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_00001_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: 0.8583333333333333

smids_1x_deit_tiny_rms_00001_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: 1.2549
Accuracy: 0.8583

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
0.4697	1.0	75	0.4183	0.8233
0.3312	2.0	150	0.3681	0.8533
0.2321	3.0	225	0.4033	0.8517
0.1334	4.0	300	0.3968	0.8617
0.1233	5.0	375	0.4520	0.8567
0.0584	6.0	450	0.5293	0.8467
0.0835	7.0	525	0.5619	0.8533
0.0113	8.0	600	0.7080	0.8483
0.0326	9.0	675	0.7194	0.86
0.0108	10.0	750	0.7779	0.8583
0.0133	11.0	825	0.7881	0.8617
0.0052	12.0	900	0.8341	0.87
0.0272	13.0	975	0.8910	0.8617
0.0077	14.0	1050	0.9561	0.8433
0.0002	15.0	1125	0.9039	0.8617
0.0001	16.0	1200	0.9956	0.86
0.032	17.0	1275	0.9953	0.8667
0.018	18.0	1350	0.9816	0.8633
0.0282	19.0	1425	1.1776	0.8467
0.0002	20.0	1500	1.0796	0.8583
0.0001	21.0	1575	1.1308	0.8567
0.0001	22.0	1650	1.1869	0.8467
0.0001	23.0	1725	1.1953	0.86
0.0134	24.0	1800	1.1511	0.85
0.0197	25.0	1875	1.2279	0.8517
0.0	26.0	1950	1.2715	0.8483
0.0011	27.0	2025	1.2389	0.85
0.0034	28.0	2100	1.2470	0.85
0.0076	29.0	2175	1.1531	0.8617
0.0	30.0	2250	1.2325	0.85
0.0	31.0	2325	1.2009	0.8633
0.0	32.0	2400	1.2311	0.85
0.0	33.0	2475	1.2487	0.8583
0.0	34.0	2550	1.2363	0.8567
0.0	35.0	2625	1.2306	0.8567
0.0	36.0	2700	1.2366	0.86
0.0048	37.0	2775	1.2202	0.8567
0.0	38.0	2850	1.2263	0.86
0.0	39.0	2925	1.2319	0.8617
0.0	40.0	3000	1.2616	0.8533
0.0038	41.0	3075	1.2358	0.8583
0.0	42.0	3150	1.2473	0.8583
0.0	43.0	3225	1.2419	0.8567
0.0	44.0	3300	1.2543	0.8583
0.0	45.0	3375	1.2531	0.8567
0.0	46.0	3450	1.2531	0.8583
0.0	47.0	3525	1.2531	0.8583
0.0	48.0	3600	1.2543	0.8583
0.0	49.0	3675	1.2544	0.8583
0.0	50.0	3750	1.2549	0.8583

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0