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---
base_model: apple/mobilevitv2-1.0-imagenet1k-256
datasets:
- webdataset
library_name: transformers
license: other
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: mobilevitv2-1.0-imagenet1k-256-finetuned_v2024-10-21-frost
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: webdataset
type: webdataset
config: default
split: train
args: default
metrics:
- type: accuracy
value: 0.9444444444444444
name: Accuracy
- type: f1
value: 0.8544819557625145
name: F1
- type: precision
value: 0.8615023474178404
name: Precision
- type: recall
value: 0.8475750577367206
name: Recall
---
<!-- 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. -->
# mobilevitv2-1.0-imagenet1k-256-finetuned_v2024-10-21-frost
This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on the webdataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1539
- Accuracy: 0.9444
- F1: 0.8545
- Precision: 0.8615
- Recall: 0.8476
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6635 | 1.7544 | 100 | 0.6513 | 0.7604 | 0.5705 | 0.4355 | 0.8268 |
| 0.4461 | 3.5088 | 200 | 0.3972 | 0.8769 | 0.7292 | 0.6322 | 0.8614 |
| 0.2599 | 5.2632 | 300 | 0.2404 | 0.9227 | 0.8049 | 0.7821 | 0.8291 |
| 0.2074 | 7.0175 | 400 | 0.1942 | 0.9347 | 0.8256 | 0.8488 | 0.8037 |
| 0.167 | 8.7719 | 500 | 0.1772 | 0.9364 | 0.8354 | 0.8326 | 0.8383 |
| 0.1661 | 10.5263 | 600 | 0.1653 | 0.9342 | 0.8259 | 0.8417 | 0.8106 |
| 0.1603 | 12.2807 | 700 | 0.1649 | 0.9409 | 0.8473 | 0.8425 | 0.8522 |
| 0.1523 | 14.0351 | 800 | 0.1568 | 0.9467 | 0.8592 | 0.8735 | 0.8453 |
| 0.1506 | 15.7895 | 900 | 0.1548 | 0.9431 | 0.8494 | 0.8657 | 0.8337 |
| 0.1485 | 17.5439 | 1000 | 0.1539 | 0.9444 | 0.8545 | 0.8615 | 0.8476 |
| 0.1263 | 19.2982 | 1100 | 0.1521 | 0.944 | 0.8535 | 0.8595 | 0.8476 |
| 0.1444 | 21.0526 | 1200 | 0.1552 | 0.9418 | 0.8471 | 0.8561 | 0.8383 |
| 0.1133 | 22.8070 | 1300 | 0.1531 | 0.9449 | 0.8561 | 0.8601 | 0.8522 |
| 0.1019 | 24.5614 | 1400 | 0.1577 | 0.9431 | 0.8491 | 0.8675 | 0.8314 |
| 0.1141 | 26.3158 | 1500 | 0.1560 | 0.9413 | 0.8472 | 0.8492 | 0.8453 |
| 0.1087 | 28.0702 | 1600 | 0.1573 | 0.9422 | 0.8492 | 0.8531 | 0.8453 |
| 0.1015 | 29.8246 | 1700 | 0.1545 | 0.9422 | 0.8488 | 0.8548 | 0.8430 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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