metadata

license: apache-2.0
base_model: google/vit-large-patch32-224-in21k
tags:
  - image-classification
  - vision
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: vit-large-patch32-224-in21k-finetuned-galaxy10-decals
    results: []

vit-large-patch32-224-in21k-finetuned-galaxy10-decals

This model is a fine-tuned version of google/vit-large-patch32-224-in21k on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

Loss: 0.5281
Accuracy: 0.8382
Precision: 0.8372
Recall: 0.8382
F1: 0.8356

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: 128
eval_batch_size: 128
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.8923	0.99	31	1.6725	0.4600	0.5537	0.4600	0.3682
1.1787	1.98	62	0.9949	0.7339	0.7513	0.7339	0.7095
0.9165	2.98	93	0.7946	0.7700	0.7694	0.7700	0.7540
0.802	4.0	125	0.6747	0.7948	0.7954	0.7948	0.7843
0.7074	4.99	156	0.6196	0.8117	0.8139	0.8117	0.8115
0.6424	5.98	187	0.6205	0.8021	0.8075	0.8021	0.7961
0.6309	6.98	218	0.5760	0.8117	0.8231	0.8117	0.8127
0.5682	8.0	250	0.5748	0.8151	0.8196	0.8151	0.8157
0.5981	8.99	281	0.5704	0.8213	0.8269	0.8213	0.8158
0.547	9.98	312	0.5282	0.8377	0.8352	0.8377	0.8345
0.5067	10.98	343	0.5281	0.8382	0.8372	0.8382	0.8356
0.5066	12.0	375	0.5441	0.8247	0.8286	0.8247	0.8219
0.4919	12.99	406	0.5580	0.8157	0.8236	0.8157	0.8155
0.4508	13.98	437	0.5269	0.8303	0.8331	0.8303	0.8279
0.4415	14.98	468	0.5399	0.8185	0.8249	0.8185	0.8203
0.4178	16.0	500	0.5229	0.8320	0.8358	0.8320	0.8301
0.366	16.99	531	0.5427	0.8275	0.8281	0.8275	0.8241
0.3706	17.98	562	0.5389	0.8241	0.8242	0.8241	0.8230
0.3609	18.98	593	0.5573	0.8247	0.8262	0.8247	0.8239
0.3443	20.0	625	0.5605	0.8320	0.8325	0.8320	0.8302
0.3214	20.99	656	0.5667	0.8281	0.8295	0.8281	0.8254
0.3262	21.98	687	0.5797	0.8236	0.8237	0.8236	0.8214
0.299	22.98	718	0.5938	0.8202	0.8225	0.8202	0.8195
0.2792	24.0	750	0.5909	0.8275	0.8258	0.8275	0.8251
0.2969	24.99	781	0.5658	0.8309	0.8319	0.8309	0.8306
0.2559	25.98	812	0.5936	0.8309	0.8294	0.8309	0.8294
0.2756	26.98	843	0.5898	0.8292	0.8295	0.8292	0.8287
0.254	28.0	875	0.6043	0.8303	0.8319	0.8303	0.8289
0.2674	28.99	906	0.5950	0.8371	0.8365	0.8371	0.8353
0.2432	29.76	930	0.5907	0.8360	0.8348	0.8360	0.8345

Framework versions

Transformers 4.37.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.15.1