https://github.com/aman-17/tshirt-segmentation-using-mrcnn
Tshirt instance segmentation using MRCNN.
https://github.com/aman-17/tshirt-segmentation-using-mrcnn
artificial-intelligence computer-vision deep-learning image-segmentation mrcnn rcnn segmentation
Last synced: 7 months ago
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Tshirt instance segmentation using MRCNN.
- Host: GitHub
- URL: https://github.com/aman-17/tshirt-segmentation-using-mrcnn
- Owner: aman-17
- License: other
- Created: 2022-01-12T10:59:08.000Z (almost 4 years ago)
- Default Branch: develop_rcnn
- Last Pushed: 2022-01-12T11:05:37.000Z (almost 4 years ago)
- Last Synced: 2025-01-25T15:11:12.611Z (9 months ago)
- Topics: artificial-intelligence, computer-vision, deep-learning, image-segmentation, mrcnn, rcnn, segmentation
- Homepage:
- Size: 72.4 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Mask R-CNN for Object Detection and Segmentation
This is an implementation of [Mask R-CNN](https://arxiv.org/abs/1703.06870) on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.
## Requirements
Python 3.6.9 and other common packages listed in `requirements.txt`. **Do not install different versions of tensorflow and keras.**
## Installation
1. Clone this repository
2. Install dependencies
```bash
pip3 install -r requirements.txt
```
3. Run train.py file
```bash
python3 train.py
```
# Getting Started
* Download the [tshirt dataset .zip file](https://drive.google.com/file/d/1aGLstfgFbBMZih_OaBaiuB40ZI8DxLpK/view?usp=sharing) from here and extract it. Annotations file is inside the folder with name ``` annotations_train.json ```
* ([model.py](mrcnn/model.py), [utils.py](mrcnn/utils.py), [config.py](mrcnn/config.py)): These files contain the main Mask RCNN implementation.
* ([train.py](train.py)): To train the model use this file. Use the same path for _dataset_train_ and _dataset_val_ in **train.py** as we are using 10% of images from training dataset for measuring validation loss.
* ([train_mask_rcnn_demo.py](demo/train_mask_rcnn_demo.py)) is used give no. of classes, batch size, steps_per_epoch, epochs, gpu_count, images_per_gpu. Change them as per your convinence wrt your GPU. You can change epochs in ```train_head``` and ```train_all_layers``` functions in `line267` and `line274` respectively.
* For Downloading the dataset
pip install gdown
```gdown --fuzzy https://drive.google.com/file/d/1aGLstfgFbBMZih_OaBaiuB40ZI8DxLpK/view?usp=sharing```