---
library_name: transformers
license: apache-2.0
base_model: facebook/dinov2-base
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
model-index:
- name: spermatogenesis-classifier
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8910256410256411
    - name: F1
      type: f1
      value: 0.8896300082346593
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# spermatogenesis-classifier

This model is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3150
- Accuracy: 0.8910
- F1: 0.8896
- Acc I-iv: 0.8710
- Acc Ix-x: 0.9048
- Acc V-vi: 0.8511
- Acc Vii-vii: 0.9714
- Acc Xi- xii: 0.8636

## 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
- mixed_precision_training: Native AMP

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:--------:|:--------:|:-----------:|:-----------:|
| 2.7787        | 1.0   | 20   | 1.2571          | 0.4295   | 0.3860 | 0.0      | 0.4762   | 0.5532   | 0.3143      | 0.9091      |
| 1.6595        | 2.0   | 40   | 1.5081          | 0.3590   | 0.3372 | 0.0323   | 0.5238   | 0.0426   | 0.5714      | 1.0         |
| 1.5573        | 3.0   | 60   | 0.8242          | 0.5513   | 0.5636 | 0.3871   | 0.5238   | 0.3830   | 0.6571      | 1.0         |
| 1.2868        | 4.0   | 80   | 0.7303          | 0.7885   | 0.7570 | 0.5484   | 0.4762   | 0.8723   | 0.9429      | 1.0         |
| 0.9034        | 5.0   | 100  | 0.4915          | 0.8077   | 0.8036 | 0.7097   | 0.8095   | 0.7660   | 0.8857      | 0.9091      |
| 1.2132        | 6.0   | 120  | 0.5243          | 0.8013   | 0.7923 | 0.6452   | 0.8095   | 0.9574   | 0.6571      | 0.9091      |
| 0.8576        | 7.0   | 140  | 0.7115          | 0.7692   | 0.7224 | 0.5806   | 0.2857   | 0.8298   | 1.0         | 1.0         |
| 0.8557        | 8.0   | 160  | 0.5277          | 0.7692   | 0.7716 | 0.8710   | 0.6667   | 0.5106   | 1.0         | 0.9091      |
| 0.7294        | 9.0   | 180  | 0.4170          | 0.8333   | 0.8306 | 0.6129   | 0.9048   | 0.8511   | 0.9143      | 0.9091      |
| 0.6713        | 10.0  | 200  | 0.4585          | 0.8141   | 0.8070 | 0.9032   | 0.9048   | 0.7234   | 0.8571      | 0.7273      |
| 0.7973        | 11.0  | 220  | 0.4767          | 0.8397   | 0.8241 | 0.7097   | 0.6667   | 0.8936   | 0.8857      | 1.0         |
| 0.6637        | 12.0  | 240  | 0.4327          | 0.8013   | 0.8057 | 0.9032   | 0.7619   | 0.6170   | 0.9143      | 0.9091      |
| 0.6284        | 13.0  | 260  | 0.3897          | 0.8462   | 0.8331 | 0.5484   | 0.8571   | 0.9149   | 1.0         | 0.8636      |
| 0.7981        | 14.0  | 280  | 0.3915          | 0.8654   | 0.8512 | 0.6774   | 0.9524   | 0.9362   | 0.9429      | 0.7727      |
| 0.5017        | 15.0  | 300  | 0.3150          | 0.8910   | 0.8896 | 0.8710   | 0.9048   | 0.8511   | 0.9714      | 0.8636      |
| 0.5893        | 16.0  | 320  | 0.3640          | 0.8526   | 0.8485 | 0.8065   | 0.9048   | 0.8298   | 0.9429      | 0.7727      |
| 0.6591        | 17.0  | 340  | 0.3563          | 0.8718   | 0.8684 | 0.7742   | 0.8571   | 0.8723   | 0.9429      | 0.9091      |
| 0.4976        | 18.0  | 360  | 0.3648          | 0.8397   | 0.8393 | 0.9355   | 0.8095   | 0.7447   | 0.8857      | 0.8636      |
| 0.5034        | 19.0  | 380  | 0.3839          | 0.8462   | 0.8389 | 0.6452   | 0.7619   | 0.9149   | 0.9429      | 0.9091      |
| 0.4612        | 20.0  | 400  | 0.3724          | 0.8654   | 0.8636 | 0.9032   | 0.8571   | 0.8723   | 0.8286      | 0.8636      |


### Framework versions

- Transformers 5.6.2
- Pytorch 2.11.0+cu130
- Datasets 4.8.4
- Tokenizers 0.22.2