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-upperfull-mix
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: 0.8004385964912281
- name: Precision
type: precision
value: 0.8160100686256399
convnextv2-tiny-1k-224-finetuned-crop-upperfull-mix
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: 0.6187
- Accuracy: 0.8004
- Precision: 0.8160
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 | 183 | 2.1471 | 0.4693 | 0.5128 |
| No log | 2.0 | 366 | 1.4576 | 0.6579 | 0.6955 |
| 1.9821 | 3.0 | 549 | 1.1372 | 0.6754 | 0.7183 |
| 1.9821 | 4.0 | 732 | 0.9214 | 0.7303 | 0.7659 |
| 1.9821 | 5.0 | 915 | 0.7792 | 0.7478 | 0.7661 |
| 0.8885 | 6.0 | 1098 | 0.7455 | 0.7654 | 0.7780 |
| 0.8885 | 7.0 | 1281 | 0.6756 | 0.7873 | 0.8020 |
| 0.8885 | 8.0 | 1464 | 0.6787 | 0.7807 | 0.7932 |
| 0.5696 | 9.0 | 1647 | 0.6694 | 0.7982 | 0.8099 |
| 0.5696 | 10.0 | 1830 | 0.6799 | 0.7741 | 0.7930 |
| 0.4056 | 11.0 | 2013 | 0.6187 | 0.8004 | 0.8160 |
| 0.4056 | 12.0 | 2196 | 0.6868 | 0.7675 | 0.8063 |
| 0.4056 | 13.0 | 2379 | 0.7525 | 0.7544 | 0.7803 |
| 0.2904 | 14.0 | 2562 | 0.6572 | 0.7895 | 0.8093 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1