FGIR-KD: how to choose your teacher for fine-grained image recognition

These are the checkpoints behind How to Choose Your Teacher for Fine Grained Image Recognition (arXiv:2605.15689), presented at the FGVC13 workshop at CVPR 2026. Small students (ResNet-18, LCNet, LeViT, ViT-T) are distilled from a range of fine-grained teachers to study which teacher helps most. teachers/ holds the teachers themselves; students/ the distilled students. The model code is shared with TGDA (arkel23/TGDA).

2083 checkpoints, one per configuration, each the last epoch of one training seed. Each file is a torch.save dict with config (the full training configuration), model (the state dict), accuracy and epoch, with no optimizer state. File names are the runs' experiment-log names, ending in the serial. Load them with fgir-zoo. The collection groups this repo with the paper.

Layout

Folder	Models	Teachers	Datasets	Files	Mean accuracy
teachers/serial_15	resnet101.a1_in1k, resnetv2_101.a1h_in1k, vgg19_bn	-	8	8	90.46
teachers/serial_16	convnext_base.fb_in22k, resnet101.a1_in1k, resnetv2_101.a1h_in1k, resnetv2_101x3_bit.goog_in21k, swin_base_patch4_window7_224.ms_in22k, van_b3, vgg19_bn, vit_b16	-	6	48	82.21
teachers/serial_17	convnext_base.fb_in22k, resnet101.a1_in1k, resnetv2_101.a1h_in1k, resnetv2_101x3_bit.goog_in21k, swin_base_patch4_window7_224.ms_in22k, van_b3, vgg19_bn, vit_b16	-	1	8	81.45
teachers/serial_18	convnext_base.fb_in22k, resnet101.a1_in1k, resnetv2_101.a1h_in1k, resnetv2_101x3_bit.goog_in21k, swin_base_patch4_window7_224.ms_in22k, vgg19_bn, vit_b16	-	1	7	78.83
teachers/serial_400	convnext_base.fb_in22k, resnet101.a1_in1k, resnetv2_101.a1h_in1k, resnetv2_101x3_bit.goog_in21k, swin_base_patch4_window7_224.ms_in22k, van_b3, vgg19_bn, vit_b16	-	1	9	71.90
teachers/serial_501	resnetv2_101x3_bit.goog_in21k	-	5	5	86.01
students/serial_1	resnet18	1	1	1	73.78
students/serial_2	lcnet_035	8	9	72	66.13
students/serial_3	lcnet_035	8	9	72	64.36
students/serial_4	lcnet_035	8	9	72	62.50
students/serial_7	levit_128s	8	8	57	63.23
students/serial_102	resnet18	1	1	1	63.62
students/serial_104	vit_t16	1	1	1	14.38
students/serial_105	lcnet_035	1	2	2	33.97
students/serial_106	levit_128s	1	2	2	39.34
students/serial_193	lcnet_035	1	1	1	76.86
students/serial_201	resnet18	8	10	72	74.73
students/serial_202	resnet18	8	9	65	71.92
students/serial_203	resnet18	8	13	104	72.22
students/serial_204	resnet18	8	13	104	66.47
students/serial_205	resnet18	8	13	104	70.14
students/serial_206	resnet18	8	13	104	64.09
students/serial_207	resnet18	8	13	104	68.60
students/serial_208	resnet18	8	13	104	63.21
students/serial_209	lcnet_035	8	13	104	55.43
students/serial_210	lcnet_035	8	13	104	54.11
students/serial_211	lcnet_035	8	13	104	53.87
students/serial_212	levit_128s	8	13	104	59.39
students/serial_213	levit_128s	8	13	103	57.04
students/serial_214	levit_128s	8	13	103	57.31
students/serial_215	lcnet_035	8	13	104	58.93
students/serial_216	levit_128s	8	13	101	60.95
students/serial_253	lcnet_035	1	1	1	77.43
students/serial_259	resnet18	1	1	1	85.47
students/serial_260	levit_128s	1	1	1	82.02
students/serial_305	levit_128s	8	1	8	63.62
students/serial_309	lcnet_035	7	1	7	61.52
students/serial_311	lcnet_035	1	1	1	0.52
students/serial_321	lcnet_035	8	1	8	60.81
students/serial_322	lcnet_035	8	1	8	54.30
students/serial_323	lcnet_035	8	1	8	0.64
students/serial_324	lcnet_035	8	1	8	51.13
students/serial_401	resnet18	8	1	8	70.81
students/serial_402	lcnet_035	8	1	8	61.19
students/serial_403	levit_128s	3	1	3	72.06
students/serial_551	resnet18	4	2	4	77.20
students/serial_552	lcnet_035	1	1	1	57.28
students/serial_553	levit_128s	8	5	40	75.94
students/serial_554	resnet18	1	1	1	54.37
students/serial_555	lcnet_035	2	2	2	66.50
students/serial_556	levit_128s	8	3	10	92.36
students/serial_999	lcnet_035	1	1	1	7.04

manifest.csv lists every file with its dataset, student, teacher, serial, seed, image size, class count, accuracy, SHA-256 and size.

Load a checkpoint and classify an image

import torch
from PIL import Image
from torchvision import transforms
from fgir_zoo import tgda

model = tgda.create_model('students/serial_203/cub_resnet18_vgg19_bn_cal_203')
cfg = model.config
tf = transforms.Compose([
    transforms.Resize((cfg.test_resize_size, cfg.test_resize_size),
                      interpolation=transforms.InterpolationMode.BICUBIC),
    transforms.CenterCrop(cfg.student_image_size or 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)
print(logits.argmax(-1).item(), logits.softmax(-1).max().item())  # 50 0.9423

Accuracy of the released checkpoints

Top-1 accuracy (%) stored in each file, on the dataset's test split at the end of training. Each value is one seed; the paper reports its own aggregates, so its tables can differ slightly. Per-file accuracy for the 2,000 students is in manifest.csv; the table below covers the teachers.

teachers/

Model	aircraft	bach	bdctkidney	cars	cub	dogs	flowers	idriddr	idridme	imagenet1k	lublung	moe	nabirds	pets
convnext_base.fb_in22k		92.50	99.96					62.14	82.52	77.94	96.71
resnet101.a1_in1k		77.50	99.96	91.94			96.41	54.37	77.67	83.91	94.08
resnetv2_101.a1h_in1k		67.50	99.96					50.49	72.82	77.70	92.34		89.05
resnetv2_101x3_bit.goog_in21k		87.50	99.90					57.14	89.29	47.81	96.23
swin_base_patch4_window7_224.ms_in22k		95.00	99.96					59.22	82.52	71.39	96.86
van_b3		92.50	100.00					54.37	82.52	85.46	96.06
vgg19_bn	88.87	91.25	100.00		86.93	83.83		60.19	81.55	54.08	94.57	93.71		92.94
vit_b16		86.25	100.00					51.46	82.52	76.65	96.64

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

@inproceedings{gosal_choose_teacher_2026,
  title         = {How to Choose Your Teacher for Fine Grained Image Recognition},
  author        = {Gosal, Oswin and Rios, Edwin Arkel and Surya, Augusto Christian and Mikael, Fernando 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        = {2605.15689},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  doi           = {10.48550/arXiv.2605.15689},
  url           = {https://arxiv.org/abs/2605.15689}
}