---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# resnet-50-finetuned-oxfordflowers

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/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