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---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: google-vit-base-patch16-224-OrganicAndInorganicWaste-classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9415
---
<!-- 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. -->
# google-vit-base-patch16-224-OrganicAndInorganicWaste-classification
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co./google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4018
- Accuracy: 0.9415
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:------:|:-----:|:--------:|:---------------:|
| 0.2168 | 0.1580 | 1000 | 0.9525 | 0.1303 |
| 0.196 | 0.3159 | 2000 | 0.941 | 0.1638 |
| 0.1993 | 0.4739 | 3000 | 0.9285 | 0.2206 |
| 0.1849 | 0.6318 | 4000 | 0.9225 | 0.2288 |
| 0.199 | 0.7898 | 5000 | 0.9105 | 0.3331 |
| 0.2171 | 0.9477 | 6000 | 0.944 | 0.1582 |
| 0.1209 | 1.1057 | 7000 | 0.9495 | 0.1887 |
| 0.114 | 1.2636 | 8000 | 0.932 | 0.1950 |
| 0.1268 | 1.4216 | 9000 | 0.9335 | 0.1965 |
| 0.1272 | 1.5795 | 10000 | 0.9165 | 0.3112 |
| 0.1003 | 1.7375 | 11000 | 0.9575 | 0.1353 |
| 0.0844 | 1.8954 | 12000 | 0.9345 | 0.2635 |
| 0.0757 | 2.0534 | 13000 | 0.952 | 0.1434 |
| 0.053 | 2.2113 | 14000 | 0.933 | 0.3203 |
| 0.0994 | 2.3693 | 15000 | 0.9405 | 0.2165 |
| 0.0248 | 2.5272 | 16000 | 0.951 | 0.2400 |
| 0.0842 | 2.6852 | 17000 | 0.906 | 0.4092 |
| 0.0733 | 2.8432 | 18000 | 0.9515 | 0.1937 |
| 0.0542 | 3.0011 | 19000 | 0.938 | 0.2911 |
| 0.0202 | 3.1591 | 20000 | 0.936 | 0.3648 |
| 0.0237 | 3.3170 | 21000 | 0.9355 | 0.3618 |
| 0.0294 | 3.4750 | 22000 | 0.4209 | 0.9255 |
| 0.0375 | 3.6329 | 23000 | 0.2840 | 0.943 |
| 0.0176 | 3.7909 | 24000 | 0.2604 | 0.9525 |
| 0.0252 | 3.9488 | 25000 | 0.2500 | 0.9515 |
| 0.0024 | 4.1068 | 26000 | 0.2892 | 0.9545 |
| 0.0119 | 4.2647 | 27000 | 0.3036 | 0.956 |
| 0.0005 | 4.4227 | 28000 | 0.4115 | 0.946 |
| 0.0011 | 4.5806 | 29000 | 0.3025 | 0.948 |
| 0.0012 | 4.7386 | 30000 | 0.3437 | 0.946 |
| 0.0001 | 4.8965 | 31000 | 0.4018 | 0.9415 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1
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