metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-new_dataset_50e
    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: 0.7972972972972973

swin-tiny-patch4-window7-224-finetuned-new_dataset_50e

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: 0.6407
Accuracy: 0.7973

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.94	4	0.7081	0.6081
No log	1.94	8	0.7104	0.6351
0.5516	2.94	12	0.6911	0.6351
0.5516	3.94	16	0.7156	0.7027
0.537	4.94	20	0.7345	0.7297
0.537	5.94	24	0.6745	0.6892
0.537	6.94	28	0.7146	0.7297
0.5333	7.94	32	0.7057	0.6892
0.5333	8.94	36	0.6531	0.7027
0.4871	9.94	40	0.6405	0.7027
0.4871	10.94	44	0.6126	0.6892
0.4871	11.94	48	0.6303	0.7027
0.4432	12.94	52	0.6264	0.7027
0.4432	13.94	56	0.6347	0.7432
0.3669	14.94	60	0.6698	0.6622
0.3669	15.94	64	0.6346	0.7568
0.3669	16.94	68	0.6510	0.6892
0.3704	17.94	72	0.6491	0.6892
0.3704	18.94	76	0.5947	0.7568
0.3624	19.94	80	0.6248	0.7027
0.3624	20.94	84	0.6580	0.7027
0.3624	21.94	88	0.6345	0.7162
0.3164	22.94	92	0.6092	0.7568
0.3164	23.94	96	0.6498	0.7162
0.2777	24.94	100	0.6915	0.7703
0.2777	25.94	104	0.6482	0.7838
0.2777	26.94	108	0.6407	0.7973
0.2946	27.94	112	0.6135	0.7838
0.2946	28.94	116	0.6819	0.7568
0.2546	29.94	120	0.6401	0.7568
0.2546	30.94	124	0.6370	0.7432
0.2546	31.94	128	0.6488	0.7703
0.2477	32.94	132	0.6429	0.7973
0.2477	33.94	136	0.6540	0.7703
0.1968	34.94	140	0.5895	0.7973
0.1968	35.94	144	0.6242	0.7568
0.1968	36.94	148	0.6575	0.7568
0.2235	37.94	152	0.6263	0.7703
0.2235	38.94	156	0.6225	0.7838
0.2005	39.94	160	0.6731	0.7703
0.2005	40.94	164	0.6844	0.7703
0.2005	41.94	168	0.6550	0.7703
0.2062	42.94	172	0.6700	0.7703
0.2062	43.94	176	0.6661	0.7703
0.1933	44.94	180	0.6606	0.7838
0.1933	45.94	184	0.6757	0.7703
0.1933	46.94	188	0.6889	0.7568
0.1895	47.94	192	0.6940	0.7568
0.1895	48.94	196	0.6919	0.7568
0.1666	49.94	200	0.6899	0.7432

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2