File size: 2,931 Bytes
5d24fcb fc42fd8 5d24fcb fc42fd8 5d24fcb fc42fd8 5d24fcb fc42fd8 5d24fcb fc42fd8 5d24fcb |
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 87 88 |
---
tags:
- generated_from_trainer
datasets:
- preprocessed1024_config
metrics:
- accuracy
- f1
model-index:
- name: vit-model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: preprocessed1024_config
type: preprocessed1024_config
args: default
metrics:
- name: Accuracy
type: accuracy
value:
accuracy: 0.6011306532663316
- name: F1
type: f1
value:
f1: 0.5956396413406886
---
<!-- 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-model
This model is a fine-tuned version of [](https://huggingface.co/) on the preprocessed1024_config dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1353
- Accuracy: {'accuracy': 0.6011306532663316}
- F1: {'f1': 0.5956396413406886}
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:---------------------------:|
| 1.224 | 1.0 | 796 | 0.9884 | {'accuracy': 0.5276381909547738} | {'f1': 0.40344173017767304} |
| 0.96 | 2.0 | 1592 | 0.9255 | {'accuracy': 0.5621859296482412} | {'f1': 0.5134011716404221} |
| 0.8878 | 3.0 | 2388 | 0.9308 | {'accuracy': 0.574748743718593} | {'f1': 0.46867195041352344} |
| 0.809 | 4.0 | 3184 | 0.8904 | {'accuracy': 0.6067839195979899} | {'f1': 0.5799288651427482} |
| 0.7541 | 5.0 | 3980 | 0.8936 | {'accuracy': 0.5954773869346733} | {'f1': 0.5938876317530138} |
| 0.6904 | 6.0 | 4776 | 0.8760 | {'accuracy': 0.6118090452261307} | {'f1': 0.6023012293668115} |
| 0.6195 | 7.0 | 5572 | 1.0032 | {'accuracy': 0.5917085427135679} | {'f1': 0.5834559014249068} |
| 0.5766 | 8.0 | 6368 | 1.0268 | {'accuracy': 0.6023869346733668} | {'f1': 0.5779800559497847} |
| 0.4963 | 9.0 | 7164 | 1.0460 | {'accuracy': 0.5992462311557789} | {'f1': 0.5875334711293277} |
| 0.4323 | 10.0 | 7960 | 1.1353 | {'accuracy': 0.6011306532663316} | {'f1': 0.5956396413406886} |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|