vit-base-16-thesis-demo-HAM10000

ahishamm/vit-base-16-thesis-demo-HAM10000

vit-base-16-thesis-demo-HAM10000 is a image classification model from ahishamm. 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 5, 2023
Updated: Dec 5, 2023
SHA: f7d9761c

Base models

google/vit-base-patch16-224-in21k

Spaces

m-hamza-hassaan/Skin_Cancer_Detection_Big_Data

Model card

From the Hugging Face model README

vit-base-16-thesis-demo-HAM10000

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the ahishamm/HAM_db_enhanced_balanced_reduced_50_20_20_50 dataset. It achieves the following results on the evaluation set:

Loss: 0.5296
Accuracy: 0.8344
Recall: 0.8344
F1: 0.8344
Precision: 0.8344

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

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Dec 5, 2023
Updated: Dec 5, 2023