vit-weight-decay-1e-4

sharren/vit-weight-decay-1e-4

vit-weight-decay-1e-4 is a image classification model from sharren. 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: Mar 21, 2024
Updated: Mar 21, 2024
SHA: 370e27ff

Base models

google/vit-base-patch16-224

Model card

From the Hugging Face model README

vit-weight-decay-1e-4

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

Loss: 0.5277
Accuracy: 0.8263
Precision: 0.8467
Recall: 0.8263
F1: 0.8324

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: 0.0001
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: cosine
lr_scheduler_warmup_steps: 1733
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.7856	1.0	321	0.9535	0.6869	0.6412	0.6869	0.6229
1.1412	2.0	642	0.6928	0.7424	0.7440	0.7424	0.7311
1.0297	3.0	963	0.6863	0.7490	0.7362	0.7490	0.7057
0.9571	4.0	1284	0.5587	0.7694	0.7901	0.7694	0.7736
0.9346	5.0	1605	0.5654	0.7940	0.8058	0.7940	0.7919
0.9802	6.0	1926	0.6318	0.7746	0.7928	0.7746	0.7794
0.8352	7.0	2247	0.6611	0.7295	0.8145	0.7295	0.7498
0.7621	8.0	2568	0.5766	0.7666	0.8162	0.7666	0.7781
0.7352	9.0	2889	0.5369	0.7996	0.8269	0.7996	0.8079
0.6919	10.0	3210	0.5500	0.7753	0.8270	0.7753	0.7900
0.6105	11.0	3531	0.5562	0.8062	0.8310	0.8062	0.8129
0.5808	12.0	3852	0.6608	0.7708	0.8278	0.7708	0.7871
0.4534	13.0	4173	0.5684	0.8301	0.8483	0.8301	0.8291
0.4519	14.0	4494	0.5928	0.8121	0.8388	0.8121	0.8201
0.3998	15.0	4815	0.5277	0.8263	0.8467	0.8263	0.8324
0.3307	16.0	5136	0.5944	0.8266	0.8458	0.8266	0.8330
0.2899	17.0	5457	0.5387	0.8526	0.8546	0.8526	0.8524
0.2466	18.0	5778	0.5559	0.8495	0.8541	0.8495	0.8506
0.201	19.0	6099	0.6360	0.8336	0.8671	0.8336	0.8427
0.2163	20.0	6420	0.6009	0.8599	0.8575	0.8599	0.8581
0.1614	21.0	6741	0.5804	0.8689	0.8648	0.8689	0.8630
0.1106	22.0	7062	0.5798	0.8689	0.8661	0.8689	0.8670
0.1243	23.0	7383	0.6228	0.8703	0.8686	0.8703	0.8672
0.1251	24.0	7704	0.5987	0.8727	0.8695	0.8727	0.8698
0.1038	25.0	8025	0.5806	0.8769	0.8756	0.8769	0.8753

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Mar 21, 2024
Updated: Mar 21, 2024