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--- |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: adapter-transformers |
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pipeline_tag: image-to-text |
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--- |
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# Model Card for Pixelated Captcha Digit Detection |
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## Model Details |
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- **License:** Apache-2.0 |
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- **Developed by:** Saidi Souhaieb |
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- **Finetuned from model:** YOLOv8 |
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## Uses |
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This model is designed to detect pixelated captcha digits by showing bounding boxes and extracting the coordinates of the detections. |
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## How to Get Started with the Model |
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```python |
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import torch |
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import torch.nn as nn |
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import torch.optim as optim |
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from torch.utils.data import DataLoader |
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import torchvision.transforms as transforms |
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from torchvision.datasets import ImageFolder |
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from tqdm import tqdm |
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from PIL import Image |
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import torch.nn.functional as F |
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import os |
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class CNN(nn.Module): |
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def __init__(self): |
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super(CNN, self).__init__() |
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self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1) |
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self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1) |
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self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) |
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self.pool = nn.MaxPool2d(2, 2) |
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self.fc1 = nn.Linear(64 * 4 * 4, 500) |
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self.fc2 = nn.Linear(500, 10) # 10 classes for example |
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def forward(self, x): |
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x = self.pool(F.relu(self.conv1(x))) |
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x = self.pool(F.relu(self.conv2(x))) |
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x = self.pool(F.relu(self.conv3(x))) |
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x = x.view(-1, 64 * 4 * 4) |
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x = F.relu(self.fc1(x)) |
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x = self.fc2(x) |
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return x |
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transform = transforms.Compose([ |
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transforms.Resize((32, 32)), # Adjust the size accordingly |
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transforms.ToTensor(), |
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transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) |
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]) |
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transform = transforms.Compose([ |
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transforms.Resize((32, 32)), # Adjust the size accordingly |
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transforms.ToTensor(), |
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transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) |
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]) |
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model = CNN() |
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model.load_state_dict(torch.load('models/99acc_model.pth')) |
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def predict_number(folder_path): |
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""" |
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Predict the numbers in the images in the folder |
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""" |
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predict_numbers = [] |
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for file in os.listdir(folder_path): |
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input_image = Image.open(f"temp/{file}").convert('RGB') |
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# Load and preprocess the input image |
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input_tensor = transform(input_image) |
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input_batch = input_tensor.unsqueeze(0) # Add a batch dimension |
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# Perform inference |
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with torch.no_grad(): |
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output = model(input_batch) |
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# Get the predicted class label |
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_, predicted = torch.max(output, 1) |
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# Print the predicted class label |
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print("Predicted class label:", predicted.item(), "file", file) |
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predict_numbers.append(predicted.item()) |
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return predict_numbers |
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``` |
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## Training Details |
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### Training Data |
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Pixel Digit Captcha Data [https://huggingface.co/datasets/Softy-lines/Pixel-Digit-Captcha-Data] |
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## Model Card Authors |
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[Saidi Souhaieb] |
