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weed_vit_balanced

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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weed_vit_balanced
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: weeds_hfclass11
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9566666666666667
---

<!-- 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. -->

# weeds_hfclass11
Model is trained on balanced dataset/ 250 image per class/ .8 .1 .1 split/ 224x224 resized

Dataset: https://www.kaggle.com/datasets/vbookshelf/v2-plant-seedlings-dataset

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3603
- Accuracy: 0.9567

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3089        | 0.99  | 37   | 2.0422          | 0.7133   |
| 1.4465        | 1.99  | 74   | 1.2227          | 0.8767   |
| 0.8455        | 2.99  | 111  | 0.8121          | 0.9067   |
| 0.6579        | 3.99  | 148  | 0.6161          | 0.9267   |
| 0.5163        | 4.99  | 185  | 0.5031          | 0.94     |
| 0.4374        | 5.99  | 222  | 0.4078          | 0.9633   |
| 0.3912        | 6.99  | 259  | 0.4134          | 0.9467   |
| 0.358         | 7.99  | 296  | 0.4207          | 0.9233   |
| 0.3509        | 8.99  | 333  | 0.3768          | 0.95     |
| 0.3288        | 9.99  | 370  | 0.3603          | 0.9567   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2