k` | 92.62 |  |  | 91.34 |  |  |  |  |  |  |  |  |  | 73.77 |  | 34.50 |  |
| `resnet101` | 82.21 | 89.99 | 35.42 | 85.85 | 89.97 | 92.17 | 96.83 | 87.11 | 62.52 | 95.07 | 84.94 | 93.92 | 54.02 | 31.86 | 23.89 | 27.17 | 91.41 |
| `resnetv2_101` | 81.82 | 91.74 | 30.83 | 85.66 | 89.41 | 91.18 | 96.42 | 86.85 | 62.44 | 95.14 | 85.25 | 93.32 | 48.91 | 31.23 | 21.50 | 25.17 | 90.47 |
| `resnetv2_101x3_bitm_in21k` | 91.69 | 94.02 | 42.92 | 89.73 | 94.66 | 88.68 | 99.24 | 90.54 | 72.25 | 95.07 | 88.34 | 93.89 | 57.54 | 72.99 | 32.97 | 36.50 | 94.28 |
| `swin_base_patch4_window7_224` | 91.48 |  |  | 86.40 |  |  |  |  |  |  |  |  |  | 69.69 |  | 20.83 |  |
| `swin_base_patch4_window7_224_in22k` | 91.96 | 94.27 | 42.92 | 90.82 | 94.12 | 88.72 | 99.63 | 92.73 | 76.02 | 97.04 | 90.14 | 95.31 | 54.67 | 76.70 | 50.65 | 38.00 | 95.98 |
| `swin_large_patch4_window7_224_in22k` | 92.59 |  |  | 91.51 |  |  |  |  |  |  |  |  |  | 76.14 |  | 39.83 |  |
| `van_b3` | 92.47 | 94.49 | 49.58 | 88.16 | 94.78 | 95.44 | 97.84 | 90.32 | 72.25 | 96.87 | 87.61 | 95.18 | 69.70 | 64.96 | 23.94 | 27.00 | 93.26 |
| `vgg19_bn` | 88.75 | 93.47 | 42.92 | 84.31 | 92.33 | 86.38 | 98.08 | 87.83 | 62.85 | 94.90 | 84.47 | 92.59 | 66.65 | 70.02 | 20.81 | 27.17 | 91.14 |
| `vit_b16` | 85.45 | 90.55 | 34.17 | 88.57 | 91.82 | 91.62 | 99.28 | 90.65 | 68.25 | 96.13 | 86.31 | 94.44 | 36.38 | 52.10 | 21.09 | 25.00 | 94.19 |

### `cal_cm_224`: CALMix (CAL with cross-image discriminative-region mixing), 224 px

| Backbone | aircraft | cub | soygene | soylocal |
|---|---|---|---|---|
| `beitv2_base_patch16_224_in22k` | 91.90 | 91.08 | 71.72 | 39.33 |
| `convnext_base` | 91.72 | 89.26 | 62.55 | 41.17 |
| `convnext_base_in22k` | 92.95 | 92.03 | 67.65 | 45.33 |
| `convnext_large_in22k` | 93.40 | 91.97 | 70.21 | 48.67 |
| `deit3_base_patch16_224` | 90.73 | 87.87 | 75.62 | 55.50 |
| `deit3_base_patch16_224_in21ft1k` | 91.15 | 89.35 | 66.65 | 47.17 |
| `deit3_large_patch16_224_in21ft1k` | 93.19 | 91.42 | 77.47 | 52.00 |
| `resnet101` | 86.41 | 87.54 | 39.00 | 24.83 |
| `resnetv2_101` | 85.33 | 86.57 | 34.09 | 26.33 |
| `resnetv2_101x3_bitm_in21k` | 92.02 | 89.75 | 70.54 | 40.83 |
| `swin_base_patch4_window7_224` | 90.91 | 87.38 | 67.18 | 46.33 |
| `swin_base_patch4_window7_224_in22k` | 92.68 | 91.06 | 77.65 | 50.50 |
| `swin_large_patch4_window7_224_in22k` | 92.50 | 91.44 | 76.43 | 52.17 |
| `van_b3` | 92.74 | 88.94 | 63.48 | 37.17 |
| `vgg19_bn` | 92.50 | 87.25 | 68.90 | 45.67 |
| `vit_b16` | 85.87 | 89.77 | 58.66 | 39.67 |

