|
---
|
|
base_model: MBZUAI/swiftformer-xs
|
|
tags:
|
|
- generated_from_trainer
|
|
datasets:
|
|
- imagefolder
|
|
metrics:
|
|
- accuracy
|
|
model-index:
|
|
- name: swiftformer-xs-ve-U13-b-80b
|
|
results:
|
|
- task:
|
|
name: Image Classification
|
|
type: image-classification
|
|
dataset:
|
|
name: imagefolder
|
|
type: imagefolder
|
|
config: default
|
|
split: validation
|
|
args: default
|
|
metrics:
|
|
- name: Accuracy
|
|
type: accuracy
|
|
value: 0.6521739130434783
|
|
---
|
|
|
|
<!-- 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. -->
|
|
|
|
# swiftformer-xs-ve-U13-b-80b
|
|
|
|
This model is a fine-tuned version of [MBZUAI/swiftformer-xs](https://huggingface.co./MBZUAI/swiftformer-xs) on the imagefolder dataset.
|
|
It achieves the following results on the evaluation set:
|
|
- Loss: 1.2197
|
|
- Accuracy: 0.6522
|
|
|
|
## 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: 32
|
|
- eval_batch_size: 32
|
|
- seed: 42
|
|
- gradient_accumulation_steps: 4
|
|
- total_train_batch_size: 128
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
|
- lr_scheduler_type: linear
|
|
- lr_scheduler_warmup_ratio: 0.1
|
|
- num_epochs: 80
|
|
|
|
### Training results
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|
|
|
| No log | 0.92 | 6 | 1.3862 | 0.2174 |
|
|
| 1.3862 | 2.0 | 13 | 1.3856 | 0.3261 |
|
|
| 1.3862 | 2.92 | 19 | 1.3849 | 0.2826 |
|
|
| 1.3848 | 4.0 | 26 | 1.3830 | 0.2609 |
|
|
| 1.3806 | 4.92 | 32 | 1.3804 | 0.1739 |
|
|
| 1.3806 | 6.0 | 39 | 1.3758 | 0.1957 |
|
|
| 1.3662 | 6.92 | 45 | 1.3700 | 0.1739 |
|
|
| 1.3261 | 8.0 | 52 | 1.3652 | 0.1739 |
|
|
| 1.3261 | 8.92 | 58 | 1.3625 | 0.1522 |
|
|
| 1.2588 | 10.0 | 65 | 1.3629 | 0.1304 |
|
|
| 1.1972 | 10.92 | 71 | 1.3592 | 0.1304 |
|
|
| 1.1972 | 12.0 | 78 | 1.3570 | 0.2174 |
|
|
| 1.1578 | 12.92 | 84 | 1.3590 | 0.1957 |
|
|
| 1.124 | 14.0 | 91 | 1.3731 | 0.2174 |
|
|
| 1.124 | 14.92 | 97 | 1.3718 | 0.1522 |
|
|
| 1.1045 | 16.0 | 104 | 1.3736 | 0.1739 |
|
|
| 1.0703 | 16.92 | 110 | 1.4983 | 0.2174 |
|
|
| 1.0703 | 18.0 | 117 | 1.5455 | 0.1739 |
|
|
| 1.0663 | 18.92 | 123 | 1.4473 | 0.1739 |
|
|
| 1.01 | 20.0 | 130 | 1.4011 | 0.2609 |
|
|
| 1.01 | 20.92 | 136 | 1.4053 | 0.2826 |
|
|
| 0.9961 | 22.0 | 143 | 1.4186 | 0.2174 |
|
|
| 0.9961 | 22.92 | 149 | 1.5168 | 0.2609 |
|
|
| 0.9754 | 24.0 | 156 | 1.3873 | 0.2826 |
|
|
| 0.9417 | 24.92 | 162 | 1.4656 | 0.3261 |
|
|
