---
library_name: transformers
license: apache-2.0
base_model: timm/efficientvit_b1.r224_in1k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: efficientvit_b1.r224_in1k_rice-leaf-disease-augmented-v4_v5_fft
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# efficientvit_b1.r224_in1k_rice-leaf-disease-augmented-v4_v5_fft

This model is a fine-tuned version of [timm/efficientvit_b1.r224_in1k](https://huggingface.co/timm/efficientvit_b1.r224_in1k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2422
- Accuracy: 0.9262

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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: cosine_with_restarts
- lr_scheduler_warmup_steps: 256
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1301        | 0.5   | 64   | 2.0282          | 0.1711   |
| 2.0177        | 1.0   | 128  | 1.8643          | 0.3121   |
| 1.7993        | 1.5   | 192  | 1.6252          | 0.4765   |
| 1.5315        | 2.0   | 256  | 1.3194          | 0.5940   |
| 1.1986        | 2.5   | 320  | 1.0574          | 0.6611   |
| 0.9772        | 3.0   | 384  | 0.8973          | 0.7349   |
| 0.7861        | 3.5   | 448  | 0.7841          | 0.7685   |
| 0.6808        | 4.0   | 512  | 0.6916          | 0.7886   |
| 0.5661        | 4.5   | 576  | 0.6288          | 0.8289   |
| 0.5042        | 5.0   | 640  | 0.5646          | 0.8456   |
| 0.4319        | 5.5   | 704  | 0.5420          | 0.8423   |
| 0.4057        | 6.0   | 768  | 0.5023          | 0.8591   |
| 0.3552        | 6.5   | 832  | 0.4788          | 0.8490   |
| 0.3201        | 7.0   | 896  | 0.4474          | 0.8591   |
| 0.3034        | 7.5   | 960  | 0.4353          | 0.8591   |
| 0.2745        | 8.0   | 1024 | 0.4315          | 0.8658   |
| 0.2588        | 8.5   | 1088 | 0.4181          | 0.8624   |
| 0.2601        | 9.0   | 1152 | 0.4154          | 0.8591   |
| 0.2374        | 9.5   | 1216 | 0.4123          | 0.8591   |
| 0.2464        | 10.0  | 1280 | 0.4054          | 0.8725   |
| 0.237         | 10.5  | 1344 | 0.4071          | 0.8591   |
| 0.2334        | 11.0  | 1408 | 0.3991          | 0.8658   |
| 0.236         | 11.5  | 1472 | 0.3978          | 0.8792   |
| 0.2267        | 12.0  | 1536 | 0.3735          | 0.8826   |
| 0.1949        | 12.5  | 1600 | 0.3543          | 0.8758   |
| 0.1715        | 13.0  | 1664 | 0.3416          | 0.8859   |
| 0.1381        | 13.5  | 1728 | 0.3150          | 0.8926   |
| 0.1274        | 14.0  | 1792 | 0.2979          | 0.8926   |
| 0.1026        | 14.5  | 1856 | 0.3080          | 0.8960   |
| 0.0888        | 15.0  | 1920 | 0.2842          | 0.8993   |
| 0.0722        | 15.5  | 1984 | 0.2812          | 0.9027   |
| 0.0723        | 16.0  | 2048 | 0.2804          | 0.8960   |
| 0.0615        | 16.5  | 2112 | 0.2734          | 0.9094   |
| 0.053         | 17.0  | 2176 | 0.2662          | 0.8993   |
| 0.0481        | 17.5  | 2240 | 0.2615          | 0.9060   |
| 0.0472        | 18.0  | 2304 | 0.2646          | 0.9027   |
| 0.0425        | 18.5  | 2368 | 0.2603          | 0.9060   |
| 0.041         | 19.0  | 2432 | 0.2545          | 0.9060   |
| 0.0379        | 19.5  | 2496 | 0.2536          | 0.9027   |
| 0.0427        | 20.0  | 2560 | 0.2567          | 0.9060   |
| 0.0403        | 20.5  | 2624 | 0.2510          | 0.9094   |
| 0.0374        | 21.0  | 2688 | 0.2571          | 0.9027   |
| 0.039         | 21.5  | 2752 | 0.2548          | 0.9027   |
| 0.0332        | 22.0  | 2816 | 0.2584          | 0.9027   |
| 0.0236        | 22.5  | 2880 | 0.2474          | 0.9161   |
| 0.0225        | 23.0  | 2944 | 0.2417          | 0.9161   |
| 0.016         | 23.5  | 3008 | 0.2442          | 0.9128   |
| 0.0135        | 24.0  | 3072 | 0.2411          | 0.9128   |
| 0.0112        | 24.5  | 3136 | 0.2378          | 0.9060   |
| 0.01          | 25.0  | 3200 | 0.2369          | 0.9161   |
| 0.008         | 25.5  | 3264 | 0.2391          | 0.9161   |
| 0.0069        | 26.0  | 3328 | 0.2333          | 0.9228   |
| 0.0062        | 26.5  | 3392 | 0.2339          | 0.9195   |
| 0.0065        | 27.0  | 3456 | 0.2366          | 0.9094   |
| 0.0056        | 27.5  | 3520 | 0.2390          | 0.9195   |
| 0.0057        | 28.0  | 3584 | 0.2387          | 0.9161   |
| 0.005         | 28.5  | 3648 | 0.2417          | 0.9128   |
| 0.005         | 29.0  | 3712 | 0.2420          | 0.9128   |
| 0.0056        | 29.5  | 3776 | 0.2457          | 0.9161   |
| 0.005         | 30.0  | 3840 | 0.2392          | 0.9195   |
| 0.0052        | 30.5  | 3904 | 0.2323          | 0.9161   |
| 0.0048        | 31.0  | 3968 | 0.2432          | 0.9195   |
| 0.0042        | 31.5  | 4032 | 0.2426          | 0.9161   |
| 0.0037        | 32.0  | 4096 | 0.2395          | 0.9195   |
| 0.0028        | 32.5  | 4160 | 0.2380          | 0.9228   |
| 0.0028        | 33.0  | 4224 | 0.2485          | 0.9195   |
| 0.0025        | 33.5  | 4288 | 0.2410          | 0.9262   |
| 0.0021        | 34.0  | 4352 | 0.2392          | 0.9228   |
| 0.0017        | 34.5  | 4416 | 0.2463          | 0.9228   |
| 0.0017        | 35.0  | 4480 | 0.2443          | 0.9161   |
| 0.0016        | 35.5  | 4544 | 0.2463          | 0.9262   |
| 0.0016        | 36.0  | 4608 | 0.2478          | 0.9161   |
| 0.0015        | 36.5  | 4672 | 0.2484          | 0.9195   |
| 0.0013        | 37.0  | 4736 | 0.2486          | 0.9228   |
| 0.0013        | 37.5  | 4800 | 0.2449          | 0.9161   |
| 0.0014        | 38.0  | 4864 | 0.2479          | 0.9228   |
| 0.0013        | 38.5  | 4928 | 0.2553          | 0.9161   |
| 0.0012        | 39.0  | 4992 | 0.2456          | 0.9228   |
| 0.0013        | 39.5  | 5056 | 0.2507          | 0.9161   |
| 0.0013        | 40.0  | 5120 | 0.2422          | 0.9262   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.1