metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: Action_all_10_class
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: Action_small_dataset
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8517382413087935

Action_all_10_class

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the Action_small_dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.4725
Accuracy: 0.8517

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: 0.0001
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: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.2411	0.36	100	1.1517	0.7546
0.8932	0.72	200	0.7856	0.7975
0.6907	1.08	300	0.6636	0.8221
0.5841	1.43	400	0.6388	0.8160
0.5425	1.79	500	0.5871	0.8436
0.5929	2.15	600	0.5646	0.8211
0.4406	2.51	700	0.5439	0.8405
0.4541	2.87	800	0.5318	0.8415
0.3835	3.23	900	0.5225	0.8344
0.3924	3.58	1000	0.5515	0.8303
0.5741	3.94	1100	0.5519	0.8252
0.3991	4.3	1200	0.4990	0.8446
0.4732	4.66	1300	0.5336	0.8303
0.3324	5.02	1400	0.5351	0.8282
0.3433	5.38	1500	0.4725	0.8517
0.2187	5.73	1600	0.5042	0.8466
0.2952	6.09	1700	0.5240	0.8548
0.2687	6.45	1800	0.5523	0.8364
0.3111	6.81	1900	0.5304	0.8497
0.2431	7.17	2000	0.5104	0.8569
0.3265	7.53	2100	0.5085	0.8691
0.2595	7.89	2200	0.5015	0.8569
0.1825	8.24	2300	0.4920	0.8620
0.2602	8.6	2400	0.5016	0.8620
0.2628	8.96	2500	0.4746	0.8681
0.1024	9.32	2600	0.4818	0.8691
0.1468	9.68	2700	0.4765	0.8681

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2