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---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: msi-swinv2-tiny
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: 0.9253901789113057
- name: F1
type: f1
value: 0.9052377115229654
- name: Precision
type: precision
value: 0.9233171693926194
- name: Recall
type: recall
value: 0.8878526831581444
---
<!-- 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. -->
# msi-swinv2-tiny
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1768
- Accuracy: 0.9254
- F1: 0.9052
- Precision: 0.9233
- Recall: 0.8879
## 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: 1e-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: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3786 | 1.0 | 1970 | 0.3166 | 0.8590 | 0.8184 | 0.8469 | 0.7917 |
| 0.2976 | 2.0 | 3941 | 0.2426 | 0.8952 | 0.8621 | 0.9138 | 0.8159 |
| 0.2525 | 3.0 | 5911 | 0.2015 | 0.9144 | 0.8908 | 0.9132 | 0.8694 |
| 0.2319 | 4.0 | 7882 | 0.1859 | 0.9216 | 0.9026 | 0.8996 | 0.9056 |
| 0.206 | 5.0 | 9850 | 0.1768 | 0.9254 | 0.9052 | 0.9233 | 0.8879 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|