metadata
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-crop-neckline
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: 0.8095238095238095
- name: Precision
type: precision
value: 0.8100590473699718
convnextv2-tiny-1k-224-finetuned-crop-neckline
This model is a fine-tuned version of facebook/convnextv2-tiny-1k-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6160
- Accuracy: 0.8095
- Precision: 0.8101
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 | 84 | 1.4226 | 0.5619 | 0.5870 |
| No log | 2.0 | 168 | 1.1924 | 0.5619 | 0.5663 |
| No log | 3.0 | 252 | 0.9542 | 0.6952 | 0.7317 |
| No log | 4.0 | 336 | 0.8255 | 0.7143 | 0.7224 |
| No log | 5.0 | 420 | 0.7614 | 0.7190 | 0.7378 |
| 1.1937 | 6.0 | 504 | 0.7303 | 0.7381 | 0.7454 |
| 1.1937 | 7.0 | 588 | 0.6770 | 0.7667 | 0.7772 |
| 1.1937 | 8.0 | 672 | 0.6849 | 0.7667 | 0.7748 |
| 1.1937 | 9.0 | 756 | 0.6720 | 0.7381 | 0.7532 |
| 1.1937 | 10.0 | 840 | 0.7036 | 0.7286 | 0.7429 |
| 1.1937 | 11.0 | 924 | 0.6752 | 0.7619 | 0.7827 |
| 0.6846 | 12.0 | 1008 | 0.6399 | 0.7810 | 0.7860 |
| 0.6846 | 13.0 | 1092 | 0.6860 | 0.7381 | 0.7553 |
| 0.6846 | 14.0 | 1176 | 0.6827 | 0.7476 | 0.7644 |
| 0.6846 | 15.0 | 1260 | 0.6160 | 0.8095 | 0.8101 |
| 0.6846 | 16.0 | 1344 | 0.7032 | 0.7619 | 0.7695 |
| 0.6846 | 17.0 | 1428 | 0.6916 | 0.8048 | 0.8197 |
| 0.5051 | 18.0 | 1512 | 0.7070 | 0.7810 | 0.7891 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1