v2: acc=0.8506, f1=0.8497 (fresh DINOv2-base, augmented, updated dataset)

cc128be verified about 1 month ago

3.49 kB

library_name: transformers
license: apache-2.0
base_model: facebook/dinov2-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: spermatogenesis-classifier-v2
    results: []

spermatogenesis-classifier-v2

This model is a fine-tuned version of facebook/dinov2-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4213
Accuracy: 0.8506
F1: 0.8497
Acc I-iv: 0.6970
Acc Ix-x: 0.8571
Acc V-vi: 0.8571
Acc Vii-vii: 0.9574
Acc Xi- xii: 0.8387

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 50
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Acc I-iv	Acc Ix-x	Acc V-vi	Acc Vii-vii	Acc Xi- xii
0.9761	1.0	25	1.0469	0.5977	0.5529	0.3030	0.0476	0.8571	0.9787	0.3548
0.8169	2.0	50	0.8854	0.6839	0.6571	0.2424	0.9048	0.8810	0.8936	0.4194
0.6579	3.0	75	0.9098	0.6782	0.6655	0.2727	0.9048	0.9762	0.6809	0.5484
0.3899	4.0	100	0.5647	0.7874	0.7882	0.6364	0.9048	0.7857	0.8936	0.7097
0.5074	5.0	125	1.0395	0.6609	0.6200	0.0909	1.0	0.9762	0.7447	0.4839
0.4786	6.0	150	0.5871	0.7816	0.7842	0.6364	0.9048	0.8333	0.8298	0.7097
0.3498	7.0	175	0.8465	0.6897	0.6908	0.7576	0.8095	0.5476	0.5745	0.9032
0.3699	8.0	200	0.4213	0.8506	0.8497	0.6970	0.8571	0.8571	0.9574	0.8387
0.5555	9.0	225	0.5174	0.7874	0.7892	0.6667	0.8571	0.8333	0.8298	0.7419
0.2530	10.0	250	0.7004	0.7989	0.7994	0.7576	0.8571	0.7857	0.9362	0.6129
0.2849	11.0	275	0.5274	0.8103	0.8078	0.6061	0.8095	0.9286	0.9149	0.7097
0.1722	12.0	300	0.5315	0.7931	0.7934	0.7879	0.8571	0.5714	0.9362	0.8387
0.1955	13.0	325	0.9076	0.7644	0.7517	0.4545	0.8571	0.9048	0.9787	0.5161

Framework versions

Transformers 5.6.2
Pytorch 2.11.0+cu130
Tokenizers 0.22.2