metadata

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: 0.963265306122449

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:

Loss: 0.1172
Accuracy: 0.9633

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
4.0243	0.98	30	3.9884	0.0204
3.7051	1.98	61	3.6012	0.0776
3.2036	2.99	92	2.9556	0.2939
2.6413	4.0	123	2.3054	0.4531
2.1015	4.98	153	1.7366	0.5224
1.6508	5.98	184	1.3509	0.6367
1.3986	6.99	215	1.0938	0.7163
1.1918	8.0	246	0.9012	0.7735
1.0633	8.98	276	0.7464	0.8143
0.8771	9.98	307	0.6569	0.8306
0.8309	10.99	338	0.5536	0.8551
0.7093	12.0	369	0.4795	0.8796
0.6579	12.98	399	0.4176	0.8837
0.5827	13.98	430	0.3888	0.8980
0.5418	14.99	461	0.3255	0.9122
0.5102	16.0	492	0.3139	0.9265
0.472	16.98	522	0.3141	0.9163
0.4273	17.98	553	0.2673	0.9245
0.384	18.99	584	0.2487	0.9265
0.3917	20.0	615	0.2353	0.9388
0.418	20.98	645	0.2113	0.9490
0.3662	21.98	676	0.2095	0.9327
0.3258	22.99	707	0.2139	0.9429
0.3268	24.0	738	0.1962	0.9449
0.3048	24.98	768	0.1935	0.9408
0.2696	25.98	799	0.2112	0.9408
0.2524	26.99	830	0.2310	0.9306
0.2491	28.0	861	0.1827	0.9449
0.2542	28.98	891	0.1720	0.9592
0.2898	29.98	922	0.1605	0.9490
0.2298	30.99	953	0.1326	0.9633
0.2137	32.0	984	0.1438	0.9571
0.2002	32.98	1014	0.1379	0.9551
0.2013	33.98	1045	0.1261	0.9653
0.1862	34.99	1076	0.1674	0.9408
0.1993	36.0	1107	0.1423	0.9571
0.2063	36.98	1137	0.1406	0.9592
0.2088	37.98	1168	0.1717	0.9429
0.1711	38.99	1199	0.1539	0.9510
0.1804	40.0	1230	0.1421	0.9571
0.1793	40.98	1260	0.0765	0.9776
0.2139	41.98	1291	0.1859	0.9449
0.1678	42.99	1322	0.1067	0.9796
0.1675	44.0	1353	0.0985	0.9735
0.1681	44.98	1383	0.1093	0.9653
0.1625	45.98	1414	0.1402	0.9592
0.1987	46.99	1445	0.1250	0.9673
0.1728	48.0	1476	0.1293	0.9633
0.1337	48.78	1500	0.1172	0.9633

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.0.0
Datasets 2.12.0
Tokenizers 0.14.1