metadata
license: apache-2.0
tags:
- image-classification
- vision
- 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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9699248120300752
- task:
type: image-classification
name: Image Classification
dataset:
name: beans
type: beans
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.90625
verified: true
- name: Precision Macro
type: precision
value: 0.9070628768303187
verified: true
- name: Precision Micro
type: precision
value: 0.90625
verified: true
- name: Precision Weighted
type: precision
value: 0.9065184916020672
verified: true
- name: Recall Macro
type: recall
value: 0.9069767441860465
verified: true
- name: Recall Micro
type: recall
value: 0.90625
verified: true
- name: Recall Weighted
type: recall
value: 0.90625
verified: true
- name: F1 Macro
type: f1
value: 0.9064471790906943
verified: true
- name: F1 Micro
type: f1
value: 0.90625
verified: true
- name: F1 Weighted
type: f1
value: 0.9058082094420463
verified: true
- name: loss
type: loss
value: 0.37941932678222656
verified: true
vit-base-beans
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset. It achieves the following results on the evaluation set:
- Loss: 0.1824
- Accuracy: 0.9699
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: 2e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.672 | 1.0 | 44 | 0.5672 | 0.9398 |
0.411 | 2.0 | 88 | 0.3027 | 0.9699 |
0.2542 | 3.0 | 132 | 0.2078 | 0.9699 |
0.1886 | 4.0 | 176 | 0.1882 | 0.9699 |
0.1931 | 5.0 | 220 | 0.1824 | 0.9699 |
Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1