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---
library_name: transformers
license: other
base_model: apple/mobilevit-xx-small
tags:
- generated_from_trainer
datasets:
- webdataset
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: mobilevit-xx-small-v2024-10-22
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: webdataset
type: webdataset
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9337777777777778
- name: F1
type: f1
value: 0.826945412311266
- name: Precision
type: precision
value: 0.8259860788863109
- name: Recall
type: recall
value: 0.827906976744186
---
<!-- 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. -->
# mobilevit-xx-small-v2024-10-22
This model is a fine-tuned version of [apple/mobilevit-xx-small](https://huggingface.co/apple/mobilevit-xx-small) on the webdataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1725
- Accuracy: 0.9338
- F1: 0.8269
- Precision: 0.8260
- Recall: 0.8279
## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6549 | 1.7544 | 100 | 0.6289 | 0.82 | 0.6260 | 0.5191 | 0.7884 |
| 0.4616 | 3.5088 | 200 | 0.4192 | 0.8867 | 0.7296 | 0.6706 | 0.8 |
| 0.3101 | 5.2632 | 300 | 0.3071 | 0.9036 | 0.7318 | 0.7810 | 0.6884 |
| 0.2932 | 7.0175 | 400 | 0.2486 | 0.908 | 0.7460 | 0.7896 | 0.7070 |
| 0.2652 | 8.7719 | 500 | 0.2279 | 0.9138 | 0.7674 | 0.7921 | 0.7442 |
| 0.2253 | 10.5263 | 600 | 0.2100 | 0.9218 | 0.7859 | 0.8240 | 0.7512 |
| 0.2257 | 12.2807 | 700 | 0.1951 | 0.9249 | 0.8019 | 0.8085 | 0.7953 |
| 0.2468 | 14.0351 | 800 | 0.1906 | 0.9307 | 0.8199 | 0.8142 | 0.8256 |
| 0.1796 | 15.7895 | 900 | 0.1949 | 0.9276 | 0.8120 | 0.8055 | 0.8186 |
| 0.1888 | 17.5439 | 1000 | 0.1807 | 0.9307 | 0.8178 | 0.8216 | 0.8140 |
| 0.202 | 19.2982 | 1100 | 0.1772 | 0.9342 | 0.8287 | 0.8249 | 0.8326 |
| 0.1824 | 21.0526 | 1200 | 0.1826 | 0.9276 | 0.8080 | 0.8186 | 0.7977 |
| 0.1808 | 22.8070 | 1300 | 0.1682 | 0.9347 | 0.8297 | 0.8268 | 0.8326 |
| 0.1792 | 24.5614 | 1400 | 0.1688 | 0.9364 | 0.8324 | 0.8392 | 0.8256 |
| 0.1852 | 26.3158 | 1500 | 0.1725 | 0.9338 | 0.8269 | 0.8260 | 0.8279 |
| 0.177 | 28.0702 | 1600 | 0.1690 | 0.9351 | 0.8282 | 0.8381 | 0.8186 |
| 0.1857 | 29.8246 | 1700 | 0.1708 | 0.9298 | 0.8176 | 0.8119 | 0.8233 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
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