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# finetuned-Leukemia-cell

This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1128
- Accuracy: 0.9850

## 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: 0.0002
- 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: 300
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 0.3182        | 2.94   | 100   | 0.2301          | 0.9211   |
| 0.2223        | 5.88   | 200   | 0.3411          | 0.8910   |
| 0.1695        | 8.82   | 300   | 0.1168          | 0.9624   |
| 0.0579        | 11.76  | 400   | 0.1632          | 0.9511   |
| 0.1184        | 14.71  | 500   | 0.4665          | 0.8346   |
| 0.0575        | 17.65  | 600   | 0.1563          | 0.9586   |
| 0.1087        | 20.59  | 700   | 0.2023          | 0.9511   |
| 0.1164        | 23.53  | 800   | 0.2283          | 0.9398   |
| 0.1144        | 26.47  | 900   | 0.1130          | 0.9624   |
| 0.1821        | 29.41  | 1000  | 0.1155          | 0.9737   |
| 0.0882        | 32.35  | 1100  | 0.0760          | 0.9850   |
| 0.1099        | 35.29  | 1200  | 0.0894          | 0.9737   |
| 0.053         | 38.24  | 1300  | 0.1248          | 0.9699   |
| 0.0489        | 41.18  | 1400  | 0.1081          | 0.9774   |
| 0.065         | 44.12  | 1500  | 0.1694          | 0.9549   |
| 0.037         | 47.06  | 1600  | 0.1060          | 0.9699   |
| 0.0281        | 50.0   | 1700  | 0.0892          | 0.9737   |
| 0.0394        | 52.94  | 1800  | 0.1680          | 0.9624   |
| 0.0828        | 55.88  | 1900  | 0.1404          | 0.9774   |
| 0.0663        | 58.82  | 2000  | 0.1683          | 0.9662   |
| 0.0698        | 61.76  | 2100  | 0.1517          | 0.9624   |
| 0.0938        | 64.71  | 2200  | 0.1031          | 0.9737   |
| 0.0324        | 67.65  | 2300  | 0.1251          | 0.9812   |
| 0.0713        | 70.59  | 2400  | 0.1597          | 0.9662   |
| 0.059         | 73.53  | 2500  | 0.1455          | 0.9699   |
| 0.0404        | 76.47  | 2600  | 0.0924          | 0.9624   |
| 0.0526        | 79.41  | 2700  | 0.0853          | 0.9812   |
| 0.0439        | 82.35  | 2800  | 0.0815          | 0.9850   |
| 0.0485        | 85.29  | 2900  | 0.1192          | 0.9774   |
| 0.0498        | 88.24  | 3000  | 0.0958          | 0.9737   |
| 0.0181        | 91.18  | 3100  | 0.1351          | 0.9699   |
| 0.0226        | 94.12  | 3200  | 0.1458          | 0.9774   |
| 0.1115        | 97.06  | 3300  | 0.1453          | 0.9737   |
| 0.0349        | 100.0  | 3400  | 0.1257          | 0.9812   |
| 0.0246        | 102.94 | 3500  | 0.1405          | 0.9662   |
| 0.0084        | 105.88 | 3600  | 0.0666          | 0.9887   |
| 0.0174        | 108.82 | 3700  | 0.1419          | 0.9662   |
| 0.0432        | 111.76 | 3800  | 0.2027          | 0.9662   |
| 0.0164        | 114.71 | 3900  | 0.0671          | 0.9812   |
| 0.0223        | 117.65 | 4000  | 0.0722          | 0.9850   |
| 0.012         | 120.59 | 4100  | 0.1285          | 0.9699   |
| 0.0143        | 123.53 | 4200  | 0.1102          | 0.9812   |
| 0.0254        | 126.47 | 4300  | 0.1139          | 0.9812   |
| 0.018         | 129.41 | 4400  | 0.1056          | 0.9737   |
| 0.0011        | 132.35 | 4500  | 0.1097          | 0.9774   |
| 0.08          | 135.29 | 4600  | 0.1425          | 0.9662   |
| 0.0292        | 138.24 | 4700  | 0.0871          | 0.9812   |
| 0.0248        | 141.18 | 4800  | 0.1082          | 0.9699   |
| 0.0064        | 144.12 | 4900  | 0.0644          | 0.9850   |
