metadata
license: apache-2.0
base_model: DouglasBraga/swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-3000
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: >-
swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-3000-finetuned-leukemia-1000
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: 1
swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-3000-finetuned-leukemia-1000
This model is a fine-tuned version of DouglasBraga/swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-3000 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0056
- 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: 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5471 | 0.9825 | 14 | 0.1240 | 0.955 |
| 0.1792 | 1.9649 | 28 | 0.0493 | 0.985 |
| 0.0936 | 2.9474 | 42 | 0.1210 | 0.965 |
| 0.0907 | 4.0 | 57 | 0.0056 | 1.0 |
| 0.0441 | 4.9825 | 71 | 0.0165 | 0.995 |
| 0.0341 | 5.9649 | 85 | 0.0059 | 0.995 |
| 0.0406 | 6.9474 | 99 | 0.0018 | 1.0 |
| 0.013 | 8.0 | 114 | 0.0200 | 0.995 |
| 0.0342 | 8.9825 | 128 | 0.0030 | 1.0 |
| 0.0246 | 9.8246 | 140 | 0.0026 | 1.0 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cpu
- Datasets 2.19.0
- Tokenizers 0.15.2