# Model Card for Model ID

This model is vit-base-patch16-224-in21k fine-tuned with a subset of the 2021 Kaggle Google Landmark Dataset competition, including only the top 51 categories. 
The dataset is available as Hugginface dataset on: https://huggingface.co/datasets/pemujo/GLDv2_Top_51_Categories


- **Developed by:** Pedro Melendez
- **Model type:** Vision transformer
- **Finetuned from model:** vit-base-patch16-224-in21k


### Training Data

Classes with more than 500 images in the 2021 Kaggle Google Landmark competition
https://huggingface.co/datasets/pemujo/GLDv2_Top_51_Categories

### Results

|                        |       |
|-----------------------:|------:|
| epoch                  |4      |
|eval_accuracy           |0.97411|
|eval_loss               |0.11560|
|eval_runtime (secs)     |79.0939|
|eval_samples_per_second |115.255|
|eval_steps_per_second   |14.413 |
|train_runtime (secs)    |4082.92|
|train_samples_per_second|35.722 |
|train_steps_per_second  |2.233  |


## Environmental Impact

- **Hardware Type:** Nvidia V100
- **Minutes used:** 68 Minutes
- **Cloud Provider:** Google Cloud
- **Compute Region:** us-central

### Compute Infrastructure

Google Cloud Workbench Instance

#### Hardware

GCP Workbench n1-highmem-8 instance with Nvidia V100 GPU

#### Software

Python 3.9
Pytorch 2.0.1+cu117
{"language": ["en"], "library_name": "transformers", "datasets": ["pemujo/GLDv2_Top_51_Categories"]}
transformers pytorch vit image-classification en dataset:pemujo/GLDv2_Top_51_Categories autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #vit #image-classification #en #dataset-pemujo/GLDv2_Top_51_Categories #autotrain_compatible #endpoints_compatible #region-us 

Model Card for Model ID
=======================


This model is vit-base-patch16-224-in21k fine-tuned with a subset of the 2021 Kaggle Google Landmark Dataset competition, including only the top 51 categories.
The dataset is available as Hugginface dataset on: URL


* Developed by: Pedro Melendez
* Model type: Vision transformer
* Finetuned from model: vit-base-patch16-224-in21k


### Training Data


Classes with more than 500 images in the 2021 Kaggle Google Landmark competition
URL


### Results



Environmental Impact
--------------------


* Hardware Type: Nvidia V100
* Minutes used: 68 Minutes
* Cloud Provider: Google Cloud
* Compute Region: us-central


### Compute Infrastructure


Google Cloud Workbench Instance


#### Hardware


GCP Workbench n1-highmem-8 instance with Nvidia V100 GPU


#### Software


Python 3.9
Pytorch 2.0.1+cu117



### Training Data


Classes with more than 500 images in the 2021 Kaggle Google Landmark competition
URL ### Results



Environmental Impact
--------------------


* Hardware Type: Nvidia V100
* Minutes used: 68 Minutes
* Cloud Provider: Google Cloud
* Compute Region: us-central ### Compute Infrastructure


Google Cloud Workbench Instance #### Hardware


GCP Workbench n1-highmem-8 instance with Nvidia V100 GPU #### Software


Python 3.9
Pytorch 2.0.1+cu117
TAGS
#transformers #pytorch #vit #image-classification #en #dataset-pemujo/GLDv2_Top_51_Categories #autotrain_compatible #endpoints_compatible #region-us 
 ### Training Data


Classes with more than 500 images in the 2021 Kaggle Google Landmark competition
URL ### Results



Environmental Impact
--------------------


* Hardware Type: Nvidia V100
* Minutes used: 68 Minutes
* Cloud Provider: Google Cloud
* Compute Region: us-central ### Compute Infrastructure


Google Cloud Workbench Instance #### Hardware


GCP Workbench n1-highmem-8 instance with Nvidia V100 GPU #### Software


Python 3.9
Pytorch 2.0.1+cu117