| # Model Card for ResNet-152 Text Detector | |
| This model was trained with the intent to quickly classify whether or not an image contains legible text or not. It was trained as a binary classification problem on the COCO-Text dataset together with some images from LLaVAR. This came out to a total of ~140k images, where 50% of them had text and 50% of them had no legible text. | |
| # Model Details | |
| ## How to Get Started with the Model | |
| ```python | |
| from PIL import Image | |
| import requests | |
| import torch | |
| from transformers import AutoImageProcessor, AutoModelForImageClassification | |
| model = AutoModelForImageClassification.from_pretrained( | |
| "miguelcarv/resnet-152-text-detector", | |
| ) | |
| processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50", do_resize=False) | |
| url = "http://images.cocodataset.org/train2017/000000044520.jpg" | |
| image = Image.open(requests.get(url, stream=True).raw).convert('RGB').resize((300,300)) | |
| inputs = processor(image, return_tensors="pt").pixel_values | |
| with torch.no_grad(): | |
| outputs = model(inputs) | |
| logits_per_image = outputs.logits | |
| probs = logits_per_image.softmax(dim=1) | |
| print(probs) | |
| # tensor([[0.1085, 0.8915]]) | |
| ``` | |
| # Training Details | |
| - Trained for three epochs | |
| - Resolution: 300x300 | |
| - Learning rate: 5e-5 | |
| - Optimizer: AdamW | |
| - Batch size: 64 | |
| - Trained with FP32 |