### `ft_384`: full fine-tune, 384 px

| Backbone | aircraft | cub | soygene | soylocal |
|---|---|---|---|---|
| `beitv2_base_patch16_224_in22k` | 76.66 | 81.57 | 7.43 | 9.33 |
| `convnext_base` | 89.50 | 83.24 | 40.16 | 22.17 |
| `convnext_base_in22k` | 88.57 | 78.46 | 40.47 | 28.67 |
| `convnext_large_in22k` | 84.13 | 87.66 | 51.49 | 32.67 |
| `deit3_base_patch16_224` | 87.04 | 84.52 | 62.12 | 34.50 |
| `deit3_base_patch16_224_in21ft1k` | 87.40 | 87.69 | 29.98 | 31.67 |
| `deit3_large_patch16_224_in21ft1k` | 88.27 | 91.13 | 60.82 | 31.00 |
| `resnet101` | 86.65 | 80.22 | 37.72 | 13.67 |
| `resnetv2_101` | 85.60 | 80.05 | 40.22 | 16.83 |
| `resnetv2_101x3_bitm_in21k` | 89.32 | 89.77 | 60.61 | 42.83 |
| `swin_base_patch4_window12_384` | 88.06 | 85.35 | 30.81 | 25.50 |
| `swin_base_patch4_window12_384_in22k` | 90.31 | 91.09 | 44.20 | 30.83 |
| `swin_large_patch4_window12_384_in22k` | 89.95 | 92.15 | 40.74 | 31.33 |
| `van_b3` | 88.36 | 75.09 | 38.92 | 22.83 |
| `vgg19_bn` | 79.78 | 75.49 | 58.06 | 34.33 |
| `vit_b16` | 85.84 | 88.99 | 46.00 | 25.50 |

### `fz_384`: frozen backbone, linear head, 384 px

| Backbone | aircraft | cub | soygene | soylocal |
|---|---|---|---|---|
| `beitv2_base_patch16_224_in22k` | 9.03 | 5.82 | 1.40 | 7.67 |
| `convnext_base` | 50.53 | 57.94 | 34.19 | 20.67 |
| `convnext_base_in22k` | 61.63 | 82.31 | 39.35 | 41.33 |
| `convnext_large_in22k` | 61.27 | 85.83 | 41.45 | 40.83 |
| `deit3_base_patch16_224` | 64.00 | 77.94 | 36.43 | 25.50 |
| `deit3_base_patch16_224_in21ft1k` | 67.48 | 85.57 | 33.52 | 29.00 |
| `deit3_large_patch16_224_in21ft1k` | 70.99 | 87.25 | 31.80 | 31.00 |
| `resnet101` | 50.65 | 66.00 | 14.43 | 10.33 |
| `resnetv2_101` | 52.66 | 60.61 | 25.80 | 25.00 |
| `resnetv2_101x3_bitm_in21k` | 57.76 | 89.25 | 35.16 | 30.50 |
| `swin_base_patch4_window12_384` | 62.92 | 76.96 | 36.42 | 30.00 |
| `swin_base_patch4_window12_384_in22k` | 72.10 | 91.37 | 41.86 | 35.50 |
| `swin_large_patch4_window12_384_in22k` | 70.42 | 90.59 | 46.40 | 43.50 |
| `van_b3` | 53.92 | 58.70 | 24.25 | 23.67 |
| `vgg19_bn` | 55.36 | 62.63 | 30.77 | 23.67 |
| `vit_b16` | 49.83 | 85.52 | 15.61 | 22.00 |

