File size: 4,730 Bytes
5eacdfb ec6c855 5eacdfb ec6c855 5eacdfb ec6c855 5eacdfb ec6c855 5eacdfb ec6c855 5eacdfb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: microsoft_swinv2-tiny-patch4-window8-256-batch_16_epoch_4_classes_24_final_withAug
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: bengali_food_images
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9456521739130435
---
<!-- 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. -->
# microsoft_swinv2-tiny-patch4-window8-256-batch_16_epoch_4_classes_24_final_withAug
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the bengali_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2321
- Accuracy: 0.9457
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7162 | 0.09 | 100 | 1.4225 | 0.7079 |
| 1.2286 | 0.17 | 200 | 0.9461 | 0.7935 |
| 1.0323 | 0.26 | 300 | 0.7366 | 0.8356 |
| 0.8678 | 0.34 | 400 | 0.6211 | 0.8628 |
| 0.7849 | 0.43 | 500 | 0.5354 | 0.8655 |
| 0.7105 | 0.51 | 600 | 0.4793 | 0.8899 |
| 0.6198 | 0.6 | 700 | 0.4319 | 0.9090 |
| 0.6276 | 0.68 | 800 | 0.4022 | 0.8981 |
| 0.5411 | 0.77 | 900 | 0.3816 | 0.9117 |
| 0.4984 | 0.85 | 1000 | 0.3824 | 0.9022 |
| 0.5665 | 0.94 | 1100 | 0.3460 | 0.9212 |
| 0.5741 | 1.02 | 1200 | 0.3336 | 0.9158 |
| 0.4039 | 1.11 | 1300 | 0.3204 | 0.9130 |
| 0.4347 | 1.19 | 1400 | 0.3038 | 0.9307 |
| 0.3639 | 1.28 | 1500 | 0.2955 | 0.9253 |
| 0.4282 | 1.36 | 1600 | 0.2948 | 0.9293 |
| 0.4375 | 1.45 | 1700 | 0.2868 | 0.9212 |
| 0.3063 | 1.53 | 1800 | 0.2861 | 0.9334 |
| 0.3549 | 1.62 | 1900 | 0.2826 | 0.9293 |
| 0.4326 | 1.71 | 2000 | 0.2698 | 0.9348 |
| 0.3697 | 1.79 | 2100 | 0.2602 | 0.9280 |
| 0.3155 | 1.88 | 2200 | 0.2523 | 0.9361 |
| 0.3348 | 1.96 | 2300 | 0.2506 | 0.9470 |
| 0.3854 | 2.05 | 2400 | 0.2565 | 0.9321 |
| 0.3951 | 2.13 | 2500 | 0.2482 | 0.9402 |
| 0.3531 | 2.22 | 2600 | 0.2455 | 0.9402 |
| 0.3643 | 2.3 | 2700 | 0.2513 | 0.9375 |
| 0.3393 | 2.39 | 2800 | 0.2492 | 0.9429 |
| 0.3635 | 2.47 | 2900 | 0.2394 | 0.9402 |
| 0.3624 | 2.56 | 3000 | 0.2425 | 0.9389 |
| 0.3608 | 2.64 | 3100 | 0.2390 | 0.9457 |
| 0.3215 | 2.73 | 3200 | 0.2483 | 0.9321 |
| 0.2971 | 2.81 | 3300 | 0.2455 | 0.9402 |
| 0.3838 | 2.9 | 3400 | 0.2363 | 0.9470 |
| 0.3036 | 2.98 | 3500 | 0.2422 | 0.9402 |
| 0.401 | 3.07 | 3600 | 0.2398 | 0.9429 |
| 0.3458 | 3.15 | 3700 | 0.2517 | 0.9429 |
| 0.2908 | 3.24 | 3800 | 0.2423 | 0.9457 |
| 0.3016 | 3.32 | 3900 | 0.2402 | 0.9443 |
| 0.2961 | 3.41 | 4000 | 0.2414 | 0.9457 |
| 0.3822 | 3.5 | 4100 | 0.2413 | 0.9416 |
| 0.2596 | 3.58 | 4200 | 0.2356 | 0.9457 |
| 0.3064 | 3.67 | 4300 | 0.2324 | 0.9497 |
| 0.3059 | 3.75 | 4400 | 0.2321 | 0.9457 |
| 0.42 | 3.84 | 4500 | 0.2556 | 0.9402 |
| 0.2959 | 3.92 | 4600 | 0.2491 | 0.9416 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|