metadata

library_name: transformers
license: apache-2.0
base_model: microsoft/resnet-50
tags:
  - generated_from_trainer
datasets:
  - oxford102_flower_dataset
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: resnet-50-finetuned-oxfordflowers
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: oxford102_flower_dataset
          type: oxford102_flower_dataset
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8329809725158562
          - name: Precision
            type: precision
            value: 0.8530722962152707
          - name: Recall
            type: recall
            value: 0.8329809725158562
          - name: F1
            type: f1
            value: 0.8319188207666911

resnet-50-finetuned-oxfordflowers

This model is a fine-tuned version of microsoft/resnet-50 on the oxford102_flower_dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.6561
Accuracy: 0.8330
Precision: 0.8531
Recall: 0.8330
F1: 0.8319

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: 0.001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
4.4813	1.0	32	4.1934	0.3176	0.3522	0.3176	0.2599
2.6507	2.0	64	1.8716	0.5382	0.5792	0.5382	0.4930
1.257	3.0	96	1.0998	0.7216	0.7663	0.7216	0.7085
0.5333	4.0	128	0.9724	0.7422	0.7875	0.7422	0.7296
0.2506	5.0	160	0.8243	0.7627	0.7975	0.7627	0.7566
0.0689	6.0	192	0.7067	0.8147	0.8482	0.8147	0.8111
0.0325	7.0	224	0.6370	0.8206	0.8428	0.8206	0.8175
0.0132	8.0	256	0.5774	0.8412	0.8617	0.8412	0.8389
0.0117	9.0	288	0.5469	0.8559	0.8726	0.8559	0.8542
0.0066	10.0	320	0.5384	0.8608	0.8722	0.8608	0.8575
0.0072	11.0	352	0.5246	0.8686	0.8783	0.8686	0.8650
0.0068	12.0	384	0.5130	0.8716	0.8790	0.8716	0.8679
0.0045	13.0	416	0.5038	0.8716	0.8814	0.8716	0.8691
0.0025	14.0	448	0.5486	0.85	0.8627	0.85	0.8448
0.0029	15.0	480	0.4992	0.8637	0.8736	0.8637	0.8619

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0