README.md

metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_tiny_adamax_00001_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: 0.9

smids_3x_deit_tiny_adamax_00001_fold3

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: 0.7809
• Accuracy: 0.9

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
0.4698	1.0	225	0.4392	0.8383
0.2874	2.0	450	0.3310	0.8683
0.2611	3.0	675	0.2984	0.8767
0.1948	4.0	900	0.2696	0.905
0.2186	5.0	1125	0.2803	0.905
0.1975	6.0	1350	0.2954	0.8983
0.1371	7.0	1575	0.2844	0.9033
0.1358	8.0	1800	0.2972	0.8983
0.1291	9.0	2025	0.3099	0.8967
0.0542	10.0	2250	0.3489	0.895
0.0501	11.0	2475	0.3613	0.8883
0.0606	12.0	2700	0.3786	0.8933
0.022	13.0	2925	0.4191	0.895
0.0131	14.0	3150	0.4369	0.8917
0.0269	15.0	3375	0.4976	0.89
0.017	16.0	3600	0.5139	0.8883
0.0239	17.0	3825	0.5627	0.905
0.0035	18.0	4050	0.5902	0.8933
0.0039	19.0	4275	0.6058	0.8967
0.0035	20.0	4500	0.6423	0.89
0.0003	21.0	4725	0.6358	0.8983
0.0002	22.0	4950	0.6169	0.9067
0.0002	23.0	5175	0.6520	0.8983
0.0001	24.0	5400	0.6716	0.8933
0.0214	25.0	5625	0.6822	0.8917
0.0001	26.0	5850	0.6829	0.895
0.0001	27.0	6075	0.7009	0.9017
0.0001	28.0	6300	0.7082	0.9033
0.0207	29.0	6525	0.7271	0.8967
0.0205	30.0	6750	0.7272	0.9033
0.0138	31.0	6975	0.7738	0.8867
0.0001	32.0	7200	0.7368	0.9033
0.0001	33.0	7425	0.7522	0.8967
0.0	34.0	7650	0.7497	0.8983
0.0	35.0	7875	0.7518	0.9017
0.0	36.0	8100	0.7530	0.9017
0.0	37.0	8325	0.7691	0.895
0.0	38.0	8550	0.7615	0.8983
0.0	39.0	8775	0.7639	0.9
0.0	40.0	9000	0.7671	0.9
0.0	41.0	9225	0.7823	0.8967
0.0	42.0	9450	0.7718	0.9
0.0	43.0	9675	0.7755	0.9
0.0	44.0	9900	0.7762	0.9017
0.0	45.0	10125	0.7825	0.8967
0.0	46.0	10350	0.7811	0.9
0.0044	47.0	10575	0.7795	0.9
0.0	48.0	10800	0.7809	0.9
0.0	49.0	11025	0.7812	0.9
0.0	50.0	11250	0.7809	0.9

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2