README.md

metadata

library_name: transformers
license: apache-2.0
base_model: microsoft/resnet-18
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: font-identifier
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9162162162162162

font-identifier

This model is a fine-tuned version of microsoft/resnet-18 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Accuracy: 0.9162
• Loss: 0.2760

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss
3.7676	1.0	24	0.0459	3.6832
3.607	2.0	48	0.0919	3.4400
3.2234	3.0	72	0.1919	3.0452
2.8944	4.0	96	0.3324	2.5182
2.1637	5.0	120	0.4351	2.0193
1.9347	6.0	144	0.5595	1.6222
1.6851	7.0	168	0.6297	1.3065
1.369	8.0	192	0.6919	1.0945
1.2987	9.0	216	0.7270	0.9188
1.1044	10.0	240	0.7541	0.8216
1.044	11.0	264	0.8	0.7295
1.0134	12.0	288	0.8270	0.6655
0.9284	13.0	312	0.8189	0.6212
0.8603	14.0	336	0.8216	0.5687
0.7748	15.0	360	0.8649	0.5291
0.8133	16.0	384	0.8324	0.5337
0.8379	17.0	408	0.8486	0.4993
0.751	18.0	432	0.8514	0.4632
0.8585	19.0	456	0.8162	0.4908
0.6627	20.0	480	0.8622	0.4358
0.6497	21.0	504	0.8486	0.4240
0.6422	22.0	528	0.8486	0.4143
0.5964	23.0	552	0.8676	0.3912
0.5793	24.0	576	0.8568	0.4026
0.5909	25.0	600	0.8838	0.3531
0.593	26.0	624	0.8811	0.3661
0.5957	27.0	648	0.8892	0.3674
0.5869	28.0	672	0.8892	0.3710
0.4999	29.0	696	0.8919	0.3422
0.4843	30.0	720	0.8946	0.3178
0.5352	31.0	744	0.8865	0.3129
0.4937	32.0	768	0.8973	0.3399
0.483	33.0	792	0.8973	0.2855
0.4265	34.0	816	0.9	0.3316
0.4412	35.0	840	0.8865	0.3273
0.4324	36.0	864	0.8973	0.3167
0.4681	37.0	888	0.9270	0.2944
0.4813	38.0	912	0.9135	0.2943
0.4585	39.0	936	0.9027	0.3019
0.4151	40.0	960	0.8892	0.3399
0.4351	41.0	984	0.9081	0.2623
0.4364	42.0	1008	0.9135	0.2892
0.4632	43.0	1032	0.9081	0.3086
0.3867	44.0	1056	0.9	0.2913
0.4007	45.0	1080	0.9135	0.2502
0.3848	46.0	1104	0.9162	0.2702
0.4061	47.0	1128	0.9162	0.2634
0.3901	48.0	1152	0.9054	0.2975
0.3794	49.0	1176	0.8973	0.2590
0.3583	50.0	1200	0.9162	0.2760

Framework versions

• Transformers 4.53.3
• Pytorch 2.7.1
• Datasets 4.0.0
• Tokenizers 0.21.4