|
--- |
|
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 |
|
|
