---
library_name: transformers
license: apache-2.0
base_model: facebook/convnext-tiny-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: convnext-tiny-224-finetuned
  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-tiny-224-finetuned

This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0311
- Logloss: 1.0311
- Accuracy: {'accuracy': 0.5626423690205011}

## 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: 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 | Logloss | Accuracy                          |
|:-------------:|:-------:|:----:|:---------------:|:-------:|:---------------------------------:|
| 1.6135        | 0.9455  | 13   | 1.5881          | 1.5881  | {'accuracy': 0.22323462414578588} |
| 1.5823        | 1.9636  | 27   | 1.5302          | 1.5302  | {'accuracy': 0.35990888382687924} |
| 1.4988        | 2.9818  | 41   | 1.4480          | 1.4480  | {'accuracy': 0.4874715261958998}  |
| 1.4303        | 4.0     | 55   | 1.3424          | 1.3424  | {'accuracy': 0.5034168564920274}  |
| 1.3653        | 4.9455  | 68   | 1.2544          | 1.2544  | {'accuracy': 0.5239179954441914}  |
| 1.2232        | 5.9636  | 82   | 1.1867          | 1.1867  | {'accuracy': 0.5239179954441914}  |
| 1.1734        | 6.9818  | 96   | 1.1330          | 1.1330  | {'accuracy': 0.5466970387243736}  |
| 1.0747        | 8.0     | 110  | 1.1197          | 1.1197  | {'accuracy': 0.5626423690205011}  |
| 1.0405        | 8.9455  | 123  | 1.0871          | 1.0871  | {'accuracy': 0.5535307517084282}  |
| 1.0313        | 9.9636  | 137  | 1.0900          | 1.0900  | {'accuracy': 0.5671981776765376}  |
| 0.959         | 10.9818 | 151  | 1.0766          | 1.0766  | {'accuracy': 0.5603644646924829}  |
| 0.9314        | 12.0    | 165  | 1.0608          | 1.0608  | {'accuracy': 0.5603644646924829}  |
| 0.9102        | 12.9455 | 178  | 1.0388          | 1.0388  | {'accuracy': 0.5649202733485194}  |
| 0.8437        | 13.9636 | 192  | 1.0332          | 1.0332  | {'accuracy': 0.5785876993166287}  |
| 0.8234        | 14.9818 | 206  | 1.0302          | 1.0302  | {'accuracy': 0.5763097949886105}  |
| 0.7883        | 16.0    | 220  | 1.0276          | 1.0276  | {'accuracy': 0.5649202733485194}  |
| 0.7364        | 16.9455 | 233  | 1.0278          | 1.0278  | {'accuracy': 0.5649202733485194}  |
| 0.7561        | 17.9636 | 247  | 1.0258          | 1.0258  | {'accuracy': 0.5649202733485194}  |
| 0.7062        | 18.9818 | 261  | 1.0196          | 1.0196  | {'accuracy': 0.5694760820045558}  |
| 0.6897        | 20.0    | 275  | 1.0308          | 1.0308  | {'accuracy': 0.5558086560364465}  |
| 0.6511        | 20.9455 | 288  | 1.0247          | 1.0247  | {'accuracy': 0.5626423690205011}  |
| 0.6338        | 21.9636 | 302  | 1.0310          | 1.0310  | {'accuracy': 0.5603644646924829}  |
| 0.619         | 22.9818 | 316  | 1.0258          | 1.0258  | {'accuracy': 0.5671981776765376}  |
| 0.6008        | 24.0    | 330  | 1.0299          | 1.0299  | {'accuracy': 0.5626423690205011}  |
| 0.601         | 24.9455 | 343  | 1.0329          | 1.0329  | {'accuracy': 0.5671981776765376}  |
| 0.595         | 25.9636 | 357  | 1.0277          | 1.0277  | {'accuracy': 0.5694760820045558}  |
| 0.598         | 26.9818 | 371  | 1.0288          | 1.0288  | {'accuracy': 0.5671981776765376}  |
| 0.5771        | 28.0    | 385  | 1.0311          | 1.0311  | {'accuracy': 0.5603644646924829}  |
| 0.5829        | 28.3636 | 390  | 1.0311          | 1.0311  | {'accuracy': 0.5626423690205011}  |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1