File size: 3,722 Bytes
6dcb75b ff97657 6dcb75b ff97657 6dcb75b ff97657 6dcb75b ff97657 6dcb75b ff97657 6dcb75b ff97657 6dcb75b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-eurosat-50
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: Augmented
split: train
args: Augmented
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
<!-- 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. -->
# swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-eurosat-50
This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0004
- Accuracy: 1.0
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5952 | 1.0 | 55 | 0.8490 | 0.6693 |
| 0.7582 | 2.0 | 110 | 0.4561 | 0.8386 |
| 0.4359 | 3.0 | 165 | 0.2408 | 0.9227 |
| 0.318 | 4.0 | 220 | 0.1294 | 0.9568 |
| 0.2414 | 5.0 | 275 | 0.0346 | 0.9909 |
| 0.1888 | 6.0 | 330 | 0.0419 | 0.9864 |
| 0.1717 | 7.0 | 385 | 0.0238 | 0.9943 |
| 0.1785 | 8.0 | 440 | 0.0230 | 0.9943 |
| 0.1654 | 9.0 | 495 | 0.0076 | 1.0 |
| 0.1322 | 10.0 | 550 | 0.0046 | 1.0 |
| 0.1123 | 11.0 | 605 | 0.0035 | 1.0 |
| 0.0953 | 12.0 | 660 | 0.0025 | 1.0 |
| 0.0864 | 13.0 | 715 | 0.0033 | 1.0 |
| 0.0984 | 14.0 | 770 | 0.0033 | 0.9989 |
| 0.0952 | 15.0 | 825 | 0.0015 | 1.0 |
| 0.0678 | 16.0 | 880 | 0.0022 | 1.0 |
| 0.0592 | 17.0 | 935 | 0.0013 | 1.0 |
| 0.0729 | 18.0 | 990 | 0.0037 | 0.9989 |
| 0.0672 | 19.0 | 1045 | 0.0041 | 0.9989 |
| 0.0615 | 20.0 | 1100 | 0.0010 | 1.0 |
| 0.058 | 21.0 | 1155 | 0.0009 | 1.0 |
| 0.0571 | 22.0 | 1210 | 0.0021 | 0.9989 |
| 0.0755 | 23.0 | 1265 | 0.0022 | 0.9989 |
| 0.0688 | 24.0 | 1320 | 0.0025 | 0.9989 |
| 0.0417 | 25.0 | 1375 | 0.0003 | 1.0 |
| 0.0589 | 26.0 | 1430 | 0.0007 | 1.0 |
| 0.0563 | 27.0 | 1485 | 0.0007 | 1.0 |
| 0.0603 | 28.0 | 1540 | 0.0010 | 0.9989 |
| 0.0469 | 29.0 | 1595 | 0.0005 | 1.0 |
| 0.0525 | 30.0 | 1650 | 0.0004 | 1.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|