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# finetuned-Blastocyst_ICM

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Blastocyst_ICM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4171
- Accuracy: 0.8571

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.6635        | 0.7874  | 100  | 0.6705          | 0.7619   |
| 0.6585        | 1.5748  | 200  | 0.4977          | 0.8571   |
| 0.8002        | 2.3622  | 300  | 0.6194          | 0.8095   |
| 0.6907        | 3.1496  | 400  | 0.6097          | 0.7619   |
| 0.5806        | 3.9370  | 500  | 0.6073          | 0.7143   |
| 0.6059        | 4.7244  | 600  | 0.4171          | 0.8571   |
| 0.5804        | 5.5118  | 700  | 0.4862          | 0.8095   |
| 0.6223        | 6.2992  | 800  | 0.4292          | 0.8095   |
| 0.6696        | 7.0866  | 900  | 0.4574          | 0.8095   |
| 0.5269        | 7.8740  | 1000 | 0.4643          | 0.8571   |
| 0.5089        | 8.6614  | 1100 | 0.4634          | 0.8095   |
| 0.4782        | 9.4488  | 1200 | 0.5434          | 0.8095   |
| 0.5426        | 10.2362 | 1300 | 0.6587          | 0.6667   |
| 0.5604        | 11.0236 | 1400 | 0.4834          | 0.7143   |
| 0.627         | 11.8110 | 1500 | 0.5787          | 0.7619   |
| 0.4389        | 12.5984 | 1600 | 0.6285          | 0.7619   |
| 0.3936        | 13.3858 | 1700 | 0.7870          | 0.7619   |
| 0.4023        | 14.1732 | 1800 | 0.6466          | 0.8095   |
| 0.4683        | 14.9606 | 1900 | 0.5086          | 0.7619   |
| 0.4502        | 15.7480 | 2000 | 0.4940          | 0.8095   |
| 0.47          | 16.5354 | 2100 | 0.6389          | 0.8095   |
| 0.4109        | 17.3228 | 2200 | 0.4713          | 0.8571   |
| 0.4654        | 18.1102 | 2300 | 0.6457          | 0.7619   |
| 0.3359        | 18.8976 | 2400 | 0.4706          | 0.8095   |
| 0.3343        | 19.6850 | 2500 | 0.6813          | 0.8095   |
| 0.4359        | 20.4724 | 2600 | 0.8620          | 0.7143   |
| 0.446         | 21.2598 | 2700 | 0.5914          | 0.9048   |
| 0.2901        | 22.0472 | 2800 | 0.8846          | 0.8095   |
| 0.3261        | 22.8346 | 2900 | 0.5528          | 0.8571   |
| 0.4159        | 23.6220 | 3000 | 0.6383          | 0.8095   |
| 0.3056        | 24.4094 | 3100 | 0.8316          | 0.8095   |
| 0.27          | 25.1969 | 3200 | 1.0901          | 0.7619   |
| 0.299         | 25.9843 | 3300 | 0.9170          | 0.7143   |
| 0.2433        | 26.7717 | 3400 | 1.0046          | 0.8095   |
| 0.2623        | 27.5591 | 3500 | 0.8359          | 0.7619   |
| 0.2526        | 28.3465 | 3600 | 0.5864          | 0.8571   |
| 0.3307        | 29.1339 | 3700 | 0.6282          | 0.8571   |
| 0.2038        | 29.9213 | 3800 | 1.0462          | 0.7619   |
| 0.3419        | 30.7087 | 3900 | 1.2281          | 0.7143   |
| 0.2625        | 31.4961 | 4000 | 0.9750          | 0.7619   |
| 0.1707        | 32.2835 | 4100 | 1.0191          | 0.8095   |
| 0.2046        | 33.0709 | 4200 | 0.9401          | 0.8095   |
| 0.2009        | 33.8583 | 4300 | 0.9374          | 0.8571   |
| 0.222         | 34.6457 | 4400 | 1.1820          | 0.8095   |
| 0.2469        | 35.4331 | 4500 | 0.8827          | 0.8571   |
| 0.1348        | 36.2205 | 4600 | 0.8871          | 0.8095   |
| 0.2494        | 37.0079 | 4700 | 0.8910          | 0.8095   |
| 0.1272        | 37.7953 | 4800 | 0.9666          | 0.8095   |
| 0.1682        | 38.5827 | 4900 | 0.8490          | 0.8571   |
| 0.1495        | 39.3701 | 5000 | 0.9831          | 0.8571   |
| 0.174         | 40.1575 | 5100 | 1.2082          | 0.7619   |
| 0.1375        | 40.9449 | 5200 | 1.2987          | 0.7619   |
| 0.1043        | 41.7323 | 5300 | 1.1446          | 0.8095   |
| 0.2958        | 42.5197 | 5400 | 1.3286          | 0.7143   |
| 0.1882        | 43.3071 | 5500 | 1.1686          | 0.8095   |
| 0.1322        | 44.0945 | 5600 | 1.3669          | 0.7619   |
| 0.1551        | 44.8819 | 5700 | 1.2882          | 0.7619   |
| 0.1749        | 45.6693 | 5800 | 1.2481          | 0.8095   |
| 0.1064        | 46.4567 | 5900 | 1.3224          | 0.8095   |
| 0.1947        | 47.2441 | 6000 | 1.1225          | 0.8095   |
| 0.1495        | 48.0315 | 6100 | 1.5170          | 0.7143   |
| 0.2192        | 48.8189 | 6200 | 0.9928          | 0.8571   |
| 0.1303        | 49.6063 | 6300 | 1.0310          | 0.8095   |
| 0.2188        | 50.3937 | 6400 | 1.0219          | 0.8095   |
| 0.1485        | 51.1811 | 6500 | 1.0695          | 0.8095   |
| 0.1065        | 51.9685 | 6600 | 1.2479          | 0.8095   |
| 0.1731        | 52.7559 | 6700 | 1.1878          | 0.8095   |
| 0.2507        | 53.5433 | 6800 | 1.1535          | 0.7619   |
| 0.1191        | 54.3307 | 6900 | 1.0544          | 0.8571   |
| 0.1048        | 55.1181 | 7000 | 1.1502          | 0.8095   |
| 0.1715        | 55.9055 | 7100 | 1.2187          | 0.8095   |
| 0.125         | 56.6929 | 7200 | 1.2635          | 0.8095   |
| 0.1022        | 57.4803 | 7300 | 1.2571          | 0.8095   |
| 0.0987        | 58.2677 | 7400 | 1.2676          | 0.8095   |
| 0.0761        | 59.0551 | 7500 | 1.2635          | 0.8095   |
| 0.1184        | 59.8425 | 7600 | 1.2629          | 0.8095   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

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