
# ViT Fine-tuned on Stanford Car Dataset

Base model: https://huggingface.co/google/vit-base-patch16-224

This achieves around 86% on the testing set, you can use it as a baseline for further tuning.

# Dataset Description 

The Stanford car dataset contains 16,185 images of 196 classes of cars. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe. The data is split into 8144 training images, 6,041 testing images, and 2000 validation images in this case. 

** Please note: this dataset does not contain newer car models **

# Using the Model in the Transformer Library

```
from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("therealcyberlord/stanford-car-vit-patch16")
model = AutoModelForImageClassification.from_pretrained("therealcyberlord/stanford-car-vit-patch16")
```


# Citations
3D Object Representations for Fine-Grained Categorization
Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei
4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.

{"license": "apache-2.0"}
transformers pytorch safetensors vit image-classification license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #vit #image-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 


# ViT Fine-tuned on Stanford Car Dataset

Base model: URL

This achieves around 86% on the testing set, you can use it as a baseline for further tuning.

# Dataset Description 

The Stanford car dataset contains 16,185 images of 196 classes of cars. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe. The data is split into 8144 training images, 6,041 testing images, and 2000 validation images in this case. 

 Please note: this dataset does not contain newer car models 

# Using the Model in the Transformer Library




s
3D Object Representations for Fine-Grained Categorization
Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei
4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.

# ViT Fine-tuned on Stanford Car Dataset

Base model: URL

This achieves around 86% on the testing set, you can use it as a baseline for further tuning. # Dataset Description 

The Stanford car dataset contains 16,185 images of 196 classes of cars. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe. The data is split into 8144 training images, 6,041 testing images, and 2000 validation images in this case. 

 Please note: this dataset does not contain newer car models # Using the Model in the Transformer Library




s
3D Object Representations for Fine-Grained Categorization
Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei
4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.
TAGS
#transformers #pytorch #safetensors #vit #image-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # ViT Fine-tuned on Stanford Car Dataset

Base model: URL

This achieves around 86% on the testing set, you can use it as a baseline for further tuning. # Dataset Description 

The Stanford car dataset contains 16,185 images of 196 classes of cars. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe. The data is split into 8144 training images, 6,041 testing images, and 2000 validation images in this case. 

 Please note: this dataset does not contain newer car models # Using the Model in the Transformer Library




s
3D Object Representations for Fine-Grained Categorization
Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei
4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.
passage: TAGS
#transformers #pytorch #safetensors #vit #image-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# ViT Fine-tuned on Stanford Car Dataset

Base model: URL

This achieves around 86% on the testing set, you can use it as a baseline for further tuning.# Dataset Description 

The Stanford car dataset contains 16,185 images of 196 classes of cars. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe. The data is split into 8144 training images, 6,041 testing images, and 2000 validation images in this case. 

 Please note: this dataset does not contain newer car models# Using the Model in the Transformer Library




s
3D Object Representations for Fine-Grained Categorization
Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei
4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.