---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: microsoft-resnet-50-cartoon-emotion-detection
  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: 0.6697247706422018
    - name: Precision
      type: precision
      value: 0.5798801171844885
    - name: Recall
      type: recall
      value: 0.6697247706422018
    - name: F1
      type: f1
      value: 0.6086361803243947
---

<!-- 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. -->

# microsoft-resnet-50-cartoon-emotion-detection

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0059
- Accuracy: 0.6697
- Precision: 0.5799
- Recall: 0.6697
- F1: 0.6086

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.97  | 8    | 1.3833          | 0.2477   | 0.2054    | 0.2477 | 0.2042 |
| 1.4276        | 1.97  | 16   | 1.3711          | 0.3028   | 0.1982    | 0.3028 | 0.1932 |
| 1.4046        | 2.97  | 24   | 1.3550          | 0.3028   | 0.0917    | 0.3028 | 0.1407 |
| 1.3817        | 3.97  | 32   | 1.3375          | 0.3119   | 0.2852    | 0.3119 | 0.1592 |
| 1.3562        | 4.97  | 40   | 1.3179          | 0.3211   | 0.4337    | 0.3211 | 0.1785 |
| 1.3562        | 5.97  | 48   | 1.2991          | 0.3761   | 0.5442    | 0.3761 | 0.2741 |
| 1.3624        | 6.97  | 56   | 1.2751          | 0.4495   | 0.5593    | 0.4495 | 0.3659 |
| 1.2914        | 7.97  | 64   | 1.2494          | 0.4771   | 0.5442    | 0.4771 | 0.4094 |
| 1.2518        | 8.97  | 72   | 1.2279          | 0.5046   | 0.5525    | 0.5046 | 0.4430 |
| 1.2085        | 9.97  | 80   | 1.1905          | 0.5321   | 0.5134    | 0.5321 | 0.4579 |
| 1.2085        | 10.97 | 88   | 1.1602          | 0.5505   | 0.5151    | 0.5505 | 0.4872 |
| 1.1865        | 11.97 | 96   | 1.1307          | 0.5963   | 0.5969    | 0.5963 | 0.5416 |
| 1.122         | 12.97 | 104  | 1.1037          | 0.5872   | 0.5069    | 0.5872 | 0.5206 |
| 1.0812        | 13.97 | 112  | 1.0797          | 0.5688   | 0.4868    | 0.5688 | 0.5068 |
| 1.0449        | 14.97 | 120  | 1.0712          | 0.6239   | 0.5269    | 0.6239 | 0.5641 |
| 1.0449        | 15.97 | 128  | 1.0425          | 0.6239   | 0.5123    | 0.6239 | 0.5517 |
| 1.0458        | 16.97 | 136  | 1.0346          | 0.6239   | 0.6487    | 0.6239 | 0.5782 |
| 1.004         | 17.97 | 144  | 1.0264          | 0.6330   | 0.5472    | 0.6330 | 0.5721 |
| 0.9806        | 18.97 | 152  | 1.0041          | 0.6606   | 0.6334    | 0.6606 | 0.6069 |
| 0.97          | 19.97 | 160  | 1.0059          | 0.6697   | 0.5799    | 0.6697 | 0.6086 |


### Framework versions

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