---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: resnet-152-fv-finetuned-memess
  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: 0.767387944358578
    - name: Precision
      type: precision
      value: 0.7651125602674349
    - name: Recall
      type: recall
      value: 0.767387944358578
    - name: F1
      type: f1
      value: 0.7646848616766787
---

<!-- 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-152-fv-finetuned-memess

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: 0.6281
- Accuracy: 0.7674
- Precision: 0.7651
- Recall: 0.7674
- F1: 0.7647

## 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: 0.00012
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.5902        | 0.99  | 20   | 1.5519          | 0.4938   | 0.3491    | 0.4938 | 0.3529 |
| 1.4694        | 1.99  | 40   | 1.3730          | 0.4892   | 0.4095    | 0.4892 | 0.3222 |
| 1.3129        | 2.99  | 60   | 1.2052          | 0.5301   | 0.3504    | 0.5301 | 0.4005 |
| 1.1831        | 3.99  | 80   | 1.1142          | 0.5587   | 0.4077    | 0.5587 | 0.4444 |
| 1.0581        | 4.99  | 100  | 0.9930          | 0.6012   | 0.5680    | 0.6012 | 0.5108 |
| 0.9464        | 5.99  | 120  | 0.9263          | 0.6507   | 0.6200    | 0.6507 | 0.6029 |
| 0.8581        | 6.99  | 140  | 0.8400          | 0.6917   | 0.6645    | 0.6917 | 0.6638 |
| 0.7739        | 7.99  | 160  | 0.7829          | 0.7087   | 0.6918    | 0.7087 | 0.6845 |
| 0.6762        | 8.99  | 180  | 0.7512          | 0.7318   | 0.7206    | 0.7318 | 0.7189 |
| 0.6162        | 9.99  | 200  | 0.7409          | 0.7264   | 0.7244    | 0.7264 | 0.7241 |
| 0.5546        | 10.99 | 220  | 0.6936          | 0.7465   | 0.7429    | 0.7465 | 0.7395 |
| 0.4633        | 11.99 | 240  | 0.6779          | 0.7473   | 0.7393    | 0.7473 | 0.7412 |
| 0.4373        | 12.99 | 260  | 0.6736          | 0.7573   | 0.7492    | 0.7573 | 0.7523 |
| 0.4074        | 13.99 | 280  | 0.6534          | 0.7566   | 0.7516    | 0.7566 | 0.7528 |
| 0.39          | 14.99 | 300  | 0.6521          | 0.7651   | 0.7603    | 0.7651 | 0.7608 |
| 0.3766        | 15.99 | 320  | 0.6499          | 0.7682   | 0.7607    | 0.7682 | 0.7630 |
| 0.3507        | 16.99 | 340  | 0.6497          | 0.7697   | 0.7686    | 0.7697 | 0.7686 |
| 0.3589        | 17.99 | 360  | 0.6519          | 0.7535   | 0.7485    | 0.7535 | 0.7502 |
| 0.3261        | 18.99 | 380  | 0.6449          | 0.7589   | 0.7597    | 0.7589 | 0.7585 |
| 0.3234        | 19.99 | 400  | 0.6281          | 0.7674   | 0.7651    | 0.7674 | 0.7647 |


### Framework versions

- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1.dev0
- Tokenizers 0.13.1