File size: 3,570 Bytes
7172d85 a153bdf 7172d85 a153bdf 7172d85 a153bdf 7172d85 |
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 99 100 101 102 103 104 105 106 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- bird species identification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification_obipix_birdID
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: private crawled images
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9719696025912545
---
<!-- 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. -->
# image_classification_obipix_birdID
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 private crawled images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1150
- Accuracy: 0.9720
## 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: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 6.9257 | 0.18 | 1000 | 5.3830 | 0.1638 |
| 3.9727 | 0.35 | 2000 | 2.7695 | 0.4797 |
| 2.057 | 0.53 | 3000 | 1.5070 | 0.6936 |
| 1.2103 | 0.7 | 4000 | 0.9727 | 0.7842 |
| 0.8513 | 0.88 | 5000 | 0.7101 | 0.8318 |
| 0.5836 | 1.06 | 6000 | 0.5797 | 0.8561 |
| 0.3545 | 1.23 | 7000 | 0.5066 | 0.8730 |
| 0.314 | 1.41 | 8000 | 0.4521 | 0.8818 |
| 0.2858 | 1.58 | 9000 | 0.3915 | 0.8960 |
| 0.2482 | 1.76 | 10000 | 0.3564 | 0.9056 |
| 0.2192 | 1.93 | 11000 | 0.3131 | 0.9148 |
| 0.1271 | 2.11 | 12000 | 0.2916 | 0.9207 |
| 0.0779 | 2.29 | 13000 | 0.2727 | 0.9260 |
| 0.0749 | 2.46 | 14000 | 0.2597 | 0.9309 |
| 0.0682 | 2.64 | 15000 | 0.2415 | 0.9355 |
| 0.0615 | 2.81 | 16000 | 0.2268 | 0.9385 |
| 0.0566 | 2.99 | 17000 | 0.2084 | 0.9440 |
| 0.0197 | 3.17 | 18000 | 0.1951 | 0.9475 |
| 0.0158 | 3.34 | 19000 | 0.1843 | 0.9513 |
| 0.0145 | 3.52 | 20000 | 0.1746 | 0.9541 |
| 0.0118 | 3.69 | 21000 | 0.1649 | 0.9573 |
| 0.0103 | 3.87 | 22000 | 0.1531 | 0.9599 |
| 0.006 | 4.05 | 23000 | 0.1379 | 0.9644 |
| 0.0016 | 4.22 | 24000 | 0.1316 | 0.9668 |
| 0.0013 | 4.4 | 25000 | 0.1265 | 0.9686 |
| 0.0014 | 4.57 | 26000 | 0.1232 | 0.9697 |
| 0.0009 | 4.75 | 27000 | 0.1189 | 0.9712 |
| 0.001 | 4.92 | 28000 | 0.1150 | 0.9720 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
|