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: 1

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: 0.0010
Accuracy: 1.0

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
3.2404	0.9524	15	3.1135	0.06
2.7846	1.9683	31	2.4576	0.33
2.3956	2.9841	47	1.7152	0.58
1.6171	4.0	63	1.0931	0.775
1.2882	4.9524	78	0.6347	0.85
0.7191	5.9683	94	0.3957	0.94
0.5196	6.9841	110	0.2080	0.965
0.3999	8.0	126	0.1480	0.965
0.2476	8.9524	141	0.0934	0.985
0.2176	9.9683	157	0.0768	0.99
0.194	10.9841	173	0.0365	0.995
0.1572	12.0	189	0.0616	0.985
0.1381	12.9524	204	0.0640	0.985
0.1291	13.9683	220	0.0522	0.985
0.094	14.9841	236	0.0442	0.99
0.1037	16.0	252	0.0492	0.99
0.1067	16.9524	267	0.0629	0.985
0.0912	17.9683	283	0.0486	0.985
0.0702	18.9841	299	0.0344	0.99
0.0677	20.0	315	0.0242	0.995
0.0566	20.9524	330	0.0295	0.99
0.0742	21.9683	346	0.0300	0.99
0.0675	22.9841	362	0.0159	1.0
0.0501	24.0	378	0.0105	0.995
0.0651	24.9524	393	0.0362	0.995
0.0665	25.9683	409	0.0335	0.985
0.0533	26.9841	425	0.0369	0.99
0.0487	28.0	441	0.0296	0.99
0.0384	28.9524	456	0.0177	0.995
0.038	29.9683	472	0.0176	0.995
0.0342	30.9841	488	0.0165	0.995
0.055	32.0	504	0.0199	0.995
0.0418	32.9524	519	0.0022	1.0
0.0447	33.9683	535	0.0071	0.995
0.0436	34.9841	551	0.0587	0.98
0.0307	36.0	567	0.0244	0.995
0.0413	36.9524	582	0.0227	0.99
0.0351	37.9683	598	0.0323	0.99
0.0267	38.9841	614	0.0510	0.985
0.0259	40.0	630	0.0009	1.0
0.0245	40.9524	645	0.0017	1.0
0.0227	41.9683	661	0.0208	0.995
0.0458	42.9841	677	0.0445	0.99
0.0263	44.0	693	0.0339	0.99
0.0458	44.9524	708	0.0124	0.995
0.0374	45.9683	724	0.0253	0.995
0.0413	46.9841	740	0.0025	1.0
0.0413	47.6190	750	0.0010	1.0

Framework versions

Transformers 4.45.2
Pytorch 2.5.0
Datasets 3.1.0
Tokenizers 0.20.1