metadata

license: apache-2.0
base_model: microsoft/beit-large-patch16-224-pt22k
tags:
  - image-classification
  - vision
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: beit-large-patch16-224-pt22k-finetuned-galaxy10-decals
    results: []

beit-large-patch16-224-pt22k-finetuned-galaxy10-decals

This model is a fine-tuned version of microsoft/beit-large-patch16-224-pt22k on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

Loss: 0.5038
Accuracy: 0.8794
Precision: 0.8781
Recall: 0.8794
F1: 0.8780

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: 5e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 256
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
1.5123	0.99	62	1.2940	0.5276	0.5208	0.5276	0.5021
0.9691	2.0	125	0.7947	0.7272	0.7161	0.7272	0.7095
0.7326	2.99	187	0.5790	0.8010	0.7979	0.8010	0.7970
0.6346	4.0	250	0.6230	0.7931	0.7984	0.7931	0.7883
0.5945	4.99	312	0.5042	0.8360	0.8390	0.8360	0.8349
0.5607	6.0	375	0.4401	0.8455	0.8464	0.8455	0.8421
0.5137	6.99	437	0.4689	0.8506	0.8533	0.8506	0.8449
0.4842	8.0	500	0.4586	0.8484	0.8560	0.8484	0.8498
0.4816	8.99	562	0.4310	0.8534	0.8548	0.8534	0.8518
0.4538	10.0	625	0.4380	0.8529	0.8528	0.8529	0.8493
0.4334	10.99	687	0.4288	0.8625	0.8628	0.8625	0.8617
0.4086	12.0	750	0.4904	0.8608	0.8627	0.8608	0.8592
0.4143	12.99	812	0.4148	0.8675	0.8697	0.8675	0.8663
0.4164	14.0	875	0.4477	0.8647	0.8676	0.8647	0.8649
0.3464	14.99	937	0.4843	0.8512	0.8534	0.8512	0.8500
0.3654	16.0	1000	0.4632	0.8625	0.8631	0.8625	0.8619
0.2933	16.99	1062	0.4811	0.8596	0.8605	0.8596	0.8574
0.3299	18.0	1125	0.4574	0.8664	0.8664	0.8664	0.8656
0.3178	18.99	1187	0.4504	0.8703	0.8697	0.8703	0.8687
0.2976	20.0	1250	0.5002	0.8636	0.8619	0.8636	0.8610
0.2982	20.99	1312	0.4977	0.8720	0.8701	0.8720	0.8701
0.3092	22.0	1375	0.4820	0.8703	0.8710	0.8703	0.8687
0.2835	22.99	1437	0.4671	0.8715	0.8711	0.8715	0.8709
0.2596	24.0	1500	0.5075	0.8732	0.8737	0.8732	0.8729
0.2669	24.99	1562	0.4963	0.8732	0.8719	0.8732	0.8716
0.2409	26.0	1625	0.4955	0.8766	0.8749	0.8766	0.8754
0.2409	26.99	1687	0.4988	0.8777	0.8783	0.8777	0.8776
0.2683	28.0	1750	0.5038	0.8794	0.8781	0.8794	0.8780
0.2299	28.99	1812	0.5038	0.8771	0.8760	0.8771	0.8759
0.2394	29.76	1860	0.5048	0.8788	0.8779	0.8788	0.8775

Framework versions

Transformers 4.37.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.15.1