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---
license: apache-2.0
base_model: facebook/convnextv2-tiny-1k-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
model-index:
- name: convnextv2-tiny-1k-224-finetuned-bottomwear-v2
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: 0.8981481481481481
- name: Precision
type: precision
value: 0.9001054377012231
---
<!-- 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. -->
# convnextv2-tiny-1k-224-finetuned-bottomwear-v2
This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3267
- Accuracy: 0.8981
- Precision: 0.9001
## 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: 10
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|
| No log | 1.0 | 87 | 1.3349 | 0.6852 | 0.7395 |
| No log | 2.0 | 174 | 0.7869 | 0.8426 | 0.8543 |
| No log | 3.0 | 261 | 0.6571 | 0.8472 | 0.8712 |
| No log | 4.0 | 348 | 0.4293 | 0.9028 | 0.9122 |
| No log | 5.0 | 435 | 0.4030 | 0.8935 | 0.8953 |
| 0.916 | 6.0 | 522 | 0.4251 | 0.8657 | 0.8787 |
| 0.916 | 7.0 | 609 | 0.3536 | 0.8889 | 0.8936 |
| 0.916 | 8.0 | 696 | 0.3611 | 0.8796 | 0.8833 |
| 0.916 | 9.0 | 783 | 0.3267 | 0.8981 | 0.9001 |
| 0.916 | 10.0 | 870 | 0.3526 | 0.8796 | 0.8972 |
| 0.916 | 11.0 | 957 | 0.3694 | 0.8981 | 0.9100 |
| 0.3192 | 12.0 | 1044 | 0.3694 | 0.8935 | 0.9007 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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