---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: convnext-base-224_finetuned_on_ImageIn_annotations
  results: []
---

<!-- 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. -->

# convnext-base-224_finetuned_on_ImageIn_annotations

This model is a fine-tuned version of [facebook/convnext-base-224](https://huggingface.co./facebook/convnext-base-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0749
- Precision: 0.9722
- Recall: 0.9811
- F1: 0.9765
- Accuracy: 0.9824

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 83   | 0.1368          | 0.9748    | 0.9632 | 0.9688 | 0.9772   |
| No log        | 2.0   | 166  | 0.0734          | 0.9750    | 0.9727 | 0.9739 | 0.9807   |
| No log        | 3.0   | 249  | 0.0693          | 0.9750    | 0.9727 | 0.9739 | 0.9807   |
| No log        | 4.0   | 332  | 0.0698          | 0.9750    | 0.9727 | 0.9739 | 0.9807   |
| No log        | 5.0   | 415  | 0.0688          | 0.9750    | 0.9727 | 0.9739 | 0.9807   |
| No log        | 6.0   | 498  | 0.0690          | 0.9729    | 0.9751 | 0.9740 | 0.9807   |
| 0.0947        | 7.0   | 581  | 0.0666          | 0.9689    | 0.9800 | 0.9743 | 0.9807   |
| 0.0947        | 8.0   | 664  | 0.0642          | 0.9689    | 0.9800 | 0.9743 | 0.9807   |
| 0.0947        | 9.0   | 747  | 0.0790          | 0.9763    | 0.9763 | 0.9763 | 0.9824   |
| 0.0947        | 10.0  | 830  | 0.0813          | 0.9750    | 0.9727 | 0.9739 | 0.9807   |
| 0.0947        | 11.0  | 913  | 0.0797          | 0.9750    | 0.9727 | 0.9739 | 0.9807   |
| 0.0947        | 12.0  | 996  | 0.0791          | 0.9763    | 0.9763 | 0.9763 | 0.9824   |
| 0.0205        | 13.0  | 1079 | 0.0871          | 0.9750    | 0.9727 | 0.9739 | 0.9807   |
| 0.0205        | 14.0  | 1162 | 0.0716          | 0.9722    | 0.9811 | 0.9765 | 0.9824   |
| 0.0205        | 15.0  | 1245 | 0.0746          | 0.9776    | 0.9799 | 0.9787 | 0.9842   |
| 0.0205        | 16.0  | 1328 | 0.0917          | 0.9738    | 0.9692 | 0.9714 | 0.9789   |
| 0.0205        | 17.0  | 1411 | 0.0694          | 0.9776    | 0.9799 | 0.9787 | 0.9842   |
| 0.0205        | 18.0  | 1494 | 0.0697          | 0.9768    | 0.9859 | 0.9812 | 0.9859   |
| 0.0166        | 19.0  | 1577 | 0.0689          | 0.9702    | 0.9835 | 0.9766 | 0.9824   |
| 0.0166        | 20.0  | 1660 | 0.0995          | 0.9738    | 0.9692 | 0.9714 | 0.9789   |
| 0.0166        | 21.0  | 1743 | 0.0847          | 0.9776    | 0.9799 | 0.9787 | 0.9842   |
| 0.0166        | 22.0  | 1826 | 0.0843          | 0.9776    | 0.9799 | 0.9787 | 0.9842   |
| 0.0166        | 23.0  | 1909 | 0.0869          | 0.9750    | 0.9727 | 0.9739 | 0.9807   |
| 0.0166        | 24.0  | 1992 | 0.0762          | 0.9789    | 0.9835 | 0.9811 | 0.9859   |
| 0.0125        | 25.0  | 2075 | 0.0778          | 0.9789    | 0.9835 | 0.9811 | 0.9859   |
| 0.0125        | 26.0  | 2158 | 0.0834          | 0.9763    | 0.9763 | 0.9763 | 0.9824   |
| 0.0125        | 27.0  | 2241 | 0.0818          | 0.9776    | 0.9799 | 0.9787 | 0.9842   |
| 0.0125        | 28.0  | 2324 | 0.0756          | 0.9684    | 0.9859 | 0.9768 | 0.9824   |
| 0.0125        | 29.0  | 2407 | 0.1150          | 0.9591    | 0.9824 | 0.9700 | 0.9772   |
| 0.0125        | 30.0  | 2490 | 0.0781          | 0.9748    | 0.9883 | 0.9813 | 0.9859   |
| 0.0111        | 31.0  | 2573 | 0.0793          | 0.9716    | 0.9871 | 0.9790 | 0.9842   |
| 0.0111        | 32.0  | 2656 | 0.0713          | 0.9748    | 0.9883 | 0.9813 | 0.9859   |
| 0.0111        | 33.0  | 2739 | 0.0802          | 0.9748    | 0.9883 | 0.9813 | 0.9859   |
| 0.0111        | 34.0  | 2822 | 0.0636          | 0.9802    | 0.9870 | 0.9835 | 0.9877   |
| 0.0111        | 35.0  | 2905 | 0.0702          | 0.9789    | 0.9835 | 0.9811 | 0.9859   |
| 0.0111        | 36.0  | 2988 | 0.0773          | 0.9748    | 0.9883 | 0.9813 | 0.9859   |
| 0.0145        | 37.0  | 3071 | 0.0663          | 0.9781    | 0.9894 | 0.9836 | 0.9877   |
| 0.0145        | 38.0  | 3154 | 0.0721          | 0.9789    | 0.9835 | 0.9811 | 0.9859   |
| 0.0145        | 39.0  | 3237 | 0.0708          | 0.9789    | 0.9835 | 0.9811 | 0.9859   |
| 0.0145        | 40.0  | 3320 | 0.0729          | 0.9748    | 0.9883 | 0.9813 | 0.9859   |
| 0.0145        | 41.0  | 3403 | 0.0760          | 0.9748    | 0.9883 | 0.9813 | 0.9859   |
| 0.0145        | 42.0  | 3486 | 0.0771          | 0.9716    | 0.9871 | 0.9790 | 0.9842   |
| 0.0106        | 43.0  | 3569 | 0.0713          | 0.9748    | 0.9883 | 0.9813 | 0.9859   |
| 0.0106        | 44.0  | 3652 | 0.0721          | 0.9748    | 0.9883 | 0.9813 | 0.9859   |
| 0.0106        | 45.0  | 3735 | 0.0732          | 0.9768    | 0.9859 | 0.9812 | 0.9859   |
| 0.0106        | 46.0  | 3818 | 0.0783          | 0.9789    | 0.9835 | 0.9811 | 0.9859   |
| 0.0106        | 47.0  | 3901 | 0.0770          | 0.9789    | 0.9835 | 0.9811 | 0.9859   |
| 0.0106        | 48.0  | 3984 | 0.0744          | 0.9735    | 0.9847 | 0.9789 | 0.9842   |
| 0.0082        | 49.0  | 4067 | 0.0752          | 0.9722    | 0.9811 | 0.9765 | 0.9824   |
| 0.0082        | 50.0  | 4150 | 0.0749          | 0.9722    | 0.9811 | 0.9765 | 0.9824   |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1