metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_small_sgd_0001_fold3
    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: 0.835

smids_5x_deit_small_sgd_0001_fold3

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: 0.4989
Accuracy: 0.835

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
1.059	1.0	375	1.0700	0.43
1.011	2.0	750	1.0336	0.4883
0.9442	3.0	1125	0.9972	0.5233
0.9044	4.0	1500	0.9613	0.5667
0.9034	5.0	1875	0.9273	0.6067
0.8397	6.0	2250	0.8960	0.6217
0.8344	7.0	2625	0.8666	0.6533
0.8102	8.0	3000	0.8388	0.66
0.7533	9.0	3375	0.8130	0.675
0.7704	10.0	3750	0.7889	0.6783
0.6922	11.0	4125	0.7657	0.695
0.7058	12.0	4500	0.7444	0.7067
0.7015	13.0	4875	0.7244	0.7183
0.7084	14.0	5250	0.7056	0.725
0.6276	15.0	5625	0.6882	0.74
0.6138	16.0	6000	0.6721	0.745
0.6401	17.0	6375	0.6573	0.7533
0.6373	18.0	6750	0.6430	0.7633
0.569	19.0	7125	0.6303	0.7633
0.5819	20.0	7500	0.6185	0.77
0.5294	21.0	7875	0.6077	0.7817
0.5473	22.0	8250	0.5978	0.7883
0.5629	23.0	8625	0.5888	0.7967
0.5783	24.0	9000	0.5802	0.8017
0.509	25.0	9375	0.5724	0.8067
0.5255	26.0	9750	0.5652	0.805
0.5612	27.0	10125	0.5585	0.81
0.5914	28.0	10500	0.5523	0.815
0.4839	29.0	10875	0.5467	0.815
0.4781	30.0	11250	0.5414	0.8167
0.5423	31.0	11625	0.5367	0.8183
0.5434	32.0	12000	0.5323	0.8183
0.5812	33.0	12375	0.5281	0.82
0.4776	34.0	12750	0.5244	0.8183
0.4385	35.0	13125	0.5209	0.8217
0.4956	36.0	13500	0.5178	0.8217
0.4746	37.0	13875	0.5150	0.8233
0.4824	38.0	14250	0.5124	0.8233
0.49	39.0	14625	0.5101	0.8217
0.4379	40.0	15000	0.5080	0.8233
0.4149	41.0	15375	0.5062	0.825
0.4917	42.0	15750	0.5046	0.8267
0.5208	43.0	16125	0.5031	0.8283
0.4676	44.0	16500	0.5020	0.8283
0.4552	45.0	16875	0.5009	0.8317
0.4563	46.0	17250	0.5002	0.8333
0.5467	47.0	17625	0.4996	0.835
0.5056	48.0	18000	0.4992	0.835
0.4817	49.0	18375	0.4990	0.835
0.4808	50.0	18750	0.4989	0.835

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2