metadata
license: apache-2.0
tags:
- image-classification
- vision
- cinematography
- film
datasets:
- szymonrucinski/types-of-film-shots
metrics:
- accuracy
base_model: microsoft/beit-large-patch16-512
beit-large-patch16-512: types of film shots
Model description
This model is a fine-tuned version of microsoft/beit-large-patch16-512 on the szymonrucinski/types-of-film-shots dataset. It achieves the following results on the evaluation set:
- Loss: 1.2335
- Accuracy: 0.6763
usage
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
"image-classification",
model="pszemraj/beit-large-patch16-512-film-shot-classifier",
)
url = "https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/9YqYvv188ZccCMSzuv0KW.png"
image = Image.open(requests.get(url, stream=True).raw)
result = pipe(image)[0]
print(result)
try some of these:
class labels
The dataset contains the following labels:
"id2label": {
"0": "ambiguous",
"1": "closeUp",
"2": "detail",
"3": "extremeLongShot",
"4": "fullShot",
"5": "longShot",
"6": "mediumCloseUp",
"7": "mediumShot"
},
as plaintext:
ambiguous, close up, detail, extreme long shot, full shot, long shot, medium close up, medium shot
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 24414
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 6.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.0435 | 1.0 | 393 | 1.4799 | 0.4892 |
| 1.1554 | 2.0 | 786 | 1.4938 | 0.4892 |
| 1.5041 | 3.0 | 1179 | 2.1702 | 0.3597 |
| 1.0457 | 4.0 | 1572 | 1.5413 | 0.5683 |
| 0.3315 | 5.0 | 1965 | 1.0769 | 0.6978 |
| 0.2178 | 6.0 | 2358 | 1.2335 | 0.6763 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2