license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-vit-base-patch16
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5851995594482614
Caracam (gen 1)
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.9156
- Accuracy: 0.5852
Model description
First generation of my AI that tells you what car you took a picture of.
More versions coming soon with accuracy ratings of 85% and higher! Trained on 70+ brands with 2700+ cars going from 1945-2024.
App coming soon (also called Caracam) to Android and IOS
(Late March - Early April 2024).
In the future I will take user opinion into account on what brands to add. The app will be updated semi-yearly with user-suggested car brands!
if you wish to support project Caracam please visit my Patreon or my Cashapp!!
Intended uses & limitations
NOT FOR COMMERCIAL USE OUTSIDE OF OFFICIAL CARACAM MOBILE APP
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.0308 | 1.0 | 5362 | 3.6948 | 0.2491 |
| 2.694 | 2.0 | 10725 | 2.2586 | 0.5199 |
| 2.4475 | 3.0 | 16086 | 1.9156 | 0.5852 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cpu
- Datasets 2.16.1
- Tokenizers 0.15.0