File size: 1,296 Bytes
19a7cd4 010be03 19a7cd4 010be03 19a7cd4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 |
# 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([[0.0767, 0.9233]])
```
# Training Details
- Trained for three epochs
- Resolution: 256x256
- Learning rate: 5e-5
- Optimizer: AdamW
- Batch size: 64
- Trained with FP32 |