metadata
library_name: transformers
license: apache-2.0
base_model: facebook/vit-msn-small
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-msn-small-lateral_flow_ivalidation_green_test
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8810408921933085
vit-msn-small-lateral_flow_ivalidation_green_test
This model is a fine-tuned version of facebook/vit-msn-small on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2823
- Accuracy: 0.8810
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.9231 | 6 | 0.4106 | 0.7918 |
| 0.5328 | 2.0 | 13 | 0.2823 | 0.8810 |
| 0.5328 | 2.9231 | 19 | 0.3051 | 0.8587 |
| 0.4244 | 4.0 | 26 | 0.2913 | 0.8996 |
| 0.3755 | 4.9231 | 32 | 0.2841 | 0.9052 |
| 0.3755 | 6.0 | 39 | 0.3204 | 0.8829 |
| 0.3569 | 6.9231 | 45 | 0.2982 | 0.8810 |
| 0.3157 | 8.0 | 52 | 0.3317 | 0.8643 |
| 0.3157 | 8.9231 | 58 | 0.5731 | 0.7249 |
| 0.3177 | 9.2308 | 60 | 0.5725 | 0.7305 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1