| 0.9417 | 26.0 | 169 | 1.3499 | 0.2609 |
|
|
| 0.9286 | 26.92 | 175 | 1.3902 | 0.3043 |
|
|
| 0.9216 | 28.0 | 182 | 1.4819 | 0.3261 |
|
|
| 0.9216 | 28.92 | 188 | 1.4133 | 0.3043 |
|
|
| 0.8868 | 30.0 | 195 | 1.4124 | 0.4130 |
|
|
| 0.8908 | 30.92 | 201 | 1.4421 | 0.3478 |
|
|
| 0.8908 | 32.0 | 208 | 1.5085 | 0.3043 |
|
|
| 0.8729 | 32.92 | 214 | 1.3854 | 0.3478 |
|
|
| 0.8685 | 34.0 | 221 | 1.3264 | 0.3043 |
|
|
| 0.8685 | 34.92 | 227 | 1.3947 | 0.3043 |
|
|
| 0.8739 | 36.0 | 234 | 1.3455 | 0.3913 |
|
|
| 0.8288 | 36.92 | 240 | 1.3621 | 0.3913 |
|
|
| 0.8288 | 38.0 | 247 | 1.3875 | 0.3913 |
|
|
| 0.8369 | 38.92 | 253 | 1.4274 | 0.3696 |
|
|
| 0.8101 | 40.0 | 260 | 1.3251 | 0.4565 |
|
|
| 0.8101 | 40.92 | 266 | 1.3039 | 0.4783 |
|
|
| 0.8126 | 42.0 | 273 | 1.2523 | 0.5435 |
|
|
| 0.8126 | 42.92 | 279 | 1.3060 | 0.5217 |
|
|
| 0.7971 | 44.0 | 286 | 1.2678 | 0.5217 |
|
|
| 0.7806 | 44.92 | 292 | 1.3332 | 0.5 |
|
|
| 0.7806 | 46.0 | 299 | 1.2550 | 0.5652 |
|
|
| 0.7899 | 46.92 | 305 | 1.2517 | 0.5870 |
|
|
| 0.7602 | 48.0 | 312 | 1.2627 | 0.5870 |
|
|
| 0.7602 | 48.92 | 318 | 1.2620 | 0.6087 |
|
|
| 0.7748 | 50.0 | 325 | 1.2286 | 0.5652 |
|
|
| 0.7613 | 50.92 | 331 | 1.1997 | 0.6087 |
|
|
| 0.7613 | 52.0 | 338 | 1.2353 | 0.5870 |
|
|
| 0.7514 | 52.92 | 344 | 1.2466 | 0.5870 |
|
|
| 0.7581 | 54.0 | 351 | 1.2161 | 0.5870 |
|
|
| 0.7581 | 54.92 | 357 | 1.2396 | 0.5435 |
|
|
| 0.7401 | 56.0 | 364 | 1.1859 | 0.6087 |
|
|
| 0.7421 | 56.92 | 370 | 1.1757 | 0.6304 |
|
|
| 0.7421 | 58.0 | 377 | 1.1754 | 0.5870 |
|
|
| 0.7261 | 58.92 | 383 | 1.1630 | 0.6304 |
|
|
| 0.709 | 60.0 | 390 | 1.2157 | 0.5870 |
|
|
| 0.709 | 60.92 | 396 | 1.2124 | 0.6087 |
|
|
| 0.7075 | 62.0 | 403 | 1.2095 | 0.6087 |
|
|
| 0.7075 | 62.92 | 409 | 1.2543 | 0.5652 |
|
|
| 0.7141 | 64.0 | 416 | 1.2210 | 0.6087 |
|
|
| 0.6907 | 64.92 | 422 | 1.3190 | 0.5435 |
|
|
| 0.6907 | 66.0 | 429 | 1.2197 | 0.6522 |
|
|
| 0.7237 | 66.92 | 435 | 1.2365 | 0.5652 |
|
|
| 0.6918 | 68.0 | 442 | 1.1570 | 0.6304 |
|
|
| 0.6918 | 68.92 | 448 | 1.1790 | 0.6087 |
|
|
| 0.7137 | 70.0 | 455 | 1.1968 | 0.6087 |
|
|
| 0.6954 | 70.92 | 461 | 1.1959 | 0.6304 |
|
|
| 0.6954 | 72.0 | 468 | 1.1782 | 0.6304 |
|
|
| 0.6961 | 72.92 | 474 | 1.1935 | 0.5652 |
|
|
| 0.6889 | 73.85 | 480 | 1.1835 | 0.6087 |
|
|
|
|
|
|
### Framework versions
|
|
|
|
- Transformers 4.36.2
|
|
- Pytorch 2.1.2+cu118
|
|
- Datasets 2.16.1
|
|
- Tokenizers 0.15.0
|
|
|
