|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
- image-classification |
|
- pytorch |
|
datasets: |
|
- food101 |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: food101_outputs |
|
results: |
|
- task: |
|
name: Image Classification |
|
type: image-classification |
|
dataset: |
|
name: food-101 |
|
type: food101 |
|
args: default |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.8912871287128713 |
|
- task: |
|
type: image-classification |
|
name: Image Classification |
|
dataset: |
|
name: food101 |
|
type: food101 |
|
config: default |
|
split: validation |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.7872475247524753 |
|
verified: true |
|
- name: Precision Macro |
|
type: precision |
|
value: 0.8037731109218832 |
|
verified: true |
|
- name: Precision Micro |
|
type: precision |
|
value: 0.7872475247524753 |
|
verified: true |
|
- name: Precision Weighted |
|
type: precision |
|
value: 0.8037731109218832 |
|
verified: true |
|
- name: Recall Macro |
|
type: recall |
|
value: 0.7872475247524753 |
|
verified: true |
|
- name: Recall Micro |
|
type: recall |
|
value: 0.7872475247524753 |
|
verified: true |
|
- name: Recall Weighted |
|
type: recall |
|
value: 0.7872475247524753 |
|
verified: true |
|
- name: F1 Macro |
|
type: f1 |
|
value: 0.7898702754048251 |
|
verified: true |
|
- name: F1 Micro |
|
type: f1 |
|
value: 0.7872475247524753 |
|
verified: true |
|
- name: F1 Weighted |
|
type: f1 |
|
value: 0.789870275404825 |
|
verified: true |
|
- name: loss |
|
type: loss |
|
value: 0.8927117586135864 |
|
verified: true |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# nateraw/food |
|
|
|
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 nateraw/food101 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4501 |
|
- Accuracy: 0.8913 |
|
|
|
## 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: 128 |
|
- eval_batch_size: 128 |
|
- seed: 1337 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 5.0 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 0.8271 | 1.0 | 592 | 0.6070 | 0.8562 | |
|
| 0.4376 | 2.0 | 1184 | 0.4947 | 0.8691 | |
|
| 0.2089 | 3.0 | 1776 | 0.4876 | 0.8747 | |
|
| 0.0882 | 4.0 | 2368 | 0.4639 | 0.8857 | |
|
| 0.0452 | 5.0 | 2960 | 0.4501 | 0.8913 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.9.0.dev0 |
|
- Pytorch 1.9.0+cu102 |
|
- Datasets 1.9.1.dev0 |
|
- Tokenizers 0.10.3 |
|
|
