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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-topwear-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: 0.8177083333333334
- name: Precision
type: precision
value: 0.8427431106605461
---
<!-- 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-topwear-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: 0.5060
- Accuracy: 0.8177
- Precision: 0.8427
## 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 | 77 | 1.4963 | 0.6615 | 0.7047 |
| No log | 2.0 | 154 | 1.0708 | 0.6354 | 0.7218 |
| No log | 3.0 | 231 | 0.8045 | 0.7708 | 0.8080 |
| No log | 4.0 | 308 | 0.6572 | 0.7969 | 0.8195 |
| No log | 5.0 | 385 | 0.5992 | 0.7969 | 0.8338 |
| No log | 6.0 | 462 | 0.5877 | 0.8021 | 0.8398 |
| 0.8831 | 7.0 | 539 | 0.5497 | 0.8073 | 0.8614 |
| 0.8831 | 8.0 | 616 | 0.5412 | 0.8073 | 0.8275 |
| 0.8831 | 9.0 | 693 | 0.5060 | 0.8177 | 0.8427 |
| 0.8831 | 10.0 | 770 | 0.5167 | 0.8281 | 0.8372 |
| 0.8831 | 11.0 | 847 | 0.6315 | 0.7969 | 0.8148 |
| 0.8831 | 12.0 | 924 | 0.5166 | 0.8125 | 0.8318 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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