---
license: apache-2.0
base_model: microsoft/resnet-152
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Resnet152-5e-5
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7614314115308151
---

<!-- 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. -->

# Resnet152-5e-5

This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8255
- Accuracy: 0.7614

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.2352        | 1.0   | 275  | 2.9196          | 0.1984   |
| 2.5896        | 2.0   | 550  | 1.9631          | 0.4736   |
| 1.8864        | 3.0   | 825  | 1.3420          | 0.6231   |
| 1.5969        | 4.0   | 1100 | 1.1232          | 0.6918   |
| 1.465         | 5.0   | 1375 | 0.9717          | 0.7213   |
| 1.371         | 6.0   | 1650 | 0.9014          | 0.7483   |
| 1.2795        | 7.0   | 1925 | 0.8566          | 0.7491   |
| 1.2448        | 8.0   | 2200 | 0.8272          | 0.7594   |
| 1.2234        | 9.0   | 2475 | 0.8145          | 0.7630   |
| 1.2143        | 10.0  | 2750 | 0.8255          | 0.7614   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2