{"id":18463671,"url":"https://github.com/alvii147/smartwardrobe","last_synced_at":"2026-05-07T11:31:41.702Z","repository":{"id":134304078,"uuid":"212214971","full_name":"alvii147/SmartWardrobe","owner":"alvii147","description":"Program that identifies pieces of clothing","archived":false,"fork":false,"pushed_at":"2020-11-17T06:13:04.000Z","size":46451,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-28T16:55:52.739Z","etag":null,"topics":["keras","matplotlib","pillow","pyqt5","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/alvii147.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-10-01T22:59:35.000Z","updated_at":"2023-09-27T21:45:00.000Z","dependencies_parsed_at":"2023-03-25T20:02:42.232Z","dependency_job_id":null,"html_url":"https://github.com/alvii147/SmartWardrobe","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/alvii147/SmartWardrobe","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alvii147%2FSmartWardrobe","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alvii147%2FSmartWardrobe/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alvii147%2FSmartWardrobe/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alvii147%2FSmartWardrobe/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/alvii147","download_url":"https://codeload.github.com/alvii147/SmartWardrobe/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alvii147%2FSmartWardrobe/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32735086,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-07T02:14:30.463Z","status":"ssl_error","status_checked_at":"2026-05-07T02:14:29.405Z","response_time":62,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["keras","matplotlib","pillow","pyqt5","tensorflow"],"created_at":"2024-11-06T09:07:37.378Z","updated_at":"2026-05-07T11:31:41.682Z","avatar_url":"https://github.com/alvii147.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Smart Wardrobe\n\n## Overview\n***Smart Wardrobe*** detects and categorizes clothing into one of 10 groups:  \n- T-Shirt/Top\n- Trousers\n- Pullover\n- Dress\n- Coat\n- Sandals\n- Shirt\n- Sneakers\n- Bag\n- Ankle Boots\n\n## Command Line Mode\nCommand line mode can be used to train the model, test the model using images in the img directory, or display the images in the img directory.\n```\n$ python SmartWardrobe.py\n\nEnter command\u003e help\n\nCOMMANDS\n        train                   train dataset\n        test                    predict images in img folder\n        display [FILE NAME]     display image file\n```\n### Training\nThe model is trained using a **multi-layered neural network** in **Tensorflow** and **Keras.**\n```\nmodel = tf.keras.models.Sequential()\nmodel.add(tf.keras.layers.Flatten())\nmodel.add(tf.keras.layers.Dense(784, activation = tf.nn.relu))\nmodel.add(tf.keras.layers.Dense(196, activation = tf.nn.relu))\nmodel.add(tf.keras.layers.Dense(10, activation = tf.nn.softmax))\n...\nmodel.compile(optimizer = optimizer, loss = lossFunction, metrics = [\"acc\", \"mse\"])\n...\nhistory = model.fit(x_train, y_train, epochs = epochsCount)\n```\nOptimizing using **stochastic gradient descent,** using a **sparse categorical entropy** loss function and with 3 epochs the accuracy stands at roughly **73%.**\n```\n$ python SmartWardrobe.py\n\nEnter command\u003e train\n...\nEpoch 1/3\n1875/1875 [==============================] - 13s 7ms/step - loss: 1.8391 - accuracy: 0.5231\nEpoch 2/3\n1875/1875 [==============================] - 18s 10ms/step - loss: 1.0088 - accuracy: 0.6804\nEpoch 3/3\n1875/1875 [==============================] - 23s 12ms/step - loss: 0.7522 - accuracy: 0.7375\n```\n\n### Testing\n```\n$ python SmartWardrobe.py\n\nEnter command\u003e test\n\n1). hoodie.jpg is an image of a COAT\n\n2). sandal.jpg is an image of SNEAKERS\n\n3). shoe.jpg is an image of ANKLE BOOTS\n\n4). tshirt.jpg is an image of a T-SHIRT/TOP\n\n5). tshirtwhite.jpg is an image of a COAT\n```\n\u003cimg src=\"img/hoodie.jpg\" alt=\"hoodieimg\" width=\"100\"/\u003e  \nhoodie.jpg  \n  \n\u003cimg src=\"img/sandal.jpg\" alt=\"sandalimg\" width=\"100\"/\u003e  \nsandal.jpg  \n  \n\u003cimg src=\"img/shoe.jpg\" alt=\"shoeimg\" width=\"100\"/\u003e  \nshoe.jpg  \n  \n\u003cimg src=\"img/tshirt.jpg\" alt=\"tshirtimg\" width=\"100\"/\u003e  \ntshirt.jpg  \n  \n\u003cimg src=\"img/tshirtwhite.jpg\" alt=\"tshirtwhiteimg\" width=\"100\"/\u003e  \ntshirtwhite.jpg  \n\n## GUI Mode\nBuilt with **PyQt5,** the GUI supports the same functionality as the command line mode and more. In addition to testing images in the img directory, the GUI allows the testing of any other images. The GUI also allows selection of the loss function, the optimizer, and has a built-in terminal and image display.\n\n![GUI Snapshot](img/snapshot.JPG)\n\n## Dataset\nThe [Fashion-MNIST](https://www.kaggle.com/zalando-research/fashionmnist) dataset was used for training.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falvii147%2Fsmartwardrobe","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falvii147%2Fsmartwardrobe","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falvii147%2Fsmartwardrobe/lists"}