| | --- |
| | license: apache-2.0 |
| | base_model: google/vit-base-patch16-224-in21k |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: lens-3 |
| | results: [] |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # Camera Lens Focal Length |
| | This model predicts the focal length that the camera lens used to capture an image. It takes in an image and returns one of the following labels: |
| | - ULTRA-WIDE |
| | - WIDE |
| | - MEDIUM |
| | - LONG-LENS |
| | - TELEPHOTO |
| |
|
| | ### How to use |
| | ```python |
| | from transformers import pipeline |
| | |
| | pipe = pipeline("image-classification", model="tonyassi/camera-lens-focal-length") |
| | result = pipe('image.png') |
| | |
| | print(result) |
| | ``` |
| |
|
| | ## Dataset |
| | Trained on a total of 5000 images. 1000 images from each label. Images were taken from popular Hollywood movies. |
| |
|
| | ### ULTRA-WIDE |
| |  |
| |
|
| | ### WIDE |
| |  |
| |
|
| | ### MEDIUM |
| |  |
| |
|
| | ### LONG-LENS |
| |  |
| |
|
| | ### TELEPHOTO |
| |  |
| |
|
| | ## Model description |
| | This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k). |
| |
|
| | ### Training hyperparameters |
| | The following hyperparameters were used during training: |
| | - learning_rate: 2e-05 |
| | - train_batch_size: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 2 |
| | - total_train_batch_size: 16 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_ratio: 0.1 |
| | - num_epochs: 5 |
| |
|
| | ### Framework versions |
| | - Transformers 4.35.0 |
| | - Pytorch 2.1.0+cu118 |
| | - Datasets 2.14.6 |
| | - Tokenizers 0.14.1 |
| |
|