| 0.0115        | 147.06 | 5000  | 0.0912          | 0.9812   |
| 0.052         | 150.0  | 5100  | 0.0927          | 0.9850   |
| 0.0103        | 152.94 | 5200  | 0.1129          | 0.9774   |
| 0.0185        | 155.88 | 5300  | 0.1250          | 0.9699   |
| 0.0185        | 158.82 | 5400  | 0.1226          | 0.9737   |
| 0.0002        | 161.76 | 5500  | 0.1146          | 0.9812   |
| 0.0249        | 164.71 | 5600  | 0.1945          | 0.9737   |
| 0.0165        | 167.65 | 5700  | 0.1875          | 0.9586   |
| 0.0028        | 170.59 | 5800  | 0.1045          | 0.9774   |
| 0.0044        | 173.53 | 5900  | 0.1279          | 0.9774   |
| 0.0078        | 176.47 | 6000  | 0.0967          | 0.9774   |
| 0.0093        | 179.41 | 6100  | 0.1450          | 0.9812   |
| 0.0261        | 182.35 | 6200  | 0.0815          | 0.9850   |
| 0.0218        | 185.29 | 6300  | 0.1586          | 0.9699   |
| 0.1184        | 188.24 | 6400  | 0.1481          | 0.9812   |
| 0.0011        | 191.18 | 6500  | 0.1698          | 0.9737   |
| 0.0131        | 194.12 | 6600  | 0.2247          | 0.9662   |
| 0.0156        | 197.06 | 6700  | 0.1205          | 0.9812   |
| 0.007         | 200.0  | 6800  | 0.1864          | 0.9699   |
| 0.015         | 202.94 | 6900  | 0.1684          | 0.9774   |
| 0.0032        | 205.88 | 7000  | 0.0835          | 0.9850   |
| 0.0017        | 208.82 | 7100  | 0.1174          | 0.9812   |
| 0.0397        | 211.76 | 7200  | 0.1926          | 0.9662   |
| 0.0015        | 214.71 | 7300  | 0.1646          | 0.9699   |
| 0.0046        | 217.65 | 7400  | 0.1520          | 0.9774   |
| 0.0193        | 220.59 | 7500  | 0.1436          | 0.9812   |
| 0.0474        | 223.53 | 7600  | 0.1747          | 0.9737   |
| 0.001         | 226.47 | 7700  | 0.1647          | 0.9812   |
| 0.0005        | 229.41 | 7800  | 0.1992          | 0.9699   |
| 0.0119        | 232.35 | 7900  | 0.1545          | 0.9699   |
| 0.0153        | 235.29 | 8000  | 0.2018          | 0.9662   |
| 0.0106        | 238.24 | 8100  | 0.1798          | 0.9774   |
| 0.0012        | 241.18 | 8200  | 0.1896          | 0.9774   |
| 0.0           | 244.12 | 8300  | 0.1500          | 0.9812   |
| 0.0339        | 247.06 | 8400  | 0.1890          | 0.9662   |
| 0.0016        | 250.0  | 8500  | 0.1410          | 0.9812   |
| 0.0003        | 252.94 | 8600  | 0.1341          | 0.9812   |
| 0.001         | 255.88 | 8700  | 0.1209          | 0.9850   |
| 0.0071        | 258.82 | 8800  | 0.1191          | 0.9812   |
| 0.0           | 261.76 | 8900  | 0.0960          | 0.9887   |
| 0.0016        | 264.71 | 9000  | 0.1063          | 0.9850   |
| 0.0048        | 267.65 | 9100  | 0.1583          | 0.9737   |
| 0.0026        | 270.59 | 9200  | 0.1473          | 0.9774   |
| 0.0006        | 273.53 | 9300  | 0.1325          | 0.9812   |
| 0.0226        | 276.47 | 9400  | 0.1214          | 0.9812   |
| 0.0075        | 279.41 | 9500  | 0.1399          | 0.9812   |
| 0.0047        | 282.35 | 9600  | 0.1291          | 0.9850   |
| 0.0           | 285.29 | 9700  | 0.1117          | 0.9812   |
| 0.0001        | 288.24 | 9800  | 0.1137          | 0.9850   |
| 0.0001        | 291.18 | 9900  | 0.1117          | 0.9850   |
| 0.0           | 294.12 | 10000 | 0.1061          | 0.9850   |
| 0.0           | 297.06 | 10100 | 0.1129          | 0.9850   |
| 0.0057        | 300.0  | 10200 | 0.1128          | 0.9850   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

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