metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - recall
  - f1
  - precision
model-index:
  - name: vit-base-16-thesis-demo-HAM10000
    results: []

vit-base-16-thesis-demo-HAM10000

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: 0.5608
Accuracy: 0.8438
Recall: 0.8438
F1: 0.8438
Precision: 0.8438

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: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	F1	Precision
1.4855	0.12	50	1.3519	0.5093	0.5093	0.5093	0.5093
1.044	0.23	100	1.0515	0.6268	0.6268	0.6268	0.6268
1.0774	0.35	150	1.2104	0.5681	0.5681	0.5681	0.5681
0.9508	0.46	200	1.0624	0.6061	0.6061	0.6061	0.6061
0.9522	0.58	250	0.9338	0.6449	0.6449	0.6449	0.6449
0.774	0.69	300	0.8939	0.6676	0.6676	0.6676	0.6676
0.7675	0.81	350	0.7742	0.7183	0.7183	0.7183	0.7183
0.7167	0.92	400	0.7695	0.7216	0.7216	0.7216	0.7216
0.5204	1.04	450	0.8005	0.7303	0.7303	0.7303	0.7303
0.456	1.15	500	0.8523	0.6903	0.6903	0.6903	0.6903
0.5421	1.27	550	0.6753	0.7543	0.7543	0.7543	0.7543
0.4446	1.38	600	0.6042	0.7810	0.7810	0.7810	0.7810
0.455	1.5	650	0.6913	0.7410	0.7410	0.7410	0.7410
0.4175	1.61	700	0.6142	0.7810	0.7810	0.7810	0.7810
0.3626	1.73	750	0.5831	0.8004	0.8004	0.8004	0.8004
0.4816	1.84	800	0.5586	0.7891	0.7891	0.7891	0.7891
0.3257	1.96	850	0.5759	0.7991	0.7991	0.7991	0.7991
0.3111	2.07	900	0.6100	0.7931	0.7931	0.7931	0.7931
0.2052	2.19	950	0.5674	0.8111	0.8111	0.8111	0.8111
0.2273	2.3	1000	0.5975	0.8017	0.8017	0.8017	0.8017
0.3007	2.42	1050	0.5714	0.8204	0.8204	0.8204	0.8204
0.2812	2.53	1100	0.6081	0.8004	0.8004	0.8004	0.8004
0.2661	2.65	1150	0.5653	0.8224	0.8224	0.8224	0.8224
0.1796	2.76	1200	0.5447	0.8338	0.8338	0.8338	0.8338
0.1882	2.88	1250	0.5357	0.8284	0.8284	0.8284	0.8284
0.1596	3.0	1300	0.5296	0.8344	0.8344	0.8344	0.8344
0.075	3.11	1350	0.5876	0.8198	0.8198	0.8198	0.8198
0.1128	3.23	1400	0.5612	0.8338	0.8338	0.8338	0.8338
0.0677	3.34	1450	0.5911	0.8331	0.8331	0.8331	0.8331
0.0794	3.46	1500	0.5971	0.8304	0.8304	0.8304	0.8304
0.0367	3.57	1550	0.5634	0.8378	0.8378	0.8378	0.8378
0.0279	3.69	1600	0.5674	0.8391	0.8391	0.8391	0.8391
0.0216	3.8	1650	0.5777	0.8358	0.8358	0.8358	0.8358
0.0161	3.92	1700	0.5608	0.8438	0.8438	0.8438	0.8438

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0