{"id":31445953,"url":"https://github.com/semanticclimate/image_classification","last_synced_at":"2025-09-30T23:53:09.386Z","repository":{"id":307651403,"uuid":"1030251476","full_name":"semanticClimate/image_classification","owner":"semanticClimate","description":null,"archived":false,"fork":false,"pushed_at":"2025-08-01T10:47:02.000Z","size":3606,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-01T12:51:01.616Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/semanticClimate.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-08-01T10:27:17.000Z","updated_at":"2025-08-01T10:47:05.000Z","dependencies_parsed_at":"2025-08-01T12:51:05.063Z","dependency_job_id":"daafae34-24a9-4679-99ae-518517c6276c","html_url":"https://github.com/semanticClimate/image_classification","commit_stats":null,"previous_names":["semanticclimate/image_classification"],"tags_count":null,"template":false,"template_full_name":"semanticClimate/notebook-template","purl":"pkg:github/semanticClimate/image_classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/semanticClimate%2Fimage_classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/semanticClimate%2Fimage_classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/semanticClimate%2Fimage_classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/semanticClimate%2Fimage_classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/semanticClimate","download_url":"https://codeload.github.com/semanticClimate/image_classification/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/semanticClimate%2Fimage_classification/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":277773147,"owners_count":25874567,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-30T02:00:09.208Z","response_time":75,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-09-30T23:53:07.457Z","updated_at":"2025-09-30T23:53:09.374Z","avatar_url":"https://github.com/semanticClimate.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Exploring Vision Transformers in Practice\n\n\u003ca href=\"https://colab.research.google.com/github/semanticClimate/image_classification/blob/main/Vision_Transformer_AH.ipynb\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\nDOI Zenodo badge: \n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.16734915.svg)](https://doi.org/10.5281/zenodo.16734915)\n\nCitation\n\nHamadani, A., Kumari, R., Simon, W., Yadav, G., \u0026 Murray-Rust, P. (2025). Exploring Vision Transformers in Practice (0.1). Zenodo. https://doi.org/10.5281/zenodo.16734915\n\nDescription: \n\nIn this notebook, we fine-tune a pretrained ViT on the Fashion Products Small dataset from Hugging Face, which contains 42,700 e-commerce images of apparel and accessories (e.g., shirts, watches) along with metadata. The data is split into training, validation, and testing. Rather than training from scratch, the notebook uses ViT-Base-Patch16-224-in21k from Hugging Face’s Transformers library.\n\n**Vision Transformers in Academic Research**\n- Understanding Visual Texts\n- Analyzing Literature Reviews\n- Multimodal Literature Review\n- Sorting Large Collections\n- Symbolism Analysis in Art\n- Visual Semantic Mapping\n\n[Link to Notebook](https://colab.research.google.com/drive/1K0Dam1Pxi2YtruwcCe1XgwL_pLtBWJHP?usp=sharing)\n\nReviewers \u0026 review process: \\\u003cAdd reviewers and review process link\\\u003e \n\n---\n\nSoftware citation information: [CITATION.cff](CITATION.cff)\n\nLicense: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ | License information: [LICENSE](LICENSE)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsemanticclimate%2Fimage_classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsemanticclimate%2Fimage_classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsemanticclimate%2Fimage_classification/lists"}