metadata

license: apache-2.0
base_model: google/vit-large-patch32-224-in21k
tags:
  - 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 an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.7429
Accuracy: 0.8202
Precision: 0.8190
Recall: 0.8202
F1: 0.8173

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
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
1.1583	0.99	124	1.0551	0.7069	0.6559	0.7069	0.6758
0.8599	2.0	249	0.7914	0.7621	0.7717	0.7621	0.7557
0.854	3.0	374	0.7115	0.7672	0.7850	0.7672	0.7642
0.7282	4.0	499	0.6807	0.7683	0.7746	0.7683	0.7604
0.6165	4.99	623	0.6208	0.8016	0.8088	0.8016	0.8015
0.5946	6.0	748	0.5850	0.8044	0.8084	0.8044	0.8009
0.6243	7.0	873	0.6090	0.7931	0.8037	0.7931	0.7935
0.5429	8.0	998	0.5830	0.8021	0.8087	0.8021	0.8006
0.558	8.99	1122	0.5725	0.8095	0.8191	0.8095	0.8081
0.457	10.0	1247	0.5702	0.8123	0.8144	0.8123	0.8085
0.4399	11.0	1372	0.5973	0.8021	0.8013	0.8021	0.7995
0.4055	12.0	1497	0.5799	0.8157	0.8186	0.8157	0.8122
0.417	12.99	1621	0.6006	0.8061	0.8175	0.8061	0.8066
0.3843	14.0	1746	0.5849	0.8236	0.8257	0.8236	0.8212
0.371	15.0	1871	0.5711	0.8196	0.8157	0.8196	0.8161
0.3546	16.0	1996	0.6050	0.8140	0.8171	0.8140	0.8147
0.2935	16.99	2120	0.6425	0.8106	0.8159	0.8106	0.8091
0.2505	18.0	2245	0.6569	0.8112	0.8091	0.8112	0.8086
0.3094	19.0	2370	0.6558	0.8162	0.8137	0.8162	0.8137
0.2739	20.0	2495	0.7201	0.8067	0.8094	0.8067	0.8025
0.2224	20.99	2619	0.7227	0.8140	0.8175	0.8140	0.8114
0.2359	22.0	2744	0.6941	0.8157	0.8142	0.8157	0.8136
0.2535	23.0	2869	0.7086	0.8157	0.8160	0.8157	0.8123
0.2047	24.0	2994	0.7185	0.8236	0.8236	0.8236	0.8207
0.2162	24.99	3118	0.7135	0.8219	0.8200	0.8219	0.8194
0.2297	26.0	3243	0.7269	0.8213	0.8172	0.8213	0.8179
0.2048	27.0	3368	0.7392	0.8145	0.8156	0.8145	0.8143
0.2156	28.0	3493	0.7453	0.8207	0.8182	0.8207	0.8174
0.1785	28.99	3617	0.7497	0.8168	0.8157	0.8168	0.8145
0.1785	29.82	3720	0.7429	0.8202	0.8190	0.8202	0.8173

Framework versions

Transformers 4.37.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.15.1