metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_1x_deit_tiny_rms_001_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: 0.7333333333333333

smids_1x_deit_tiny_rms_001_fold4

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: 0.9960
Accuracy: 0.7333

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
1.1598	1.0	75	4.0739	0.3033
1.1025	2.0	150	1.1472	0.335
1.0056	3.0	225	0.8843	0.5617
0.9034	4.0	300	0.8985	0.5117
0.8764	5.0	375	0.9613	0.5017
0.9617	6.0	450	0.9074	0.5317
0.8578	7.0	525	0.8240	0.5717
0.8424	8.0	600	0.8437	0.5617
0.8025	9.0	675	0.7942	0.5833
0.7777	10.0	750	0.7683	0.57
0.8053	11.0	825	0.7474	0.5983
0.818	12.0	900	0.7555	0.61
0.8018	13.0	975	0.7629	0.5833
0.8411	14.0	1050	0.7216	0.635
0.6416	15.0	1125	0.8742	0.56
0.8084	16.0	1200	0.7814	0.6083
0.7505	17.0	1275	0.7600	0.6183
0.6996	18.0	1350	0.7346	0.6283
0.7648	19.0	1425	0.7240	0.6617
0.6916	20.0	1500	0.6768	0.6767
0.7556	21.0	1575	0.7263	0.6617
0.6471	22.0	1650	0.7297	0.6583
0.752	23.0	1725	0.7501	0.635
0.7349	24.0	1800	0.6751	0.6883
0.6802	25.0	1875	0.6689	0.6817
0.6239	26.0	1950	0.8871	0.5817
0.6865	27.0	2025	0.6485	0.7033
0.6138	28.0	2100	0.6457	0.7233
0.6707	29.0	2175	0.6937	0.6833
0.6824	30.0	2250	0.6688	0.7033
0.5913	31.0	2325	0.6725	0.715
0.5797	32.0	2400	0.6508	0.7167
0.5524	33.0	2475	0.7048	0.7
0.4736	34.0	2550	0.6807	0.6933
0.5263	35.0	2625	0.6317	0.7233
0.5348	36.0	2700	0.6398	0.7367
0.5082	37.0	2775	0.6440	0.7183
0.4972	38.0	2850	0.6697	0.7167
0.4567	39.0	2925	0.6947	0.73
0.4313	40.0	3000	0.6527	0.7383
0.4762	41.0	3075	0.6875	0.74
0.4293	42.0	3150	0.7259	0.7333
0.4594	43.0	3225	0.7531	0.7367
0.379	44.0	3300	0.7792	0.7383
0.3265	45.0	3375	0.7882	0.74
0.2807	46.0	3450	0.8615	0.7367
0.2733	47.0	3525	0.9438	0.73
0.2273	48.0	3600	0.9312	0.73
0.2189	49.0	3675	0.9889	0.7383
0.1609	50.0	3750	0.9960	0.7333

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0