metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_5x_deit_tiny_rms_001_fold4
    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: 0.6904761904761905

hushem_5x_deit_tiny_rms_001_fold4

This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.2679
Accuracy: 0.6905

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: 0.001
train_batch_size: 32
eval_batch_size: 32
seed: 42
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
2.1506	1.0	28	2.1514	0.2381
1.4805	2.0	56	1.6187	0.2619
1.4792	3.0	84	1.5112	0.2619
1.5148	4.0	112	1.3546	0.3095
1.3804	5.0	140	1.3723	0.4286
1.4296	6.0	168	1.1490	0.4048
1.1847	7.0	196	1.3299	0.4524
1.1564	8.0	224	1.0799	0.4762
1.0992	9.0	252	1.1631	0.5
1.0863	10.0	280	1.1300	0.4524
1.0126	11.0	308	0.9131	0.5
1.0272	12.0	336	0.9239	0.5
0.9747	13.0	364	0.9521	0.6667
0.9219	14.0	392	0.8729	0.7619
0.8522	15.0	420	0.6286	0.7381
0.8968	16.0	448	0.8515	0.6429
0.8266	17.0	476	0.8301	0.6429
0.8581	18.0	504	1.0046	0.5476
0.8265	19.0	532	0.8082	0.6429
0.8594	20.0	560	0.8196	0.6190
0.7439	21.0	588	0.7591	0.6190
0.7899	22.0	616	0.8303	0.5952
0.8223	23.0	644	0.6299	0.7143
0.8203	24.0	672	0.7361	0.7143
0.7414	25.0	700	0.7251	0.7143
0.6879	26.0	728	0.8771	0.6905
0.8008	27.0	756	0.8469	0.5714
0.7402	28.0	784	0.6058	0.7857
0.7223	29.0	812	0.8210	0.6905
0.7302	30.0	840	0.8614	0.7143
0.7098	31.0	868	0.9312	0.7143
0.7044	32.0	896	0.8159	0.7143
0.7096	33.0	924	0.9197	0.6905
0.6854	34.0	952	0.8631	0.6190
0.7442	35.0	980	0.8324	0.6667
0.6271	36.0	1008	0.8632	0.7381
0.6052	37.0	1036	0.8753	0.7143
0.6189	38.0	1064	1.0917	0.7381
0.5817	39.0	1092	0.9635	0.6429
0.5324	40.0	1120	1.0245	0.6667
0.5312	41.0	1148	1.1733	0.6905
0.5538	42.0	1176	1.0809	0.7143
0.4355	43.0	1204	1.0395	0.6667
0.3909	44.0	1232	1.1631	0.6667
0.301	45.0	1260	1.2110	0.6667
0.3678	46.0	1288	1.2357	0.6905
0.3355	47.0	1316	1.2487	0.7143
0.2983	48.0	1344	1.2713	0.6905
0.2527	49.0	1372	1.2679	0.6905
0.2761	50.0	1400	1.2679	0.6905

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0