|
---
|
|
license: apache-2.0
|
|
base_model: google/vit-base-patch16-224-in21k
|
|
tags:
|
|
- generated_from_trainer
|
|
datasets:
|
|
- imagefolder
|
|
metrics:
|
|
- accuracy
|
|
- f1
|
|
- precision
|
|
- recall
|
|
model-index:
|
|
- name: vit-base-patch16-224-in21k-FINALLaneClassifier-VIT30epochsAUGMENTEDWITHTEST
|
|
results:
|
|
- task:
|
|
name: Image Classification
|
|
type: image-classification
|
|
dataset:
|
|
name: imagefolder
|
|
type: imagefolder
|
|
config: default
|
|
split: train
|
|
args: default
|
|
metrics:
|
|
- name: Accuracy
|
|
type: accuracy
|
|
value:
|
|
accuracy: 1.0
|
|
- name: F1
|
|
type: f1
|
|
value:
|
|
f1: 1.0
|
|
- name: Precision
|
|
type: precision
|
|
value:
|
|
precision: 1.0
|
|
- name: Recall
|
|
type: recall
|
|
value:
|
|
recall: 1.0
|
|
---
|
|
|
|
<!-- 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-base-patch16-224-in21k-FINALLaneClassifier-VIT30epochsAUGMENTEDWITHTEST
|
|
|
|
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
|
|
It achieves the following results on the evaluation set:
|
|
- Loss: 0.0000
|
|
- Accuracy: {'accuracy': 1.0}
|
|
- F1: {'f1': 1.0}
|
|
- Precision: {'precision': 1.0}
|
|
- Recall: {'recall': 1.0}
|
|
|
|
## 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: 32
|
|
- eval_batch_size: 32
|
|
- seed: 42
|
|
- gradient_accumulation_steps: 4
|
|
- total_train_batch_size: 128
|
|
- 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
|
|
|
|
### Training results
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|
|
|:-------------:|:-------:|:----:|:---------------:|:--------------------------------:|:--------------------------:|:---------------------------------:|:------------------------------:|
|
|
| 0.0229 | 0.9973 | 274 | 0.0166 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0083 | 1.9982 | 549 | 0.0062 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0055 | 2.9991 | 824 | 0.0032 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0025 | 4.0 | 1099 | 0.0019 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.004 | 4.9973 | 1373 | 0.0013 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.001 | 5.9982 | 1648 | 0.0009 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0032 | 6.9991 | 1923 | 0.0014 | {'accuracy': 0.9998862343572241} | {'f1': 0.9998861783406705} | {'precision': 0.9998887157801024} | {'recall': 0.9998836668217777} |
|
|
| 0.0011 | 8.0 | 2198 | 0.0005 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0035 | 8.9973 | 2472 | 0.0004 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0004 | 9.9982 | 2747 | 0.0003 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0003 | 10.9991 | 3022 | 0.0003 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0004 | 12.0 | 3297 | 0.0003 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0002 | 12.9973 | 3571 | 0.0002 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0005 | 13.9982 | 3846 | 0.0002 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.006 | 14.9991 | 4121 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0001 | 16.0 | 4396 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0001 | 16.9973 | 4670 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0001 | 17.9982 | 4945 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0004 | 18.9991 | 5220 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0001 | 20.0 | 5495 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0001 | 20.9973 | 5769 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0012 | 21.9982 | 6044 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0001 | 22.9991 | 6319 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0001 | 24.0 | 6594 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0001 | 24.9973 | 6868 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0002 | 25.9982 | 7143 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0001 | 26.9991 | 7418 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0001 | 28.0 | 7693 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0001 | 28.9973 | 7967 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
| 0.0001 | 29.9181 | 8220 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
|
|
|
|
|
|
### Framework versions
|
|
|
|
- Transformers 4.43.3
|
|
- Pytorch 2.3.1
|
|
- Datasets 2.20.0
|
|
- Tokenizers 0.19.1
|
|
|
