metadata
license: apache-2.0
base_model: microsoft/resnet-152
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Dogs-Breed-Image-Classification-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: 0.8408163265306122
Dogs-Breed-Image-Classification-V2
This model is a fine-tuned version of microsoft/resnet-152 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0115
- Accuracy: 0.8408
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: 5e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 483 | 4.6525 | 0.7382 |
| 4.7329 | 2.0 | 966 | 4.3558 | 0.7298 |
| 4.5033 | 3.0 | 1449 | 3.9568 | 0.7471 |
| 4.1405 | 4.0 | 1932 | 3.5160 | 0.7782 |
| 3.7176 | 5.0 | 2415 | 3.0805 | 0.7946 |
| 3.293 | 6.0 | 2898 | 2.6907 | 0.8021 |
| 2.8898 | 7.0 | 3381 | 2.3044 | 0.8126 |
| 2.5343 | 8.0 | 3864 | 2.0091 | 0.8177 |
| 2.2188 | 9.0 | 4347 | 1.7910 | 0.8126 |
| 1.9698 | 10.0 | 4830 | 1.6015 | 0.8194 |
| 1.7532 | 11.0 | 5313 | 1.4383 | 0.8220 |
| 1.586 | 12.0 | 5796 | 1.3355 | 0.8264 |
| 1.4533 | 13.0 | 6279 | 1.2467 | 0.8260 |
| 1.336 | 14.0 | 6762 | 1.1575 | 0.8313 |
| 1.2641 | 15.0 | 7245 | 1.1038 | 0.8321 |
| 1.185 | 16.0 | 7728 | 1.0606 | 0.8395 |
| 1.1329 | 17.0 | 8211 | 1.0178 | 0.8398 |
| 1.0977 | 18.0 | 8694 | 1.0115 | 0.8408 |
| 1.0732 | 19.0 | 9177 | 0.9945 | 0.8381 |
| 1.0508 | 20.0 | 9660 | 0.9930 | 0.8393 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.15.0
- Tokenizers 0.15.1