---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-inaturalist
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8541666666666666
---

<!-- 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. -->

# vit-base-patch16-224-in21k-finetuned-inaturalist

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: 0.7703
- Accuracy: 0.8542

## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.8421  | 4    | 3.1793          | 0.0347   |
| No log        | 1.8947  | 9    | 3.1647          | 0.0486   |
| 3.1648        | 2.9474  | 14   | 3.1382          | 0.0944   |
| 3.1648        | 4.0     | 19   | 3.0995          | 0.1556   |
| 3.0817        | 4.8421  | 23   | 3.0555          | 0.2639   |
| 3.0817        | 5.8947  | 28   | 2.9849          | 0.3889   |
| 2.9167        | 6.9474  | 33   | 2.8932          | 0.5139   |
| 2.9167        | 8.0     | 38   | 2.7775          | 0.5972   |
| 2.6682        | 8.8421  | 42   | 2.6706          | 0.6528   |
| 2.6682        | 9.8947  | 47   | 2.5233          | 0.7069   |
| 2.3659        | 10.9474 | 52   | 2.3859          | 0.7375   |
| 2.3659        | 12.0    | 57   | 2.2546          | 0.75     |
| 2.079         | 12.8421 | 61   | 2.1531          | 0.7528   |
| 2.079         | 13.8947 | 66   | 2.0372          | 0.75     |
| 1.828         | 14.9474 | 71   | 1.9339          | 0.7597   |
| 1.828         | 16.0    | 76   | 1.8403          | 0.7694   |
| 1.6253        | 16.8421 | 80   | 1.7733          | 0.7764   |
| 1.6253        | 17.8947 | 85   | 1.6914          | 0.7903   |
| 1.4502        | 18.9474 | 90   | 1.6153          | 0.7875   |
| 1.4502        | 20.0    | 95   | 1.5510          | 0.7986   |
| 1.4502        | 20.8421 | 99   | 1.5016          | 0.8      |
| 1.2959        | 21.8947 | 104  | 1.4454          | 0.8222   |
| 1.2959        | 22.9474 | 109  | 1.3912          | 0.8181   |
| 1.1802        | 24.0    | 114  | 1.3390          | 0.8333   |
| 1.1802        | 24.8421 | 118  | 1.2995          | 0.8333   |
| 1.0629        | 25.8947 | 123  | 1.2707          | 0.8389   |
| 1.0629        | 26.9474 | 128  | 1.2335          | 0.8361   |
| 0.9801        | 28.0    | 133  | 1.1975          | 0.8444   |
| 0.9801        | 28.8421 | 137  | 1.1672          | 0.8389   |
| 0.9076        | 29.8947 | 142  | 1.1338          | 0.8444   |
| 0.9076        | 30.9474 | 147  | 1.1137          | 0.8472   |
| 0.8349        | 32.0    | 152  | 1.0855          | 0.8528   |
| 0.8349        | 32.8421 | 156  | 1.0717          | 0.8542   |
| 0.7782        | 33.8947 | 161  | 1.0483          | 0.8514   |
| 0.7782        | 34.9474 | 166  | 1.0352          | 0.85     |
| 0.7208        | 36.0    | 171  | 1.0202          | 0.8556   |
| 0.7208        | 36.8421 | 175  | 0.9994          | 0.8486   |
| 0.6708        | 37.8947 | 180  | 0.9814          | 0.8556   |
| 0.6708        | 38.9474 | 185  | 0.9691          | 0.8542   |
| 0.6303        | 40.0    | 190  | 0.9599          | 0.8486   |
| 0.6303        | 40.8421 | 194  | 0.9422          | 0.8472   |
| 0.6303        | 41.8947 | 199  | 0.9278          | 0.8486   |
| 0.6018        | 42.9474 | 204  | 0.9172          | 0.8528   |
| 0.6018        | 44.0    | 209  | 0.9093          | 0.8514   |
| 0.5622        | 44.8421 | 213  | 0.9030          | 0.8583   |
| 0.5622        | 45.8947 | 218  | 0.8972          | 0.8625   |
| 0.5474        | 46.9474 | 223  | 0.8859          | 0.8569   |
| 0.5474        | 48.0    | 228  | 0.8858          | 0.8653   |
| 0.5254        | 48.8421 | 232  | 0.8779          | 0.8556   |
| 0.5254        | 49.8947 | 237  | 0.8635          | 0.8569   |
| 0.5036        | 50.9474 | 242  | 0.8563          | 0.8611   |
| 0.5036        | 52.0    | 247  | 0.8613          | 0.8542   |
| 0.4855        | 52.8421 | 251  | 0.8546          | 0.8625   |
| 0.4855        | 53.8947 | 256  | 0.8469          | 0.8597   |
| 0.4697        | 54.9474 | 261  | 0.8327          | 0.8528   |
| 0.4697        | 56.0    | 266  | 0.8268          | 0.8597   |
| 0.4482        | 56.8421 | 270  | 0.8188          | 0.8556   |
| 0.4482        | 57.8947 | 275  | 0.8171          | 0.8653   |
| 0.4436        | 58.9474 | 280  | 0.8133          | 0.8486   |
| 0.4436        | 60.0    | 285  | 0.8070          | 0.8639   |
| 0.4436        | 60.8421 | 289  | 0.7986          | 0.8542   |
| 0.4211        | 61.8947 | 294  | 0.7937          | 0.8597   |
| 0.4211        | 62.9474 | 299  | 0.7908          | 0.8611   |
| 0.4228        | 64.0    | 304  | 0.7952          | 0.8625   |
| 0.4228        | 64.8421 | 308  | 0.8010          | 0.8514   |
| 0.4046        | 65.8947 | 313  | 0.7975          | 0.8472   |
| 0.4046        | 66.9474 | 318  | 0.7927          | 0.8417   |
| 0.4048        | 68.0    | 323  | 0.7880          | 0.8556   |
| 0.4048        | 68.8421 | 327  | 0.7860          | 0.8514   |
| 0.3925        | 69.8947 | 332  | 0.7899          | 0.8403   |
| 0.3925        | 70.9474 | 337  | 0.7883          | 0.8417   |
| 0.3936        | 72.0    | 342  | 0.7885          | 0.8417   |
| 0.3936        | 72.8421 | 346  | 0.7874          | 0.8361   |
| 0.3985        | 73.8947 | 351  | 0.7832          | 0.8417   |
| 0.3985        | 74.9474 | 356  | 0.7787          | 0.8514   |
| 0.3849        | 76.0    | 361  | 0.7753          | 0.8486   |
| 0.3849        | 76.8421 | 365  | 0.7746          | 0.8514   |
| 0.3796        | 77.8947 | 370  | 0.7736          | 0.8542   |
| 0.3796        | 78.9474 | 375  | 0.7731          | 0.8528   |
| 0.3717        | 80.0    | 380  | 0.7715          | 0.8556   |
| 0.3717        | 80.8421 | 384  | 0.7709          | 0.8556   |
| 0.3717        | 81.8947 | 389  | 0.7706          | 0.8569   |
| 0.3802        | 82.9474 | 394  | 0.7704          | 0.8556   |
| 0.3802        | 84.0    | 399  | 0.7704          | 0.8542   |
| 0.3782        | 84.2105 | 400  | 0.7703          | 0.8542   |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.1