---
base_model: juliensimon/autotrain-chest-xray-demo-1677859324
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Text2Image_PyData_23
  results: []
---

<!-- 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. -->

# Text2Image_PyData_23

This model is a fine-tuned version of [juliensimon/autotrain-chest-xray-demo-1677859324](https://huggingface.co/juliensimon/autotrain-chest-xray-demo-1677859324) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3421
- Accuracy: 0.8333
- F1: [0.71584699 0.88208617]
- Precision: [0.99242424 0.79065041]
- Recall: [0.55982906 0.9974359 ]

## 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: 16
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1                      | Precision               | Recall                  |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------------:|:-----------------------:|:-----------------------:|
| 0.0451        | 0.98  | 40   | 0.6974          | 0.7933   | [0.62170088 0.85777288] | [0.99065421 0.75241779] | [0.45299145 0.9974359 ] |
| 0.036         | 1.99  | 81   | 0.3557          | 0.8958   | [0.84107579 0.92252682] | [0.98285714 0.86191537] | [0.73504274 0.99230769] |
| 0.043         | 2.99  | 122  | 0.4253          | 0.9006   | [0.84803922 0.92619048] | [0.99425287 0.86444444] | [0.73931624 0.9974359 ] |
| 0.0225        | 4.0   | 163  | 0.8776          | 0.8349   | [0.71934605 0.8830874 ] | [0.9924812  0.79226069] | [0.56410256 0.9974359 ] |
| 0.0153        | 4.98  | 203  | 0.7095          | 0.8670   | [0.78552972 0.90360046] | [0.99346405 0.82590234] | [0.64957265 0.9974359 ] |
| 0.0107        | 5.99  | 244  | 0.8537          | 0.8446   | [0.73994638 0.88914286] | [0.99280576 0.80206186] | [0.58974359 0.9974359 ] |
| 0.0052        | 6.99  | 285  | 1.0167          | 0.8462   | [0.74331551 0.89016018] | [0.99285714 0.80371901] | [0.59401709 0.9974359 ] |
| 0.0049        | 8.0   | 326  | 1.3230          | 0.8045   | [0.64942529 0.86444444] | [0.99122807 0.7627451 ] | [0.48290598 0.9974359 ] |
| 0.0061        | 8.98  | 366  | 1.2652          | 0.8269   | [0.70165746 0.87810384] | [0.9921875  0.78427419] | [0.54273504 0.9974359 ] |
| 0.004         | 9.99  | 407  | 1.4846          | 0.8157   | [0.67605634 0.8712206 ] | [0.99173554 0.77335984] | [0.51282051 0.9974359 ] |
| 0.0005        | 10.99 | 448  | 1.5685          | 0.8109   | [0.66477273 0.86830357] | [0.99152542 0.7687747 ] | [0.5       0.9974359]   |
| 0.0029        | 12.0  | 489  | 1.2547          | 0.8397   | [0.72972973 0.88610478] | [0.99264706 0.79713115] | [0.57692308 0.9974359 ] |
| 0.0015        | 12.98 | 529  | 1.4026          | 0.8285   | [0.70523416 0.87909605] | [0.99224806 0.78585859] | [0.54700855 0.9974359 ] |
| 0.0012        | 13.99 | 570  | 1.4444          | 0.8237   | [0.69444444 0.87612613] | [0.99206349 0.7811245 ] | [0.53418803 0.9974359 ] |
| 0.0039        | 14.72 | 600  | 1.3421          | 0.8333   | [0.71584699 0.88208617] | [0.99242424 0.79065041] | [0.55982906 0.9974359 ] |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1