metadata
library_name: transformers
base_model: MoGHenry/cat_dog_classifier_with_small_datasest
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: cat_dog_classifier_with_small_datasest
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: 0.9571428571428572
cat_dog_classifier_with_small_datasest
This model is a fine-tuned version of MoGHenry/cat_dog_classifier_with_small_datasest on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1495
- Accuracy: 0.9571
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 18 | 0.1574 | 0.9571 |
| No log | 2.0 | 36 | 0.1638 | 0.9429 |
| No log | 3.0 | 54 | 0.1581 | 0.9429 |
| No log | 4.0 | 72 | 0.1470 | 0.9571 |
| No log | 5.0 | 90 | 0.1424 | 0.9571 |
| No log | 6.0 | 108 | 0.1389 | 0.9643 |
| No log | 7.0 | 126 | 0.1632 | 0.9429 |
| No log | 8.0 | 144 | 0.1510 | 0.95 |
| No log | 9.0 | 162 | 0.1474 | 0.95 |
| No log | 10.0 | 180 | 0.1430 | 0.9571 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0