metadata

license: apache-2.0
base_model: facebook/dinov2-small-imagenet1k-1-layer
tags:
  - image-classification
  - vision
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: dinov2-small-imagenet1k-1-layer-finetuned-galaxy10-decals
    results: []

dinov2-small-imagenet1k-1-layer-finetuned-galaxy10-decals

This model is a fine-tuned version of facebook/dinov2-small-imagenet1k-1-layer on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

Loss: 0.5373
Accuracy: 0.8563
Precision: 0.8536
Recall: 0.8563
F1: 0.8543

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
1.0027	0.99	62	0.8262	0.7080	0.7231	0.7080	0.7017
0.8374	2.0	125	0.6129	0.7948	0.7960	0.7948	0.7899
0.7753	2.99	187	0.6555	0.7892	0.7921	0.7892	0.7787
0.7165	4.0	250	0.5862	0.8005	0.8053	0.8005	0.7970
0.6477	4.99	312	0.6183	0.7965	0.8119	0.7965	0.7985
0.6892	6.0	375	0.5310	0.8247	0.8275	0.8247	0.8195
0.6171	6.99	437	0.5678	0.8083	0.8157	0.8083	0.8022
0.55	8.0	500	0.4961	0.8326	0.8353	0.8326	0.8316
0.5615	8.99	562	0.5033	0.8309	0.8312	0.8309	0.8274
0.5107	10.0	625	0.5162	0.8191	0.8164	0.8191	0.8152
0.5237	10.99	687	0.4790	0.8422	0.8452	0.8422	0.8381
0.4954	12.0	750	0.4782	0.8422	0.8430	0.8422	0.8373
0.4887	12.99	812	0.4689	0.8371	0.8395	0.8371	0.8358
0.4629	14.0	875	0.4541	0.8523	0.8500	0.8523	0.8502
0.4486	14.99	937	0.4755	0.8405	0.8400	0.8405	0.8394
0.4361	16.0	1000	0.4763	0.8371	0.8392	0.8371	0.8370
0.3833	16.99	1062	0.4982	0.8416	0.8429	0.8416	0.8396
0.3788	18.0	1125	0.5632	0.8292	0.8365	0.8292	0.8267
0.3722	18.99	1187	0.5162	0.8388	0.8364	0.8388	0.8357
0.3467	20.0	1250	0.5125	0.8399	0.8357	0.8399	0.8342
0.3518	20.99	1312	0.5569	0.8309	0.8327	0.8309	0.8276
0.3432	22.0	1375	0.5032	0.8484	0.8451	0.8484	0.8454
0.3067	22.99	1437	0.5246	0.8433	0.8462	0.8433	0.8433
0.2923	24.0	1500	0.5363	0.8467	0.8482	0.8467	0.8464
0.303	24.99	1562	0.5435	0.8484	0.8453	0.8484	0.8457
0.2523	26.0	1625	0.5500	0.8444	0.8422	0.8444	0.8419
0.2523	26.99	1687	0.5369	0.8529	0.8533	0.8529	0.8529
0.262	28.0	1750	0.5373	0.8563	0.8536	0.8563	0.8543
0.232	28.99	1812	0.5384	0.8529	0.8509	0.8529	0.8516
0.2278	29.76	1860	0.5429	0.8512	0.8489	0.8512	0.8495

Framework versions

Transformers 4.37.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.15.1