smids_10x_deit_tiny_rms_0001_fold5

hkivancoral/smids_10x_deit_tiny_rms_0001_fold5

smids_10x_deit_tiny_rms_0001_fold5 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 23, 2023
Updated: Dec 23, 2023
SHA: 68b3160a

Base models

facebook/deit-tiny-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_10x_deit_tiny_rms_0001_fold5

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: 1.4754
Accuracy: 0.83

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
0.8611	1.0	750	0.8101	0.59
0.7337	2.0	1500	0.7328	0.5933
0.6092	3.0	2250	0.7436	0.6133
0.4788	4.0	3000	0.6067	0.7117
0.5206	5.0	3750	0.5059	0.79
0.4534	6.0	4500	0.5164	0.7733
0.4278	7.0	5250	0.5196	0.7883
0.421	8.0	6000	0.4508	0.82
0.3903	9.0	6750	0.4527	0.8067
0.3955	10.0	7500	0.4845	0.7867
0.4005	11.0	8250	0.4149	0.835
0.3221	12.0	9000	0.4996	0.815
0.3587	13.0	9750	0.4119	0.8433
0.2736	14.0	10500	0.4909	0.85
0.2907	15.0	11250	0.4508	0.8217
0.2288	16.0	12000	0.5192	0.825
0.2378	17.0	12750	0.4692	0.8383
0.2297	18.0	13500	0.5883	0.8217
0.2065	19.0	14250	0.4741	0.8483
0.1668	20.0	15000	0.5264	0.82
0.1867	21.0	15750	0.5865	0.8217
0.1397	22.0	16500	0.5812	0.8283
0.15	23.0	17250	0.7165	0.83
0.083	24.0	18000	0.6760	0.8267
0.3309	25.0	18750	0.8204	0.79
0.6907	26.0	19500	0.6426	0.7217
0.4092	27.0	20250	0.7365	0.72
0.4562	28.0	21000	0.4704	0.7817
0.4588	29.0	21750	0.4367	0.7983
0.4089	30.0	22500	0.5011	0.7883
0.4005	31.0	23250	0.5056	0.805
0.2986	32.0	24000	0.4328	0.83
0.3319	33.0	24750	0.5242	0.805
0.2453	34.0	25500	0.4883	0.8383
0.2459	35.0	26250	0.4886	0.8233
0.2873	36.0	27000	0.5046	0.8217
0.1814	37.0	27750	0.5539	0.8133
0.2073	38.0	28500	0.5379	0.8233
0.2284	39.0	29250	0.5765	0.815
0.1577	40.0	30000	0.6100	0.8333
0.1325	41.0	30750	0.6730	0.8333
0.1239	42.0	31500	0.7561	0.8333
0.0741	43.0	32250	0.8428	0.8217
0.0999	44.0	33000	0.9214	0.8167
0.0863	45.0	33750	0.9203	0.8367
0.0855	46.0	34500	1.0656	0.83
0.064	47.0	35250	1.2523	0.815
0.0407	48.0	36000	1.2659	0.83
0.0522	49.0	36750	1.4174	0.835
0.0025	50.0	37500	1.4754	0.83

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 23, 2023
Updated: Dec 23, 2023