Dogs Breed Image Classification V2

🚀 Dogs-Breed-Image-Classification-V2

This model is a fine - tuned version of microsoft/resnet - 152 for dog breed image classification, achieving high accuracy on the Standford dogs dataset.

🚀 Quick Start

This model is a fine - tuned version of microsoft/resnet-152 on the Standford dogs dataset. It achieves the following results on the evaluation set:

• Loss: 1.0115
• Accuracy: 0.8408

✨ Features

• Based on the well - known microsoft/resnet-152 architecture.
• Fine - tuned on the Standford dogs dataset for high - accuracy dog breed classification.

📚 Documentation

Model description

• Link to the fine - tuned model using resnet - 50
• Link to the fine - tuned model using resnet - 101

This model was trained using dataset from Kaggle - Standford dogs dataset

Quotes from the website: The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine - grained image categorization. It was originally collected for fine - grain image categorization, a challenging problem as certain dog breeds have near identical features or differ in colour and age.

citation: Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao and Li Fei - Fei. Novel dataset for Fine - Grained Image Categorization. First Workshop on Fine - Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. [pdf] [poster] [BibTex]

Secondary: J. Deng, W. Dong, R. Socher, L. - J. Li, K. Li and L. Fei - Fei, ImageNet: A Large - Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009. [pdf] [BibTex]

Intended uses & limitations

This model is fine - tuned solely for classifying 120 species of dogs.

Training and evaluation data

75% training data, 25% testing data.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e - 06
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Framework versions

• Transformers 4.37.2
• Pytorch 2.3.0
• Datasets 2.15.0
• Tokenizers 0.15.1

📄 License

This model is released under the Apache - 2.0 license.

📦 Information Table