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_00001_fold2
    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: 0.6222222222222222

hushem_1x_deit_tiny_rms_00001_fold2

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: 1.4676
• Accuracy: 0.6222

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	1.3067	0.4222
1.3733	2.0	12	1.3951	0.4444
1.3733	3.0	18	1.3740	0.4222
0.6558	4.0	24	1.2467	0.5333
0.3343	5.0	30	1.5107	0.4667
0.3343	6.0	36	1.6079	0.4444
0.1446	7.0	42	1.2227	0.5333
0.1446	8.0	48	1.2018	0.5333
0.0575	9.0	54	1.2408	0.5111
0.0237	10.0	60	1.2581	0.5111
0.0237	11.0	66	1.4007	0.6
0.0072	12.0	72	1.2676	0.6444
0.0072	13.0	78	1.2933	0.5778
0.0036	14.0	84	1.3326	0.6222
0.0025	15.0	90	1.3074	0.6444
0.0025	16.0	96	1.3484	0.6222
0.002	17.0	102	1.3984	0.6222
0.002	18.0	108	1.3916	0.6222
0.0017	19.0	114	1.3871	0.6222
0.0014	20.0	120	1.4171	0.6222
0.0014	21.0	126	1.4207	0.6222
0.0012	22.0	132	1.4218	0.6222
0.0012	23.0	138	1.4371	0.6222
0.0011	24.0	144	1.4404	0.6222
0.001	25.0	150	1.4321	0.6222
0.001	26.0	156	1.4218	0.6222
0.0009	27.0	162	1.4367	0.6222
0.0009	28.0	168	1.4359	0.6222
0.0008	29.0	174	1.4387	0.6222
0.0008	30.0	180	1.4566	0.6222
0.0008	31.0	186	1.4528	0.6222
0.0007	32.0	192	1.4517	0.6222
0.0007	33.0	198	1.4535	0.6222
0.0007	34.0	204	1.4488	0.6444
0.0007	35.0	210	1.4494	0.6444
0.0007	36.0	216	1.4561	0.6444
0.0007	37.0	222	1.4595	0.6444
0.0007	38.0	228	1.4667	0.6222
0.0006	39.0	234	1.4671	0.6222
0.0007	40.0	240	1.4686	0.6222
0.0007	41.0	246	1.4681	0.6222
0.0006	42.0	252	1.4676	0.6222
0.0006	43.0	258	1.4676	0.6222
0.0006	44.0	264	1.4676	0.6222
0.0006	45.0	270	1.4676	0.6222
0.0006	46.0	276	1.4676	0.6222
0.0006	47.0	282	1.4676	0.6222
0.0006	48.0	288	1.4676	0.6222
0.0006	49.0	294	1.4676	0.6222
0.0006	50.0	300	1.4676	0.6222

Framework versions

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