smids_10x_deit_small_rms_00001_fold1

hkivancoral/smids_10x_deit_small_rms_00001_fold1

smids_10x_deit_small_rms_00001_fold1 is a image classification model from hkivancoral. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Dec 20, 2023
Updated: Dec 20, 2023
SHA: 0fdce18c

Base models

facebook/deit-small-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_10x_deit_small_rms_00001_fold1

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: 0.8415
Accuracy: 0.9132

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
0.2066	1.0	751	0.2884	0.8948
0.1058	2.0	1502	0.3397	0.8898
0.0676	3.0	2253	0.3801	0.9098
0.0667	4.0	3004	0.5510	0.8998
0.0253	5.0	3755	0.5205	0.9048
0.0216	6.0	4506	0.6002	0.9082
0.0362	7.0	5257	0.8486	0.8815
0.0357	8.0	6008	0.6658	0.9082
0.035	9.0	6759	0.5803	0.9065
0.001	10.0	7510	0.6719	0.9082
0.0231	11.0	8261	0.7324	0.9098
0.0001	12.0	9012	0.6703	0.9082
0.0008	13.0	9763	0.8412	0.8965
0.0582	14.0	10514	0.7418	0.9115
0.0077	15.0	11265	0.8736	0.8998
0.0	16.0	12016	0.7725	0.9115
0.0019	17.0	12767	0.7981	0.9065
0.0064	18.0	13518	1.0317	0.8848
0.0	19.0	14269	0.8686	0.9048
0.0	20.0	15020	0.8083	0.9082
0.0	21.0	15771	0.6862	0.9115
0.0	22.0	16522	0.7317	0.9132
0.0	23.0	17273	0.6937	0.9182
0.0	24.0	18024	0.7497	0.9199
0.0	25.0	18775	0.8180	0.9098
0.0001	26.0	19526	0.8680	0.9098
0.0	27.0	20277	0.8268	0.9115
0.0	28.0	21028	0.8126	0.9082
0.0042	29.0	21779	0.8397	0.9048
0.024	30.0	22530	0.8418	0.9098
0.0	31.0	23281	0.8800	0.9065
0.0	32.0	24032	0.8577	0.9065
0.0	33.0	24783	0.7988	0.9048
0.0	34.0	25534	0.8415	0.9032
0.0	35.0	26285	0.8311	0.9132
0.0	36.0	27036	0.8203	0.9149
0.0	37.0	27787	0.8136	0.9165
0.0	38.0	28538	0.8129	0.9199
0.0	39.0	29289	0.8165	0.9199
0.0	40.0	30040	0.8151	0.9149
0.0	41.0	30791	0.8242	0.9115
0.0	42.0	31542	0.8225	0.9115
0.0	43.0	32293	0.8294	0.9132
0.0	44.0	33044	0.8299	0.9132
0.0	45.0	33795	0.8337	0.9132
0.0	46.0	34546	0.8327	0.9132
0.0	47.0	35297	0.8358	0.9132
0.0	48.0	36048	0.8374	0.9132
0.0	49.0	36799	0.8388	0.9115
0.0	50.0	37550	0.8415	0.9132

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Dec 20, 2023
Updated: Dec 20, 2023