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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50-resnet50_fashion
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: 0.974025974025974
---
<!-- 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. -->
# resnet-50-resnet50_fashion
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1064
- Accuracy: 0.9740
## 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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 7
- total_train_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6532 | 0.99 | 86 | 0.6781 | 0.6345 |
| 0.5407 | 2.0 | 173 | 0.5222 | 0.8590 |
| 0.4086 | 2.99 | 259 | 0.3595 | 0.8924 |
| 0.3449 | 4.0 | 346 | 0.2616 | 0.9184 |
| 0.3518 | 4.99 | 432 | 0.2288 | 0.9443 |
| 0.308 | 6.0 | 519 | 0.2758 | 0.9425 |
| 0.3209 | 7.0 | 606 | 0.3777 | 0.9369 |
| 0.284 | 7.99 | 692 | 0.1704 | 0.9555 |
| 0.2466 | 9.0 | 779 | 0.1571 | 0.9462 |
| 0.3123 | 9.99 | 865 | 0.6492 | 0.9406 |
| 0.2827 | 11.0 | 952 | 0.4968 | 0.9406 |
| 0.2736 | 11.99 | 1038 | 0.1370 | 0.9592 |
| 0.2476 | 13.0 | 1125 | 0.1616 | 0.9499 |
| 0.195 | 14.0 | 1212 | 0.1362 | 0.9610 |
| 0.2536 | 14.99 | 1298 | 0.1298 | 0.9536 |
| 0.2022 | 16.0 | 1385 | 0.7470 | 0.9518 |
| 0.2406 | 16.99 | 1471 | 0.1241 | 0.9647 |
| 0.2019 | 18.0 | 1558 | 0.1278 | 0.9536 |
| 0.2073 | 18.99 | 1644 | 0.1134 | 0.9685 |
| 0.1873 | 20.0 | 1731 | 0.6738 | 0.9629 |
| 0.2446 | 21.0 | 1818 | 0.1033 | 0.9685 |
| 0.1999 | 21.99 | 1904 | 0.1181 | 0.9647 |
| 0.1716 | 23.0 | 1991 | 0.1099 | 0.9610 |
| 0.175 | 23.99 | 2077 | 0.1064 | 0.9740 |
| 0.1962 | 25.0 | 2164 | 0.1174 | 0.9722 |
| 0.1943 | 25.99 | 2250 | 1.0625 | 0.9518 |
| 0.2044 | 27.0 | 2337 | 0.8419 | 0.9573 |
| 0.1835 | 28.0 | 2424 | 0.1112 | 0.9703 |
| 0.191 | 28.99 | 2510 | 0.1142 | 0.9685 |
| 0.1676 | 30.0 | 2597 | 0.1080 | 0.9647 |
| 0.1533 | 30.99 | 2683 | 0.1494 | 0.9647 |
| 0.1991 | 32.0 | 2770 | 0.1000 | 0.9703 |
| 0.1845 | 32.99 | 2856 | 0.0989 | 0.9740 |
| 0.1605 | 34.0 | 2943 | 0.0975 | 0.9685 |
| 0.1928 | 35.0 | 3030 | 0.4555 | 0.9629 |
| 0.1506 | 35.99 | 3116 | 0.1059 | 0.9703 |
| 0.1912 | 37.0 | 3203 | 0.1016 | 0.9647 |
| 0.1689 | 37.99 | 3289 | 0.5421 | 0.9666 |
| 0.1467 | 39.0 | 3376 | 0.1095 | 0.9647 |
| 0.1513 | 39.99 | 3462 | 0.3828 | 0.9703 |
| 0.1768 | 41.0 | 3549 | 0.0945 | 0.9703 |
| 0.1633 | 42.0 | 3636 | 0.2250 | 0.9592 |
| 0.1945 | 42.99 | 3722 | 0.2015 | 0.9685 |
| 0.1896 | 44.0 | 3809 | 0.1114 | 0.9666 |
| 0.1629 | 44.99 | 3895 | 0.0954 | 0.9666 |
| 0.1825 | 46.0 | 3982 | 0.0974 | 0.9740 |
| 0.1664 | 46.99 | 4068 | 0.0939 | 0.9703 |
| 0.1535 | 48.0 | 4155 | 0.0935 | 0.9722 |
| 0.1801 | 49.0 | 4242 | 0.0999 | 0.9703 |
| 0.1502 | 49.67 | 4300 | 0.1959 | 0.9703 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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