vit-base-brain-tumor-detection

dhritic9/vit-base-brain-tumor-detection

vit-base-brain-tumor-detection is a image classification model from dhritic9. 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: Jul 15, 2024
Updated: Jul 15, 2024
SHA: 3e6be2f1

Base models

google/vit-base-patch16-224-in21k

Model card

From the Hugging Face model README

vit-base-brain-tumor-detection

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5832
Accuracy: 0.785

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: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9535	0.4	100	0.8966	0.618
0.862	0.8	200	1.1149	0.561
0.7373	1.2	300	0.8543	0.605
0.6476	1.6	400	0.7307	0.666
0.6712	2.0	500	0.6954	0.694
0.4892	2.4	600	0.6391	0.707
0.5801	2.8	700	0.6247	0.708
0.3505	3.2	800	0.6056	0.778
0.3503	3.6	900	0.6264	0.743
0.3416	4.0	1000	0.5832	0.785
0.1427	4.4	1100	0.7297	0.769
0.1982	4.8	1200	0.7761	0.73
0.193	5.2	1300	0.8467	0.741
0.1831	5.6	1400	0.6975	0.774
0.2612	6.0	1500	0.8719	0.775
0.102	6.4	1600	0.9045	0.788
0.1029	6.8	1700	0.9655	0.783
0.0735	7.2	1800	0.9906	0.78
0.0715	7.6	1900	0.8893	0.787
0.1254	8.0	2000	1.1221	0.761
0.021	8.4	2100	1.1648	0.779
0.0133	8.8	2200	0.9857	0.806
0.0086	9.2	2300	1.0365	0.799
0.0223	9.6	2400	0.9826	0.812
0.0023	10.0	2500	1.0697	0.795
0.0021	10.4	2600	1.0490	0.815
0.0401	10.8	2700	1.1594	0.8
0.0012	11.2	2800	1.0811	0.817
0.0034	11.6	2900	1.0956	0.825
0.0012	12.0	3000	1.2010	0.808
0.0011	12.4	3100	1.1712	0.81
0.0092	12.8	3200	1.1814	0.813
0.0007	13.2	3300	1.1677	0.818
0.0007	13.6	3400	1.1723	0.818
0.0006	14.0	3500	1.1852	0.821
0.0005	14.4	3600	1.1928	0.82
0.0005	14.8	3700	1.2030	0.819
0.0005	15.2	3800	1.2093	0.818
0.0005	15.6	3900	1.2160	0.818
0.0004	16.0	4000	1.2232	0.819
0.0004	16.4	4100	1.2302	0.819
0.0004	16.8	4200	1.2350	0.819
0.0004	17.2	4300	1.2400	0.82
0.0004	17.6	4400	1.2442	0.821
0.0004	18.0	4500	1.2483	0.821
0.0004	18.4	4600	1.2518	0.821
0.0004	18.8	4700	1.2546	0.821
0.0004	19.2	4800	1.2561	0.821
0.0004	19.6	4900	1.2574	0.82
0.0004	20.0	5000	1.2577	0.82

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Jul 15, 2024
Updated: Jul 15, 2024