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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit_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: 0.9296875
verified: true
- name: Precision Macro
type: precision
value: 0.9320220841959972
verified: true
- name: Precision Micro
type: precision
value: 0.9296875
verified: true
- name: Precision Weighted
type: precision
value: 0.9314910067287785
verified: true
- name: Recall Macro
type: recall
value: 0.9298634182355112
verified: true
- name: Recall Micro
type: recall
value: 0.9296875
verified: true
- name: Recall Weighted
type: recall
value: 0.9296875
verified: true
- name: F1 Macro
type: f1
value: 0.9303585725877088
verified: true
- name: F1 Micro
type: f1
value: 0.9296875
verified: true
- name: F1 Weighted
type: f1
value: 0.930005047716538
verified: true
- name: loss
type: loss
value: 0.23633646965026855
verified: true
---
<!-- 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_beans
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1176
- 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.2
- Datasets 2.0.0
- Tokenizers 0.10.3
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