metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: resnet-50-finetuned-omars3
    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: 0.7435897435897436

resnet-50-finetuned-omars3

This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.7345
Accuracy: 0.7436

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: 0.0005
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.3928	1.0	11	1.3871	0.2308
1.3864	2.0	22	1.3820	0.3077
1.3694	3.0	33	1.3510	0.5385
1.3513	4.0	44	1.2942	0.4872
1.3067	5.0	55	1.1984	0.6154
1.2184	6.0	66	0.9974	0.6923
1.0967	7.0	77	0.7869	0.6667
0.9731	8.0	88	0.7923	0.7436
0.9506	9.0	99	0.7161	0.6667
0.7783	10.0	110	0.6736	0.6923
0.7072	11.0	121	0.6693	0.7436
0.6669	12.0	132	0.7203	0.6923
0.6579	13.0	143	0.6195	0.7949
0.6695	14.0	154	0.6395	0.7692
0.678	15.0	165	0.6870	0.7692
0.5919	16.0	176	0.6681	0.7692
0.5459	17.0	187	0.6895	0.7692
0.5635	18.0	198	0.6617	0.7692
0.5378	19.0	209	0.6401	0.7949
0.5105	20.0	220	0.7108	0.7692
0.4656	21.0	231	0.7267	0.7692
0.5338	22.0	242	0.7531	0.7436
0.4846	23.0	253	0.7103	0.7179
0.4212	24.0	264	0.7809	0.7436
0.4677	25.0	275	0.7825	0.7692
0.4496	26.0	286	0.8240	0.6923
0.3784	27.0	297	0.7563	0.7179
0.4949	28.0	308	0.6823	0.7692
0.4612	29.0	319	0.7542	0.6667
0.4491	30.0	330	0.7345	0.7436

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.0
Tokenizers 0.13.3