metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: project_4_transfer_learning
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: 0.64375
project_4_transfer_learning
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.1429
- Accuracy: 0.6438
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 | Accuracy | Validation Loss |
|---|---|---|---|---|
| 2.0754 | 1.0 | 10 | 0.125 | 2.0725 |
| 2.0459 | 2.0 | 20 | 0.2625 | 2.0286 |
| 1.968 | 3.0 | 30 | 0.3 | 1.9506 |
| 1.8311 | 4.0 | 40 | 0.4188 | 1.8060 |
| 1.6911 | 5.0 | 50 | 0.4313 | 1.6814 |
| 1.5677 | 6.0 | 60 | 0.4313 | 1.5851 |
| 1.4801 | 7.0 | 70 | 0.4813 | 1.5169 |
| 1.4033 | 8.0 | 80 | 0.4813 | 1.4614 |
| 1.3435 | 9.0 | 90 | 0.475 | 1.4358 |
| 1.3054 | 10.0 | 100 | 0.525 | 1.4292 |
| 1.2532 | 11.0 | 110 | 0.5188 | 1.3942 |
| 1.2178 | 12.0 | 120 | 0.5312 | 1.3684 |
| 1.1857 | 13.0 | 130 | 0.5062 | 1.3599 |
| 1.1558 | 14.0 | 140 | 0.5312 | 1.2992 |
| 1.1118 | 15.0 | 150 | 0.5375 | 1.3217 |
| 1.0967 | 16.0 | 160 | 0.525 | 1.3177 |
| 1.0671 | 17.0 | 170 | 0.5312 | 1.3420 |
| 1.0635 | 18.0 | 180 | 0.5062 | 1.3319 |
| 1.044 | 19.0 | 190 | 0.5813 | 1.2977 |
| 1.037 | 20.0 | 200 | 0.5125 | 1.3127 |
| 1.0743 | 21.0 | 210 | 1.2062 | 0.6062 |
| 1.0454 | 22.0 | 220 | 1.1564 | 0.65 |
| 1.0457 | 23.0 | 230 | 1.1484 | 0.6312 |
| 1.0246 | 24.0 | 240 | 1.1470 | 0.6312 |
| 0.9859 | 25.0 | 250 | 1.1200 | 0.6438 |
| 0.9885 | 26.0 | 260 | 1.1331 | 0.6375 |
| 0.9823 | 27.0 | 270 | 1.1069 | 0.6562 |
| 0.9412 | 28.0 | 280 | 1.1163 | 0.6375 |
| 0.9172 | 29.0 | 290 | 1.1192 | 0.6375 |
| 0.9334 | 30.0 | 300 | 1.1573 | 0.6 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3