|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: google/vit-base-patch16-224 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- webdataset |
|
metrics: |
|
- accuracy |
|
- f1 |
|
- precision |
|
- recall |
|
model-index: |
|
- name: frost-vision-v2-google_vit-base-patch16-224-v2024-11-09 |
|
results: |
|
- task: |
|
name: Image Classification |
|
type: image-classification |
|
dataset: |
|
name: webdataset |
|
type: webdataset |
|
config: default |
|
split: train |
|
args: default |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9411971830985916 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8485947416137806 |
|
- name: Precision |
|
type: precision |
|
value: 0.8540145985401459 |
|
- name: Recall |
|
type: recall |
|
value: 0.8432432432432433 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# frost-vision-v2-google_vit-base-patch16-224-v2024-11-09 |
|
|
|
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the webdataset dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1716 |
|
- Accuracy: 0.9412 |
|
- F1: 0.8486 |
|
- Precision: 0.8540 |
|
- Recall: 0.8432 |
|
|
|
## 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: 0.0002 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- num_epochs: 30 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
|
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
|
| 0.2398 | 1.4085 | 100 | 0.2096 | 0.9215 | 0.7833 | 0.8502 | 0.7261 | |
|
| 0.1746 | 2.8169 | 200 | 0.1676 | 0.9370 | 0.8362 | 0.8494 | 0.8234 | |
|
| 0.1316 | 4.2254 | 300 | 0.1750 | 0.9282 | 0.8125 | 0.8293 | 0.7964 | |
|
| 0.1305 | 5.6338 | 400 | 0.1671 | 0.9342 | 0.8270 | 0.8498 | 0.8054 | |
|
| 0.1119 | 7.0423 | 500 | 0.1747 | 0.9317 | 0.8240 | 0.8300 | 0.8180 | |
|
| 0.0913 | 8.4507 | 600 | 0.1515 | 0.9415 | 0.8505 | 0.8505 | 0.8505 | |
|
| 0.0964 | 9.8592 | 700 | 0.1680 | 0.9377 | 0.8418 | 0.8351 | 0.8486 | |
|
| 0.0659 | 11.2676 | 800 | 0.1891 | 0.9275 | 0.8144 | 0.8144 | 0.8144 | |
|
| 0.0706 | 12.6761 | 900 | 0.1788 | 0.9320 | 0.8234 | 0.8364 | 0.8108 | |
|
| 0.069 | 14.0845 | 1000 | 0.1716 | 0.9412 | 0.8486 | 0.8540 | 0.8432 | |
|
| 0.0543 | 15.4930 | 1100 | 0.1847 | 0.9363 | 0.8341 | 0.8489 | 0.8198 | |
|
| 0.0515 | 16.9014 | 1200 | 0.1741 | 0.9408 | 0.8470 | 0.8564 | 0.8378 | |
|
| 0.0489 | 18.3099 | 1300 | 0.1793 | 0.9461 | 0.8620 | 0.8628 | 0.8613 | |
|
| 0.0339 | 19.7183 | 1400 | 0.1806 | 0.9444 | 0.8569 | 0.8616 | 0.8523 | |
|
| 0.0409 | 21.1268 | 1500 | 0.1784 | 0.9440 | 0.8569 | 0.8561 | 0.8577 | |
|
| 0.0275 | 22.5352 | 1600 | 0.1839 | 0.9437 | 0.8548 | 0.8611 | 0.8486 | |
|
| 0.0231 | 23.9437 | 1700 | 0.1865 | 0.9415 | 0.8480 | 0.8622 | 0.8342 | |
|
| 0.0204 | 25.3521 | 1800 | 0.1884 | 0.9405 | 0.8482 | 0.8459 | 0.8505 | |
|
| 0.0245 | 26.7606 | 1900 | 0.1935 | 0.9377 | 0.8410 | 0.8387 | 0.8432 | |
|
| 0.0202 | 28.1690 | 2000 | 0.1888 | 0.9394 | 0.8456 | 0.8426 | 0.8486 | |
|
| 0.0187 | 29.5775 | 2100 | 0.1914 | 0.9415 | 0.8502 | 0.8517 | 0.8486 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.5.0+cu121 |
|
- Datasets 3.1.0 |
|
- Tokenizers 0.19.1 |
|
|
