---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: unet-efficientnet-b2-malaria-trophozoites-wbc
  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. -->

# unet-efficientnet-b2-malaria-trophozoites-wbc

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1470

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 90

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log        | 1.0   | 484   | 0.8717          |
| 10.9992       | 2.0   | 968   | 0.1936          |
| 0.2866        | 3.0   | 1452  | 0.1798          |
| 0.1569        | 4.0   | 1936  | 0.1670          |
| 0.1414        | 5.0   | 2420  | 0.1650          |
| 0.1392        | 6.0   | 2904  | 0.1629          |
| 0.1342        | 7.0   | 3388  | 0.1626          |
| 0.1326        | 8.0   | 3872  | 0.1569          |
| 0.1332        | 9.0   | 4356  | 0.1577          |
| 0.1323        | 10.0  | 4840  | 0.1609          |
| 0.1295        | 11.0  | 5324  | 0.1549          |
| 0.127         | 12.0  | 5808  | 0.1552          |
| 0.1277        | 13.0  | 6292  | 0.1560          |
| 0.126         | 14.0  | 6776  | 0.1550          |
| 0.1267        | 15.0  | 7260  | 0.1524          |
| 0.1261        | 16.0  | 7744  | 0.1535          |
| 0.131         | 17.0  | 8228  | 0.1541          |
| 0.1258        | 18.0  | 8712  | 0.1532          |
| 0.1232        | 19.0  | 9196  | 0.1505          |
| 0.1258        | 20.0  | 9680  | 0.1616          |
| 0.1257        | 21.0  | 10164 | 0.1547          |
| 0.1251        | 22.0  | 10648 | 0.1516          |
| 0.1264        | 23.0  | 11132 | 0.1512          |
| 0.1221        | 24.0  | 11616 | 0.1527          |
| 0.1239        | 25.0  | 12100 | 0.1536          |
| 0.1221        | 26.0  | 12584 | 0.1520          |
| 0.1272        | 27.0  | 13068 | 0.1515          |
| 0.1214        | 28.0  | 13552 | 0.1522          |
| 0.1211        | 29.0  | 14036 | 0.1490          |
| 0.1202        | 30.0  | 14520 | 0.1515          |
| 0.1256        | 31.0  | 15004 | 0.1503          |
| 0.1256        | 32.0  | 15488 | 0.1513          |
| 0.1215        | 33.0  | 15972 | 0.1494          |
| 0.1222        | 34.0  | 16456 | 0.1516          |
| 0.1226        | 35.0  | 16940 | 0.1507          |
| 0.1218        | 36.0  | 17424 | 0.1493          |
| 0.1222        | 37.0  | 17908 | 0.1529          |
| 0.12          | 38.0  | 18392 | 0.1516          |
| 0.1226        | 39.0  | 18876 | 0.1506          |
| 0.1213        | 40.0  | 19360 | 0.1495          |
| 0.1205        | 41.0  | 19844 | 0.1481          |
| 0.1207        | 42.0  | 20328 | 0.1495          |
| 0.1189        | 43.0  | 20812 | 0.1480          |
| 0.1194        | 44.0  | 21296 | 0.1488          |
| 0.1219        | 45.0  | 21780 | 0.1510          |
| 0.1191        | 46.0  | 22264 | 0.1492          |
| 0.1217        | 47.0  | 22748 | 0.1487          |
| 0.1185        | 48.0  | 23232 | 0.1511          |
| 0.1205        | 49.0  | 23716 | 0.1475          |
| 0.1218        | 50.0  | 24200 | 0.1497          |
| 0.1194        | 51.0  | 24684 | 0.1495          |
| 0.1221        | 52.0  | 25168 | 0.1497          |
| 0.1171        | 53.0  | 25652 | 0.1487          |
| 0.1189        | 54.0  | 26136 | 0.1476          |
| 0.1188        | 55.0  | 26620 | 0.1498          |
| 0.1186        | 56.0  | 27104 | 0.1483          |
| 0.1188        | 57.0  | 27588 | 0.1484          |
| 0.1177        | 58.0  | 28072 | 0.1488          |
| 0.1198        | 59.0  | 28556 | 0.1503          |
| 0.1173        | 60.0  | 29040 | 0.1476          |
| 0.1192        | 61.0  | 29524 | 0.1474          |
| 0.1189        | 62.0  | 30008 | 0.1476          |
| 0.1189        | 63.0  | 30492 | 0.1490          |
| 0.1167        | 64.0  | 30976 | 0.1485          |
| 0.1198        | 65.0  | 31460 | 0.1474          |
| 0.1177        | 66.0  | 31944 | 0.1477          |
| 0.1188        | 67.0  | 32428 | 0.1489          |
| 0.1174        | 68.0  | 32912 | 0.1478          |
| 0.1171        | 69.0  | 33396 | 0.1477          |
| 0.12          | 70.0  | 33880 | 0.1483          |
| 0.1188        | 71.0  | 34364 | 0.1475          |
| 0.1172        | 72.0  | 34848 | 0.1477          |
| 0.1174        | 73.0  | 35332 | 0.1474          |
| 0.1174        | 74.0  | 35816 | 0.1487          |
| 0.1174        | 75.0  | 36300 | 0.1484          |
| 0.1178        | 76.0  | 36784 | 0.1478          |
| 0.1169        | 77.0  | 37268 | 0.1488          |
| 0.1182        | 78.0  | 37752 | 0.1478          |
| 0.1132        | 79.0  | 38236 | 0.1477          |
| 0.1175        | 80.0  | 38720 | 0.1472          |
| 0.1142        | 81.0  | 39204 | 0.1473          |
| 0.1176        | 82.0  | 39688 | 0.1475          |
| 0.118         | 83.0  | 40172 | 0.1476          |
| 0.1168        | 84.0  | 40656 | 0.1475          |
| 0.116         | 85.0  | 41140 | 0.1475          |
| 0.116         | 86.0  | 41624 | 0.1472          |
| 0.1164        | 87.0  | 42108 | 0.1472          |
| 0.1149        | 88.0  | 42592 | 0.1474          |
| 0.1144        | 89.0  | 43076 | 0.1471          |
| 0.1154        | 90.0  | 43560 | 0.1470          |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.0
- Tokenizers 0.21.0