metadata

license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-plant-classification
    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: 0.7557471264367817

swin-tiny-patch4-window7-224-finetuned-plant-classification

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.6592
Accuracy: 0.7557

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8257	1.0	268	0.7941	0.6695
0.7235	2.0	537	0.7696	0.6695
0.6939	3.0	806	0.7428	0.6724
0.665	4.0	1075	0.6884	0.7328
0.6846	5.0	1343	0.7144	0.6954
0.6391	6.0	1612	0.6854	0.7155
0.6172	7.0	1881	0.6698	0.7011
0.6332	8.0	2150	0.6510	0.7126
0.5679	9.0	2418	0.6323	0.7299
0.5109	10.0	2687	0.6629	0.7098
0.5594	11.0	2956	0.6556	0.7270
0.4874	12.0	3225	0.6627	0.7155
0.4687	13.0	3493	0.6645	0.7299
0.4686	14.0	3762	0.6469	0.7213
0.4862	15.0	4031	0.6602	0.7356
0.4432	16.0	4300	0.6550	0.7270
0.4368	17.0	4568	0.6472	0.7385
0.3815	18.0	4837	0.6557	0.7557
0.3674	19.0	5106	0.6638	0.7529
0.4224	19.94	5360	0.6592	0.7557

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1