metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_small_rms_00001_fold5
    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: 0.8933333333333333

smids_3x_deit_small_rms_00001_fold5

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: 0.9898
Accuracy: 0.8933

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: 1e-05
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
0.2981	1.0	225	0.3128	0.8667
0.1441	2.0	450	0.2643	0.895
0.112	3.0	675	0.3212	0.8817
0.0406	4.0	900	0.3851	0.8983
0.0366	5.0	1125	0.4872	0.8767
0.0159	6.0	1350	0.5831	0.8817
0.0669	7.0	1575	0.5966	0.8833
0.0217	8.0	1800	0.7066	0.89
0.0482	9.0	2025	0.7260	0.8917
0.003	10.0	2250	0.6702	0.9017
0.0029	11.0	2475	0.6212	0.9133
0.0459	12.0	2700	0.7442	0.8967
0.0005	13.0	2925	0.7171	0.9
0.0	14.0	3150	0.7165	0.905
0.0001	15.0	3375	0.7191	0.9017
0.0236	16.0	3600	0.6965	0.8967
0.0	17.0	3825	0.7247	0.9
0.0002	18.0	4050	0.8019	0.89
0.0206	19.0	4275	0.7794	0.9
0.0023	20.0	4500	0.7187	0.9017
0.0	21.0	4725	0.8080	0.9017
0.0033	22.0	4950	0.9120	0.885
0.0	23.0	5175	0.9403	0.88
0.0076	24.0	5400	0.8853	0.8917
0.0	25.0	5625	0.8438	0.8983
0.0	26.0	5850	0.8326	0.8983
0.0	27.0	6075	0.9235	0.8867
0.0023	28.0	6300	0.8353	0.9
0.0039	29.0	6525	0.9907	0.8883
0.0	30.0	6750	0.9749	0.885
0.0	31.0	6975	0.9599	0.8917
0.0	32.0	7200	0.9273	0.8883
0.0038	33.0	7425	0.9025	0.9
0.0	34.0	7650	0.9166	0.9
0.0036	35.0	7875	0.9319	0.9017
0.0	36.0	8100	0.9400	0.89
0.0042	37.0	8325	0.9533	0.895
0.0	38.0	8550	0.9627	0.8883
0.0	39.0	8775	0.9661	0.8967
0.0	40.0	9000	0.9682	0.89
0.0	41.0	9225	0.9782	0.89
0.0	42.0	9450	0.9830	0.89
0.0028	43.0	9675	0.9854	0.8917
0.0	44.0	9900	0.9812	0.8917
0.0	45.0	10125	0.9848	0.8917
0.0	46.0	10350	0.9870	0.895
0.0	47.0	10575	0.9891	0.8917
0.0	48.0	10800	0.9895	0.8933
0.0	49.0	11025	0.9898	0.8933
0.0	50.0	11250	0.9898	0.8933

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2