metadata
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8858613861386139
swinv2-tiny-patch4-window8-256-finetuned-eurosat
This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the food101 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3997
- Accuracy: 0.8859
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 |
|---|---|---|---|---|
| 1.8552 | 1.0 | 592 | 1.1245 | 0.6955 |
| 1.2938 | 2.0 | 1184 | 0.6712 | 0.8131 |
| 1.2294 | 3.0 | 1776 | 0.5354 | 0.8492 |
| 1.0199 | 4.0 | 2368 | 0.4958 | 0.8594 |
| 0.9914 | 5.0 | 2960 | 0.4633 | 0.8678 |
| 0.8786 | 6.0 | 3552 | 0.4390 | 0.8750 |
| 0.806 | 7.0 | 4144 | 0.4206 | 0.8791 |
| 0.7506 | 8.0 | 4736 | 0.4093 | 0.8832 |
| 0.7433 | 9.0 | 5328 | 0.4053 | 0.8841 |
| 0.6393 | 10.0 | 5920 | 0.3997 | 0.8859 |
Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3