---
license: apache-2.0
base_model: microsoft/resnet-18
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Action_agent_small_34_class
  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: 0.09523809523809523
---

<!-- 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. -->

# Action_agent_small_34_class

This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Accuracy: 0.0952

## 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: 1e-05
- train_batch_size: 32
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0           | 0.32  | 100  | nan             | 0.0952   |
| 0.0           | 0.64  | 200  | nan             | 0.0952   |
| 0.0           | 0.96  | 300  | nan             | 0.0952   |
| 0.0           | 1.27  | 400  | nan             | 0.0952   |
| 0.0           | 1.59  | 500  | nan             | 0.0952   |
| 0.0           | 1.91  | 600  | nan             | 0.0952   |
| 0.0           | 2.23  | 700  | nan             | 0.0952   |
| 0.0           | 2.55  | 800  | nan             | 0.0952   |
| 0.0           | 2.87  | 900  | nan             | 0.0952   |
| 0.0           | 3.18  | 1000 | nan             | 0.0952   |
| 0.0           | 3.5   | 1100 | nan             | 0.0952   |
| 0.0           | 3.82  | 1200 | nan             | 0.0952   |
| 0.0           | 4.14  | 1300 | nan             | 0.0952   |
| 0.0           | 4.46  | 1400 | nan             | 0.0952   |
| 0.0           | 4.78  | 1500 | nan             | 0.0952   |
| 0.0           | 5.1   | 1600 | nan             | 0.0952   |
| 0.0           | 5.41  | 1700 | nan             | 0.0952   |
| 0.0           | 5.73  | 1800 | nan             | 0.0952   |
| 0.0           | 6.05  | 1900 | nan             | 0.0952   |
| 0.0           | 6.37  | 2000 | nan             | 0.0952   |
| 0.0           | 6.69  | 2100 | nan             | 0.0952   |
| 0.0           | 7.01  | 2200 | nan             | 0.0952   |
| 0.0           | 7.32  | 2300 | nan             | 0.0952   |
| 0.0           | 7.64  | 2400 | nan             | 0.0952   |
| 0.0           | 7.96  | 2500 | nan             | 0.0952   |
| 0.0           | 8.28  | 2600 | nan             | 0.0952   |
| 0.0           | 8.6   | 2700 | nan             | 0.0952   |
| 0.0           | 8.92  | 2800 | nan             | 0.0952   |
| 0.0           | 9.24  | 2900 | nan             | 0.0952   |
| 0.0           | 9.55  | 3000 | nan             | 0.0952   |
| 0.0           | 9.87  | 3100 | nan             | 0.0952   |


### Framework versions

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