prove_melanomaprova_melanoma

sergiocannata/prove_melanomaprova_melanoma

prove_melanomaprova_melanoma is a image classification model from sergiocannata. 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: Apr 26, 2023
Updated: May 5, 2023
SHA: e82d8b7f

Datasets

imagefolder

Model card

From the Hugging Face model README

prove_melanomaprova_melanoma

This model is a fine-tuned version of UnipaPolitoUnimore/vit-large-patch32-384-melanoma on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.5191
Accuracy: 0.8467

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: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8964	0.99	31	1.0906	0.52
0.6588	1.98	62	1.0817	0.52
0.6774	2.98	93	0.9474	0.52
0.7785	4.0	125	0.8185	0.6267
0.6732	4.99	156	0.7531	0.7267
0.5438	5.98	187	0.6972	0.7333
0.5497	6.98	218	0.6714	0.7533
0.4161	8.0	250	0.6440	0.7667
0.4968	8.99	281	0.6438	0.78
0.5861	9.98	312	0.6266	0.7933
0.5182	10.98	343	0.6158	0.7867
0.6797	12.0	375	0.6237	0.8133
0.622	12.99	406	0.5858	0.8333
0.6419	13.98	437	0.5735	0.8267
0.3727	14.98	468	0.5641	0.8133
0.3822	16.0	500	0.5520	0.8267
0.4766	16.99	531	0.5642	0.8267
0.4791	17.98	562	0.5309	0.8267
0.3918	18.98	593	0.5749	0.8267
0.3847	20.0	625	0.5317	0.84
0.3722	20.99	656	0.5719	0.8267
0.5402	21.98	687	0.5316	0.84
0.4358	22.98	718	0.5292	0.8333
0.2957	24.0	750	0.5172	0.8467
0.4801	24.99	781	0.5376	0.84
0.3656	25.98	812	0.5118	0.8333
0.3956	26.98	843	0.5081	0.8533
0.3343	28.0	875	0.5198	0.8533
0.3839	28.99	906	0.5269	0.8467
0.4286	29.98	937	0.5163	0.8467
0.2736	30.98	968	0.5359	0.8333
0.3465	32.0	1000	0.5277	0.84
0.4244	32.99	1031	0.5385	0.8333
0.308	33.98	1062	0.5141	0.8533
0.3494	34.98	1093	0.5129	0.8533
0.3851	36.0	1125	0.5199	0.84
0.3949	36.99	1156	0.5250	0.84
0.3235	37.98	1187	0.5142	0.8533
0.3076	38.98	1218	0.5166	0.8533
0.3679	39.68	1240	0.5191	0.8467

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Apr 26, 2023
Updated: May 5, 2023