File size: 2,265 Bytes
4a9acf8 c2105b9 4a9acf8 c2105b9 4a9acf8 c2105b9 4a9acf8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9924812030075187
---
<!-- 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-beans
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0226
- Accuracy: 0.9925
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3365 | 0.38 | 50 | 0.2455 | 0.9323 |
| 0.1728 | 0.77 | 100 | 0.1544 | 0.9549 |
| 0.1519 | 1.15 | 150 | 0.1072 | 0.9624 |
| 0.0209 | 1.54 | 200 | 0.1594 | 0.9624 |
| 0.0206 | 1.92 | 250 | 0.0913 | 0.9699 |
| 0.0135 | 2.31 | 300 | 0.1488 | 0.9624 |
| 0.0079 | 2.69 | 350 | 0.0226 | 0.9925 |
| 0.0074 | 3.08 | 400 | 0.0582 | 0.9925 |
| 0.0064 | 3.46 | 450 | 0.0984 | 0.9774 |
| 0.0061 | 3.85 | 500 | 0.1151 | 0.9699 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|