smids_3x_deit_base_sgd_001_fold2

hkivancoral/smids_3x_deit_base_sgd_001_fold2

smids_3x_deit_base_sgd_001_fold2 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 11, 2023
Updated: Dec 11, 2023
SHA: bb9ba9bd

Base models

facebook/deit-base-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_3x_deit_base_sgd_001_fold2

This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3164
Accuracy: 0.8702

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
0.9356	1.0	225	0.9551	0.6140
0.7266	2.0	450	0.7486	0.7288
0.5616	3.0	675	0.6080	0.7704
0.547	4.0	900	0.5262	0.7937
0.4582	5.0	1125	0.4769	0.7987
0.3947	6.0	1350	0.4429	0.8103
0.3972	7.0	1575	0.4203	0.8270
0.3767	8.0	1800	0.4042	0.8286
0.3401	9.0	2025	0.3910	0.8369
0.3023	10.0	2250	0.3806	0.8419
0.3373	11.0	2475	0.3718	0.8436
0.2966	12.0	2700	0.3663	0.8502
0.2871	13.0	2925	0.3599	0.8502
0.2823	14.0	3150	0.3565	0.8502
0.3247	15.0	3375	0.3500	0.8486
0.3466	16.0	3600	0.3478	0.8486
0.2808	17.0	3825	0.3471	0.8486
0.2148	18.0	4050	0.3407	0.8469
0.245	19.0	4275	0.3382	0.8502
0.2737	20.0	4500	0.3376	0.8486
0.2877	21.0	4725	0.3336	0.8486
0.3056	22.0	4950	0.3302	0.8502
0.3242	23.0	5175	0.3293	0.8519
0.2649	24.0	5400	0.3291	0.8536
0.2721	25.0	5625	0.3296	0.8519
0.2345	26.0	5850	0.3266	0.8519
0.2272	27.0	6075	0.3224	0.8586
0.2367	28.0	6300	0.3218	0.8569
0.2688	29.0	6525	0.3231	0.8552
0.2737	30.0	6750	0.3223	0.8569
0.2277	31.0	6975	0.3231	0.8602
0.2491	32.0	7200	0.3225	0.8602
0.2511	33.0	7425	0.3193	0.8602
0.2122	34.0	7650	0.3202	0.8569
0.2292	35.0	7875	0.3193	0.8602
0.243	36.0	8100	0.3185	0.8619
0.2358	37.0	8325	0.3187	0.8652
0.2127	38.0	8550	0.3178	0.8669
0.2259	39.0	8775	0.3182	0.8686
0.2023	40.0	9000	0.3176	0.8686
0.194	41.0	9225	0.3177	0.8686
0.2145	42.0	9450	0.3163	0.8669
0.188	43.0	9675	0.3174	0.8686
0.2222	44.0	9900	0.3170	0.8686
0.2664	45.0	10125	0.3165	0.8652
0.2195	46.0	10350	0.3166	0.8686
0.2046	47.0	10575	0.3165	0.8686
0.1994	48.0	10800	0.3164	0.8669
0.2327	49.0	11025	0.3164	0.8702
0.1935	50.0	11250	0.3164	0.8702

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 11, 2023
Updated: Dec 11, 2023