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_sgd_001_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: 0.885

smids_3x_deit_small_sgd_001_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: 0.2981
Accuracy: 0.885

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
0.8586	1.0	225	0.8598	0.645
0.6573	2.0	450	0.6819	0.745
0.5284	3.0	675	0.5681	0.79
0.4678	4.0	900	0.5034	0.82
0.4555	5.0	1125	0.4634	0.835
0.3848	6.0	1350	0.4333	0.8467
0.3445	7.0	1575	0.4134	0.85
0.3821	8.0	1800	0.3974	0.8533
0.3848	9.0	2025	0.3892	0.86
0.352	10.0	2250	0.3850	0.8483
0.3444	11.0	2475	0.3683	0.86
0.3393	12.0	2700	0.3678	0.8583
0.3465	13.0	2925	0.3528	0.865
0.3329	14.0	3150	0.3471	0.865
0.2839	15.0	3375	0.3437	0.865
0.3134	16.0	3600	0.3375	0.865
0.3254	17.0	3825	0.3357	0.865
0.2941	18.0	4050	0.3300	0.8717
0.2779	19.0	4275	0.3259	0.8767
0.2907	20.0	4500	0.3243	0.88
0.2541	21.0	4725	0.3245	0.875
0.2729	22.0	4950	0.3234	0.8717
0.2394	23.0	5175	0.3164	0.8867
0.24	24.0	5400	0.3164	0.8767
0.2295	25.0	5625	0.3132	0.8783
0.2317	26.0	5850	0.3134	0.8767
0.2077	27.0	6075	0.3127	0.875
0.2292	28.0	6300	0.3093	0.885
0.2441	29.0	6525	0.3091	0.885
0.2145	30.0	6750	0.3086	0.8767
0.2324	31.0	6975	0.3048	0.8833
0.2227	32.0	7200	0.3048	0.8817
0.245	33.0	7425	0.3041	0.8833
0.214	34.0	7650	0.3030	0.8833
0.1813	35.0	7875	0.3036	0.8817
0.2664	36.0	8100	0.3024	0.8833
0.1941	37.0	8325	0.3004	0.8883
0.2199	38.0	8550	0.3022	0.8833
0.2069	39.0	8775	0.2984	0.885
0.1869	40.0	9000	0.2998	0.8833
0.2101	41.0	9225	0.2995	0.8833
0.2258	42.0	9450	0.2989	0.8883
0.2128	43.0	9675	0.2987	0.8867
0.2283	44.0	9900	0.2982	0.8867
0.2441	45.0	10125	0.2992	0.8833
0.1971	46.0	10350	0.2984	0.885
0.221	47.0	10575	0.2978	0.8867
0.2053	48.0	10800	0.2980	0.885
0.2465	49.0	11025	0.2980	0.885
0.2188	50.0	11250	0.2981	0.885

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2