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---
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
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