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: hushem_1x_deit_tiny_rms_lr00001_fold1
    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: 0.6888888888888889

hushem_1x_deit_tiny_rms_lr00001_fold1

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: 1.2449
• Accuracy: 0.6889

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
No log	1.0	6	1.2771	0.4
1.263	2.0	12	1.0784	0.5556
1.263	3.0	18	0.9616	0.5556
0.5461	4.0	24	1.0339	0.6889
0.2446	5.0	30	0.9939	0.6667
0.2446	6.0	36	1.2442	0.4889
0.0817	7.0	42	0.7980	0.6222
0.0817	8.0	48	0.8675	0.6444
0.0302	9.0	54	0.8969	0.6889
0.009	10.0	60	0.9399	0.6222
0.009	11.0	66	1.0591	0.7111
0.0037	12.0	72	1.0283	0.6667
0.0037	13.0	78	1.0855	0.6667
0.0025	14.0	84	1.1121	0.6667
0.0019	15.0	90	1.1082	0.6667
0.0019	16.0	96	1.1158	0.6667
0.0015	17.0	102	1.1382	0.6667
0.0015	18.0	108	1.1574	0.6667
0.0013	19.0	114	1.1739	0.6667
0.0011	20.0	120	1.1736	0.6667
0.0011	21.0	126	1.1594	0.6889
0.001	22.0	132	1.1738	0.6889
0.001	23.0	138	1.1962	0.6667
0.0009	24.0	144	1.1951	0.6889
0.0008	25.0	150	1.2004	0.6889
0.0008	26.0	156	1.1996	0.6889
0.0008	27.0	162	1.2076	0.6889
0.0008	28.0	168	1.2144	0.6889
0.0007	29.0	174	1.2117	0.6889
0.0007	30.0	180	1.2204	0.6889
0.0007	31.0	186	1.2217	0.6889
0.0006	32.0	192	1.2270	0.6889
0.0006	33.0	198	1.2321	0.6889
0.0006	34.0	204	1.2307	0.6889
0.0006	35.0	210	1.2313	0.6889
0.0006	36.0	216	1.2374	0.6889
0.0006	37.0	222	1.2446	0.6889
0.0006	38.0	228	1.2471	0.6889
0.0005	39.0	234	1.2452	0.6889
0.0006	40.0	240	1.2458	0.6889
0.0006	41.0	246	1.2454	0.6889
0.0005	42.0	252	1.2449	0.6889
0.0005	43.0	258	1.2449	0.6889
0.0005	44.0	264	1.2449	0.6889
0.0005	45.0	270	1.2449	0.6889
0.0005	46.0	276	1.2449	0.6889
0.0005	47.0	282	1.2449	0.6889
0.0005	48.0	288	1.2449	0.6889
0.0005	49.0	294	1.2449	0.6889
0.0005	50.0	300	1.2449	0.6889

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1