metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_1x_deit_small_sgd_0001_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: 0.685

smids_1x_deit_small_sgd_0001_fold4

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

Loss: 0.7697
Accuracy: 0.685

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: 0.0001
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
1.0786	1.0	75	1.0493	0.4483
1.0628	2.0	150	1.0330	0.4767
1.0285	3.0	225	1.0186	0.5233
0.9944	4.0	300	1.0062	0.53
1.0232	5.0	375	0.9947	0.5517
0.9927	6.0	450	0.9839	0.56
0.9942	7.0	525	0.9736	0.5767
0.9843	8.0	600	0.9634	0.595
0.9686	9.0	675	0.9535	0.6033
0.9669	10.0	750	0.9439	0.6067
0.9496	11.0	825	0.9345	0.6117
0.9424	12.0	900	0.9255	0.615
0.9379	13.0	975	0.9166	0.615
0.9246	14.0	1050	0.9079	0.6217
0.9261	15.0	1125	0.8998	0.63
0.8974	16.0	1200	0.8916	0.6333
0.9045	17.0	1275	0.8836	0.6367
0.8617	18.0	1350	0.8760	0.6417
0.885	19.0	1425	0.8688	0.6433
0.8736	20.0	1500	0.8617	0.6467
0.8843	21.0	1575	0.8551	0.6433
0.8472	22.0	1650	0.8488	0.6417
0.8796	23.0	1725	0.8428	0.6417
0.8784	24.0	1800	0.8370	0.6467
0.8408	25.0	1875	0.8316	0.65
0.8377	26.0	1950	0.8263	0.655
0.8101	27.0	2025	0.8213	0.6583
0.8334	28.0	2100	0.8166	0.66
0.8187	29.0	2175	0.8122	0.6567
0.8337	30.0	2250	0.8080	0.6583
0.8018	31.0	2325	0.8041	0.665
0.8384	32.0	2400	0.8003	0.67
0.813	33.0	2475	0.7968	0.6767
0.7997	34.0	2550	0.7936	0.6817
0.7882	35.0	2625	0.7905	0.6833
0.7651	36.0	2700	0.7878	0.6817
0.7706	37.0	2775	0.7852	0.6817
0.7916	38.0	2850	0.7828	0.6783
0.8116	39.0	2925	0.7807	0.6783
0.7662	40.0	3000	0.7787	0.6783
0.7857	41.0	3075	0.7769	0.6817
0.7862	42.0	3150	0.7753	0.6817
0.8172	43.0	3225	0.7740	0.685
0.7812	44.0	3300	0.7728	0.6867
0.803	45.0	3375	0.7718	0.685
0.7949	46.0	3450	0.7710	0.685
0.779	47.0	3525	0.7704	0.685
0.7941	48.0	3600	0.7700	0.685
0.7892	49.0	3675	0.7698	0.685
0.7766	50.0	3750	0.7697	0.685

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0