File size: 2,512 Bytes
fe3f66e 0bf809f fe3f66e 4353a35 fe3f66e 9d75e3a fe3f66e 4353a35 fe3f66e 0bf809f fe3f66e |
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 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: ViTFineTuned
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: KTH-TIPS2-b
type: images
args: default
metrics:
- name: Accuracy
type: accuracy
value: 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. -->
# ViTFineTuned
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 KTH-TIPS2-b dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0075
- Accuracy: 1.0
## Model description
Transfer learning by fine tuning the Vision Transformer by Google on KTP-TIP2-b dataset.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2859 | 0.99 | 67 | 0.2180 | 0.9784 |
| 0.293 | 1.99 | 134 | 0.3308 | 0.9185 |
| 0.1444 | 2.99 | 201 | 0.1532 | 0.9568 |
| 0.0833 | 3.99 | 268 | 0.0515 | 0.9856 |
| 0.1007 | 4.99 | 335 | 0.0295 | 0.9904 |
| 0.0372 | 5.99 | 402 | 0.0574 | 0.9808 |
| 0.0919 | 6.99 | 469 | 0.0537 | 0.9880 |
| 0.0135 | 7.99 | 536 | 0.0117 | 0.9952 |
| 0.0472 | 8.99 | 603 | 0.0075 | 1.0 |
| 0.0151 | 9.99 | 670 | 0.0048 | 1.0 |
| 0.0052 | 10.99 | 737 | 0.0073 | 0.9976 |
| 0.0109 | 11.99 | 804 | 0.0198 | 0.9952 |
| 0.0033 | 12.99 | 871 | 0.0066 | 0.9976 |
| 0.011 | 13.99 | 938 | 0.0067 | 0.9976 |
| 0.0032 | 14.99 | 1005 | 0.0060 | 0.9976 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|