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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-pattern-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: 0.8075
- name: Precision
type: precision
value: 0.815145699366171
---
<!-- 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-pattern-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: 0.5496
- Accuracy: 0.8075
- Precision: 0.8151
## 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 | 320 | 0.8829 | 0.7275 | 0.7493 |
| 1.2279 | 2.0 | 640 | 0.7396 | 0.7412 | 0.7517 |
| 1.2279 | 3.0 | 960 | 0.6526 | 0.7775 | 0.7902 |
| 0.6811 | 4.0 | 1280 | 0.5722 | 0.7975 | 0.8076 |
| 0.5073 | 5.0 | 1600 | 0.5496 | 0.8075 | 0.8151 |
| 0.5073 | 6.0 | 1920 | 0.6014 | 0.7887 | 0.7991 |
| 0.4098 | 7.0 | 2240 | 0.5759 | 0.8125 | 0.8171 |
| 0.3357 | 8.0 | 2560 | 0.6241 | 0.7987 | 0.8126 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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