Object-Recognition-Detection

Team Number : 43
Ali Osama, 20140247
Taha Magdy, 20140222
We have uploaded the code only, without the dataset becuase it is so large to upload.
Datasets used: COCO & CIFAR-100.

Some Details

We have made two models:

The first model is Object detection using COCO dataset. Because we have found it so big and it needs a lot of computation power, so we have downloaded a pre-trained model, then we added a new feature, which is we take a stream of images from the camera using OpenCV then we fed those images to the model before we display it. To try it : run the jupyter notebook object_detection_runMe.ipynb
The second model is for an images classifier using CIFAR-100 dataset. We have built from scratch and traind it. To try it : run the jupyter notebook Multi-Class Classification With Keras.ipynb

Name		Name	Last commit message	Last commit date
Latest commit History 23 Commits
anchor_generators		anchor_generators
box_coders		box_coders
builders		builders
core		core
data		data
data_decoders		data_decoders
g3doc		g3doc
matchers		matchers
meta_architectures		meta_architectures
models		models
protos		protos
samples		samples
test_images		test_images
utils		utils
BUILD		BUILD
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
Multi-Class Classification With Keras.ipynb		Multi-Class Classification With Keras.ipynb
README.md		README.md
create_pascal_tf_record.py		create_pascal_tf_record.py
create_pascal_tf_record_test.py		create_pascal_tf_record_test.py
create_pet_tf_record.py		create_pet_tf_record.py
eval.py		eval.py
eval_util.py		eval_util.py
evaluator.py		evaluator.py
export_inference_graph.py		export_inference_graph.py
exporter.py		exporter.py
exporter_test.py		exporter_test.py
object_detection_runMe.ipynb		object_detection_runMe.ipynb
train.py		train.py
trainer.py		trainer.py
trainer_test.py		trainer_test.py