### `cal_384`: CAL (counterfactual attention learning), 384 px

| Backbone | aircraft | cub | soygene | soylocal |
|---|---|---|---|---|
| `beitv2_base_patch16_224_in22k` | 86.77 | 84.95 | 75.78 | 39.50 |
| `convnext_base` | 91.90 | 90.14 | 77.50 | 20.83 |
| `convnext_base_in22k` | 94.87 | 92.23 | 80.96 | 41.33 |
| `convnext_large_in22k` | 93.94 | 92.46 | 80.64 | 42.17 |
| `deit3_base_patch16_224` | 92.65 | 88.38 | 80.98 | 41.00 |
| `deit3_base_patch16_224_in21ft1k` | 93.13 | 89.56 | 67.06 | 39.83 |
| `deit3_large_patch16_224_in21ft1k` | 94.09 | 91.16 | 82.95 | 43.17 |
| `resnet101` | 87.76 | 89.09 | 51.21 | 31.00 |
| `resnetv2_101` | 87.46 | 88.16 | 51.13 | 24.17 |
| `resnetv2_101x3_bitm_in21k` | 92.35 | 90.87 | 78.52 | 35.83 |
| `swin_base_patch4_window12_384` | 93.13 | 87.61 | 78.36 | 19.67 |
| `swin_base_patch4_window12_384_in22k` | 93.49 | 91.85 | 83.16 | 40.00 |
| `swin_large_patch4_window12_384_in22k` | 94.09 | 91.73 | 82.78 | 42.50 |
| `van_b3` | 93.46 | 89.40 | 76.44 | 20.50 |
| `vgg19_bn` | 89.89 | 86.87 | 78.43 | 28.67 |
| `vit_b16` | 89.32 | 89.80 | 68.20 | 14.33 |

### `cal_448`: CAL (counterfactual attention learning), 448 px

| Backbone | aircraft | cars | cub |
|---|---|---|---|
| `resnet18` | 92.41 | 94.19 | 87.47 |
| `tv_resnet101` | 94.63 | 95.01 | 89.92 |
| `tv_resnet34` | 93.55 | 94.02 | 88.44 |
| `tv_resnet50` | 94.54 | 94.94 | 89.56 |
| `vit_b16` |  |  | 90.71 |

### `cal_cm_448`: CALMix (CAL with cross-image discriminative-region mixing), 448 px

| Backbone | aircraft | cars | cub |
|---|---|---|---|
| `resnet18` | 93.13 | 93.94 | 88.18 |
| `tv_resnet101` | 94.45 | 95.22 | 90.27 |
| `tv_resnet34` | 93.97 | 94.79 | 89.16 |
| `tv_resnet50` | 94.63 | 94.99 | 89.63 |
| `vit_b16` |  |  | 90.82 |

## Requirements

- `fgir-zoo` (`pip install git+https://github.com/arkel23/fgir-zoo.git`), which pins
  `timm==0.9.12`
- `torch` (checked with 2.5.1)

## Citation

```bibtex
@inproceedings{rios_large-scale_2026,
  title     = {A Large-Scale Study on the Accuracy vs Cost Trade-offs of Training and Evaluation
               Settings in Fine-Grained Image Recognition},
  author    = {Rios, Edwin Arkel and Surya, Augusto Christian and Gosal, Oswin and Mikael, Fernando and
               Nicole, Mary Madeline and Jang, Kisoon and Lai, Bo-Cheng and Hu, Min-Chun},
  booktitle = {The 13th Workshop on Fine-Grained Visual Categorization (FGVC13) at CVPR 2026},
  note      = {Non-archival extended abstract},
  year      = {2026},
  eprint    = {2605.18700},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  doi       = {10.48550/arXiv.2605.18700},
  url       = {https://arxiv.org/abs/2605.18700}
}
```