File size: 2,934 Bytes
4d48bb4 cba4e03 4d48bb4 cba4e03 4d48bb4 cba4e03 4d48bb4 cba4e03 4d48bb4 |
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 |
---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
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: 0.808641975308642
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5712
- Accuracy: 0.8086
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.87 | 5 | 1.3767 | 0.5370 |
| 1.289 | 1.91 | 11 | 1.3503 | 0.5494 |
| 1.289 | 2.96 | 17 | 1.3712 | 0.5556 |
| 1.0376 | 4.0 | 23 | 1.3064 | 0.5556 |
| 1.0376 | 4.87 | 28 | 1.1062 | 0.5802 |
| 0.8346 | 5.91 | 34 | 0.9249 | 0.6481 |
| 0.7096 | 6.96 | 40 | 0.8947 | 0.6235 |
| 0.7096 | 8.0 | 46 | 0.8626 | 0.6543 |
| 0.6356 | 8.87 | 51 | 0.6820 | 0.7222 |
| 0.6356 | 9.91 | 57 | 0.7249 | 0.7346 |
| 0.5956 | 10.96 | 63 | 0.6818 | 0.7407 |
| 0.5956 | 12.0 | 69 | 0.6111 | 0.7840 |
| 0.5534 | 12.87 | 74 | 0.6026 | 0.7778 |
| 0.519 | 13.91 | 80 | 0.6070 | 0.7901 |
| 0.519 | 14.96 | 86 | 0.5758 | 0.7963 |
| 0.5117 | 16.0 | 92 | 0.5791 | 0.7840 |
| 0.5117 | 16.87 | 97 | 0.5711 | 0.8025 |
| 0.4913 | 17.39 | 100 | 0.5712 | 0.8086 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|