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---
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-ultralytics_yolo_cropped_lateral_flow_ivalidation
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: 0.948936170212766
---
<!-- 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. -->
# vit-msn-small-ultralytics_yolo_cropped_lateral_flow_ivalidation
This model is a fine-tuned version of [facebook/vit-msn-small](https://huggingface.co/facebook/vit-msn-small) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1774
- Accuracy: 0.9489
## 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: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 0.9231 | 3 | 0.8678 | 0.4277 |
| No log | 1.8462 | 6 | 0.6171 | 0.7 |
| No log | 2.7692 | 9 | 0.4174 | 0.8723 |
| 0.6518 | 4.0 | 13 | 0.5366 | 0.7106 |
| 0.6518 | 4.9231 | 16 | 0.3255 | 0.8851 |
| 0.6518 | 5.8462 | 19 | 0.6159 | 0.6809 |
| 0.4119 | 6.7692 | 22 | 0.3017 | 0.9191 |
| 0.4119 | 8.0 | 26 | 0.5130 | 0.7128 |
| 0.4119 | 8.9231 | 29 | 0.2183 | 0.9255 |
| 0.3387 | 9.8462 | 32 | 0.2523 | 0.9149 |
| 0.3387 | 10.7692 | 35 | 0.1774 | 0.9489 |
| 0.3387 | 12.0 | 39 | 0.2376 | 0.9255 |
| 0.3055 | 12.9231 | 42 | 0.3930 | 0.8383 |
| 0.3055 | 13.8462 | 45 | 0.2308 | 0.9234 |
| 0.3055 | 14.7692 | 48 | 0.1587 | 0.9468 |
| 0.2909 | 16.0 | 52 | 0.6113 | 0.6830 |
| 0.2909 | 16.9231 | 55 | 0.2910 | 0.8915 |
| 0.2909 | 17.8462 | 58 | 0.3612 | 0.8447 |
| 0.2227 | 18.7692 | 61 | 0.3117 | 0.8787 |
| 0.2227 | 20.0 | 65 | 0.2684 | 0.9170 |
| 0.2227 | 20.9231 | 68 | 0.3767 | 0.8404 |
| 0.2129 | 21.8462 | 71 | 0.2527 | 0.9234 |
| 0.2129 | 22.7692 | 74 | 0.3270 | 0.8745 |
| 0.2129 | 24.0 | 78 | 0.4314 | 0.8064 |
| 0.213 | 24.9231 | 81 | 0.2874 | 0.9 |
| 0.213 | 25.8462 | 84 | 0.4797 | 0.7894 |
| 0.213 | 26.7692 | 87 | 0.4896 | 0.7851 |
| 0.1758 | 28.0 | 91 | 0.3144 | 0.8723 |
| 0.1758 | 28.9231 | 94 | 0.5881 | 0.7213 |
| 0.1758 | 29.8462 | 97 | 0.5599 | 0.7298 |
| 0.1766 | 30.7692 | 100 | 0.3413 | 0.8702 |
| 0.1766 | 32.0 | 104 | 0.3453 | 0.8638 |
| 0.1766 | 32.9231 | 107 | 0.3634 | 0.8596 |
| 0.1583 | 33.8462 | 110 | 0.3799 | 0.8468 |
| 0.1583 | 34.7692 | 113 | 0.3840 | 0.8468 |
| 0.1583 | 36.0 | 117 | 0.3890 | 0.8447 |
| 0.1969 | 36.9231 | 120 | 0.3950 | 0.8426 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1
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