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-herbify
    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: 1

swin-tiny-patch4-window7-224-finetuned-herbify

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: 0.0378
Accuracy: 1.0

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: 35

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.94	4	1.8723	0.2787
No log	1.88	8	1.5899	0.6885
1.8465	2.82	12	1.1661	0.8197
1.8465	4.0	17	0.5156	0.9508
0.9675	4.94	21	0.2177	0.9836
0.9675	5.88	25	0.0929	0.9836
0.9675	6.82	29	0.0378	1.0
0.2342	8.0	34	0.0128	1.0
0.2342	8.94	38	0.0075	1.0
0.1022	9.88	42	0.0053	1.0
0.1022	10.82	46	0.0049	1.0
0.0553	12.0	51	0.0032	1.0
0.0553	12.94	55	0.0022	1.0
0.0553	13.88	59	0.0017	1.0
0.0278	14.82	63	0.0018	1.0
0.0278	16.0	68	0.0012	1.0
0.0266	16.94	72	0.0011	1.0
0.0266	17.88	76	0.0006	1.0
0.046	18.82	80	0.0007	1.0
0.046	20.0	85	0.0007	1.0
0.046	20.94	89	0.0012	1.0
0.0245	21.88	93	0.0015	1.0
0.0245	22.82	97	0.0011	1.0
0.0249	24.0	102	0.0007	1.0
0.0249	24.94	106	0.0006	1.0
0.0201	25.88	110	0.0005	1.0
0.0201	26.82	114	0.0005	1.0
0.0201	28.0	119	0.0004	1.0
0.0208	28.94	123	0.0004	1.0
0.0208	29.88	127	0.0004	1.0
0.0122	30.82	131	0.0004	1.0
0.0122	32.0	136	0.0004	1.0
0.0222	32.94	140	0.0004	1.0

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cpu
Datasets 2.14.5
Tokenizers 0.13.3