Image Recognition Deep Learning Model
Budget: $90 – $100 USD
So the deep learning architecture is a triplet-based deep learning model to detect similar ballot images, where the goal is to learn an embedding space that minimizes the distance between similar images (anchor and positive) and maximizes the distance between dissimilar images (anchor and negative).
Key Components:
1. Triplet Loss: The core of this architecture is the triplet loss function. This loss ensures that the network learns to embed images such that:
• The distance between the anchor (reference image) and positive (same-class image) is smaller than the distance between the anchor and negative (different-class image) by a predefined margin.
The margin ensures that the difference in distances between positive and negative pairs is sufficient.
2. Shared Embedding Network:
• DenseNet121 is used as the backbone for feature extraction. It outputs a 1D vector for each image, which represents the image in a high-dimensional feature space.
• The same network is used for all three images in the triplet (anchor, positive, negative) to ensure that the model learns a consistent embedding space for all images.
3. Classification Head:
• A classification head is used to classify whether the anchor-positive pair or the anchor-negative pair is similar or not. This head consists of a small MLP (Multi-Layer Perceptron) with 2 fully connected layers and Dropout for regularization.
• The output is a sigmoid-activated logit, which helps in determining if the pair is a match (positive) or not (negative).
4. Training Setup:
• Batch Size and Epochs: The model is trained with a batch size of 64 and for a number of epochs (30).
• The triplet loss and binary cross-entropy loss are combined to optimize the network. The binary cross-entropy helps in classifying whether the pair of images is similar or not, and the triplet loss ensures the embeddings for positive and negative pairs are correctly.
5. Data Preparation:
• The model is trained using triplet data, where each sample consists of three images: an anchor, a positive image (same class), and a negative image (different class).
• The build_triplets_from_pairs function generates triplets from pairs of similar images, ensuring that each triplet consists of an anchor, a positive, and a negative.
6. Evaluation:
• After training, the model’s performance is evaluated based on the learned embeddings. The distances between embeddings of anchor and other images are computed, and a threshold is applied to classify the pairs as either similar (distance < threshold) or dissimilar (distance > threshold).
• Evaluation metrics such as accuracy, precision, recall, F1 score, and classification report are used to assess the model’s performance on the test data.
Purpose:
The goal of this architecture is to learn ballot image representations (embeddings) that map similar images close together and dissimilar images far apart in the feature space. I have already built the model but the accuracy is very less, i need your help to improve the model to get high accuracy of above 94.
Key Components:
1. Triplet Loss: The core of this architecture is the triplet loss function. This loss ensures that the network learns to embed images such that:
• The distance between the anchor (reference image) and positive (same-class image) is smaller than the distance between the anchor and negative (different-class image) by a predefined margin.
The margin ensures that the difference in distances between positive and negative pairs is sufficient.
2. Shared Embedding Network:
• DenseNet121 is used as the backbone for feature extraction. It outputs a 1D vector for each image, which represents the image in a high-dimensional feature space.
• The same network is used for all three images in the triplet (anchor, positive, negative) to ensure that the model learns a consistent embedding space for all images.
3. Classification Head:
• A classification head is used to classify whether the anchor-positive pair or the anchor-negative pair is similar or not. This head consists of a small MLP (Multi-Layer Perceptron) with 2 fully connected layers and Dropout for regularization.
• The output is a sigmoid-activated logit, which helps in determining if the pair is a match (positive) or not (negative).
4. Training Setup:
• Batch Size and Epochs: The model is trained with a batch size of 64 and for a number of epochs (30).
• The triplet loss and binary cross-entropy loss are combined to optimize the network. The binary cross-entropy helps in classifying whether the pair of images is similar or not, and the triplet loss ensures the embeddings for positive and negative pairs are correctly.
5. Data Preparation:
• The model is trained using triplet data, where each sample consists of three images: an anchor, a positive image (same class), and a negative image (different class).
• The build_triplets_from_pairs function generates triplets from pairs of similar images, ensuring that each triplet consists of an anchor, a positive, and a negative.
6. Evaluation:
• After training, the model’s performance is evaluated based on the learned embeddings. The distances between embeddings of anchor and other images are computed, and a threshold is applied to classify the pairs as either similar (distance < threshold) or dissimilar (distance > threshold).
• Evaluation metrics such as accuracy, precision, recall, F1 score, and classification report are used to assess the model’s performance on the test data.
Purpose:
The goal of this architecture is to learn ballot image representations (embeddings) that map similar images close together and dissimilar images far apart in the feature space. I have already built the model but the accuracy is very less, i need your help to improve the model to get high accuracy of above 94.