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---
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
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