metadata
license: apache-2.0
base_model: >-
weightbot/swin-tiny-patch4-window7-224-finetuned-plant-classification-finetuned-crops-classification
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: >-
swin-tiny-patch4-window7-224-finetuned-plant-classification-finetuned-crops-classification-ft
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8773946360153256
swin-tiny-patch4-window7-224-finetuned-plant-classification-finetuned-crops-classification-ft
This model is a fine-tuned version of weightbot/swin-tiny-patch4-window7-224-finetuned-plant-classification-finetuned-crops-classification on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3404
- Accuracy: 0.8774
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4665 | 1.0 | 201 | 0.3881 | 0.8352 |
| 0.4054 | 2.0 | 403 | 0.3799 | 0.8582 |
| 0.3735 | 2.99 | 603 | 0.3404 | 0.8774 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1