---
license: apache-2.0
base_model: matthieulel/dinov2-base-imagenet1k-1-layer-finetuned-galaxy10-decals
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: dinov2-base-imagenet1k-1-layer-finetuned-galaxy10-decals-head-finetuned-100-galaxy_mnist
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# dinov2-base-imagenet1k-1-layer-finetuned-galaxy10-decals-head-finetuned-100-galaxy_mnist

This model is a fine-tuned version of [matthieulel/dinov2-base-imagenet1k-1-layer-finetuned-galaxy10-decals](https://huggingface.co/matthieulel/dinov2-base-imagenet1k-1-layer-finetuned-galaxy10-decals) on the matthieulel/galaxy_mnist dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2235
- Accuracy: 0.909
- Precision: 0.9108
- Recall: 0.909
- F1: 0.9089

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.1235        | 0.99  | 62   | 0.9579          | 0.622    | 0.6410    | 0.622  | 0.6249 |
| 0.5488        | 2.0   | 125  | 0.3964          | 0.852    | 0.8534    | 0.852  | 0.8523 |
| 0.384         | 2.99  | 187  | 0.2877          | 0.882    | 0.8820    | 0.882  | 0.8819 |
| 0.3493        | 4.0   | 250  | 0.2621          | 0.8885   | 0.8885    | 0.8885 | 0.8885 |
| 0.3184        | 4.99  | 312  | 0.2570          | 0.8945   | 0.8957    | 0.8945 | 0.8946 |
| 0.3027        | 6.0   | 375  | 0.2485          | 0.9      | 0.9013    | 0.9    | 0.9000 |
| 0.3245        | 6.99  | 437  | 0.2397          | 0.9035   | 0.9038    | 0.9035 | 0.9034 |
| 0.2666        | 8.0   | 500  | 0.2413          | 0.9035   | 0.9051    | 0.9035 | 0.9034 |
| 0.2705        | 8.99  | 562  | 0.2336          | 0.905    | 0.9057    | 0.905  | 0.9050 |
| 0.2524        | 10.0  | 625  | 0.2320          | 0.9065   | 0.9072    | 0.9065 | 0.9065 |
| 0.271         | 10.99 | 687  | 0.2293          | 0.908    | 0.9085    | 0.908  | 0.9079 |
| 0.2624        | 12.0  | 750  | 0.2266          | 0.9045   | 0.9047    | 0.9045 | 0.9045 |
| 0.2681        | 12.99 | 812  | 0.2286          | 0.908    | 0.9088    | 0.908  | 0.9079 |
| 0.262         | 14.0  | 875  | 0.2239          | 0.9085   | 0.9089    | 0.9085 | 0.9085 |
| 0.3015        | 14.99 | 937  | 0.2265          | 0.9075   | 0.9085    | 0.9075 | 0.9074 |
| 0.2635        | 16.0  | 1000 | 0.2256          | 0.9065   | 0.9082    | 0.9065 | 0.9064 |
| 0.2708        | 16.99 | 1062 | 0.2239          | 0.9075   | 0.9089    | 0.9075 | 0.9075 |
| 0.2616        | 18.0  | 1125 | 0.2231          | 0.9075   | 0.9084    | 0.9075 | 0.9074 |
| 0.2658        | 18.99 | 1187 | 0.2229          | 0.9075   | 0.9092    | 0.9075 | 0.9075 |
| 0.2578        | 20.0  | 1250 | 0.2216          | 0.908    | 0.9093    | 0.908  | 0.9080 |
| 0.2823        | 20.99 | 1312 | 0.2206          | 0.9075   | 0.9084    | 0.9075 | 0.9075 |
| 0.2714        | 22.0  | 1375 | 0.2223          | 0.9085   | 0.9100    | 0.9085 | 0.9084 |
| 0.2631        | 22.99 | 1437 | 0.2235          | 0.909    | 0.9108    | 0.909  | 0.9089 |
| 0.2695        | 24.0  | 1500 | 0.2214          | 0.9085   | 0.9101    | 0.9085 | 0.9084 |
| 0.2698        | 24.99 | 1562 | 0.2208          | 0.908    | 0.9095    | 0.908  | 0.9079 |
| 0.2285        | 26.0  | 1625 | 0.2188          | 0.9075   | 0.9083    | 0.9075 | 0.9075 |
| 0.2716        | 26.99 | 1687 | 0.2202          | 0.9075   | 0.9089    | 0.9075 | 0.9075 |
| 0.2628        | 28.0  | 1750 | 0.2205          | 0.9075   | 0.9090    | 0.9075 | 0.9074 |
| 0.2619        | 28.99 | 1812 | 0.2207          | 0.907    | 0.9085    | 0.907  | 0.9069 |
| 0.2568        | 29.76 | 1860 | 0.2204          | 0.907    | 0.9085    | 0.907  | 0.9069 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1