File size: 3,455 Bytes
9436add c100af2 9436add fefaac6 c100af2 fefaac6 c100af2 fefaac6 9436add fefaac6 9436add c100af2 9436add c100af2 9436add c100af2 9436add c100af2 fefaac6 9436add c100af2 9436add c100af2 9436add |
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 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: beit-base-patch16-224
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8966666666666666
- name: Precision
type: precision
value: 0.891224605606628
- name: Recall
type: recall
value: 0.8966666666666666
---
<!-- 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. -->
# beit-base-patch16-224
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2426
- Accuracy: 0.8967
- Precision: 0.8912
- Recall: 0.8967
- F1 Score: 0.8935
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log | 1.0 | 4 | 0.4160 | 0.8667 | 0.8037 | 0.8667 | 0.8160 |
| No log | 2.0 | 8 | 0.4441 | 0.8375 | 0.7702 | 0.8375 | 0.7998 |
| No log | 3.0 | 12 | 0.4451 | 0.8667 | 0.8559 | 0.8667 | 0.8605 |
| 0.4959 | 4.0 | 16 | 0.3299 | 0.8792 | 0.8545 | 0.8792 | 0.8551 |
| 0.4959 | 5.0 | 20 | 0.3813 | 0.8458 | 0.8776 | 0.8458 | 0.8580 |
| 0.4959 | 6.0 | 24 | 0.2802 | 0.8958 | 0.8851 | 0.8958 | 0.8881 |
| 0.4959 | 7.0 | 28 | 0.2991 | 0.8875 | 0.8830 | 0.8875 | 0.8850 |
| 0.3696 | 8.0 | 32 | 0.2565 | 0.8917 | 0.8792 | 0.8917 | 0.8825 |
| 0.3696 | 9.0 | 36 | 0.2582 | 0.9 | 0.8949 | 0.9 | 0.8970 |
| 0.3696 | 10.0 | 40 | 0.2472 | 0.9 | 0.8927 | 0.9 | 0.8954 |
| 0.3696 | 11.0 | 44 | 0.2463 | 0.9208 | 0.9179 | 0.9208 | 0.9191 |
| 0.3299 | 12.0 | 48 | 0.2474 | 0.9167 | 0.9145 | 0.9167 | 0.9155 |
| 0.3299 | 13.0 | 52 | 0.2826 | 0.8833 | 0.8971 | 0.8833 | 0.8889 |
| 0.3299 | 14.0 | 56 | 0.2720 | 0.8958 | 0.9035 | 0.8958 | 0.8991 |
| 0.3036 | 15.0 | 60 | 0.2629 | 0.9 | 0.9059 | 0.9 | 0.9025 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|