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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.\\n![Instance Segmentation Sample](assets/street.png)\\nThe repository includes:\\n* Source code of Mask R-CNN built on FPN and ResNet101.\\n* Training code for MS COCO\\n* Pre-trained weights for MS COCO\\n* Jupyter notebooks to visualize the detection pipeline at every step\\n* ParallelModel class for multi-GPU training\\n* Evaluation on MS COCO metrics (AP)\\n* Example of training on your own dataset\\nThe code is documented and designed to be easy to extend. If you use it in your research, please consider citing this repository (bibtex below). If you work on 3D vision, you might find our recently released [Matterport3D](https://matterport.com/blog/2017/09/20/announcing-matterport3d-research-dataset/) dataset useful as well.\\nThis dataset was created from 3D-reconstructed spaces captured by our customers who agreed to make them publicly available for academic use. You can see more examples [here](https://matterport.com/gallery/).\\n# Getting Started\\n* [demo.ipynb](samples/demo.ipynb) Is the easiest way to start. It shows an example of using a model pre-trained on MS COCO to segment objects in your own images.\\nIt includes code to run object detection and instance segmentation on arbitrary images.\\n* [train_shapes.ipynb](samples/shapes/train_shapes.ipynb) shows how to train Mask R-CNN on your own dataset. This notebook introduces a toy dataset (Shapes) to demonstrate training on a new dataset.\\n* ([model.py](mrcnn/model.py), [utils.py](mrcnn/utils.py), [config.py](mrcnn/config.py)): These files contain the main Mask RCNN implementation. \\n* [inspect_data.ipynb](samples/coco/inspect_data.ipynb). This notebook visualizes the different pre-processing steps\\nto prepare the training data.\\n* [inspect_model.ipynb](samples/coco/inspect_model.ipynb) This notebook goes in depth into the steps performed to detect and segment objects. It provides visualizations of every step of the pipeline.\\n* [inspect_weights.ipynb](samples/coco/inspect_weights.ipynb)\\nThis notebooks inspects the weights of a trained model and looks for anomalies and odd patterns.\\n# Step by Step Detection\\nTo help with debugging and understanding the model, there are 3 notebooks \\n([inspect_data.ipynb](samples/coco/inspect_data.ipynb), [inspect_model.ipynb](samples/coco/inspect_model.ipynb),\\n[inspect_weights.ipynb](samples/coco/inspect_weights.ipynb)) that provide a lot of visualizations and allow running the model step by step to inspect the output at each point. Here are a few examples:\\n## 1. Anchor sorting and filtering\\nVisualizes every step of the first stage Region Proposal Network and displays positive and negative anchors along with anchor box refinement.\\n![](assets/detection_anchors.png)\\n## 2. Bounding Box Refinement\\nThis is an example of final detection boxes (dotted lines) and the refinement applied to them (solid lines) in the second stage.\\n![](assets/detection_refinement.png)\\n## 3. Mask Generation\\nExamples of generated masks. These then get scaled and placed on the image in the right location.\\n![](assets/detection_masks.png)\\n## 4.Layer activations\\nOften it's useful to inspect the activations at different layers to look for signs of trouble (all zeros or random noise).\\n![](assets/detection_activations.png)\\n## 5. Weight Histograms\\nAnother useful debugging tool is to inspect the weight histograms. These are included in the inspect_weights.ipynb notebook.\\n![](assets/detection_histograms.png)\\n## 6. Logging to TensorBoard\\nTensorBoard is another great debugging and visualization tool. The model is configured to log losses and save weights at the end of every epoch.\\n![](assets/detection_tensorboard.png)\\n## 6. Composing the different pieces into a final result\\n![](assets/detection_final.png)\\n# Training on MS COCO\\nWe're providing pre-trained weights for MS COCO to make it easier to start. You can\\nuse those weights as a starting point to train your own variation on the network.\\nTraining and evaluation code is in `samples/coco/coco.py`. You can import this\\nmodule in Jupyter notebook (see the provided notebooks for examples) or you\\ncan run it directly from the command line as such:\\n```\\n# Train a new model starting from pre-trained COCO weights\\npython3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=coco\\n# Train a new model starting from ImageNet weights\\npython3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=imagenet\\n# Continue training a model that you had trained earlier\\npython3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=/path/to/weights.h5\\n# Continue training the last model you trained. This will find\\n# the last trained weights in the model directory.\\npython3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=last\\n```\\nYou can also run the COCO evaluation code with:\\n```\\n# Run COCO evaluation on the last trained model\\npython3 samples/coco/coco.py evaluate --dataset=/path/to/coco/ --model=last\\n```\\nThe training schedule, learning rate, and other parameters should be set in `samples/coco/coco.py`.\\n# Training on Your Own Dataset\\nStart by reading this [blog post about the balloon color splash sample](https://engineering.matterport.com/splash-of-color-instance-segmentation-with-mask-r-cnn-and-tensorflow-7c761e238b46). It covers the process starting from annotating images to training to using the results in a sample application.\\nIn summary, to train the model on your own dataset you'll need to extend two classes:\\n```Config```\\nThis class contains the default configuration. Subclass it and modify the attributes you need to change.\\n```Dataset```\\nThis class provides a consistent way to work with any dataset. \\nIt allows you to use new datasets for training without having to change \\nthe code of the model. It also supports loading multiple datasets at the\\nsame time, which is useful if the objects you want to detect are not \\nall available in one dataset. \\nSee examples in `samples/shapes/train_shapes.ipynb`, `samples/coco/coco.py`, `samples/balloon/balloon.py`, and `samples/nucleus/nucleus.py`.\\n## Differences from the Official Paper\\nThis implementation follows the Mask RCNN paper for the most part, but there are a few cases where we deviated in favor of code simplicity and generalization. These are some of the differences we're aware of. If you encounter other differences, please do let us know.\\n* **Image Resizing:** To support training multiple images per batch we resize all images to the same size. For example, 1024x1024px on MS COCO. We preserve the aspect ratio, so if an image is not square we pad it with zeros. In the paper the resizing is done such that the smallest side is 800px and the largest is trimmed at 1000px.\\n* **Bounding Boxes**: Some datasets provide bounding boxes and some provide masks only. To support training on multiple datasets we opted to ignore the bounding boxes that come with the dataset and generate them on the fly instead. We pick the smallest box that encapsulates all the pixels of the mask as the bounding box. This simplifies the implementation and also makes it easy to apply image augmentations that would otherwise be harder to apply to bounding boxes, such as image rotation.\\n To validate this approach, we compared our computed bounding boxes to those provided by the COCO dataset.\\nWe found that ~2% of bounding boxes differed by 1px or more, ~0.05% differed by 5px or more, \\nand only 0.01% differed by 10px or more.\\n* **Learning Rate:** The paper uses a learning rate of 0.02, but we found that to be\\ntoo high, and often causes the weights to explode, especially when using a small batch\\nsize. It might be related to differences between how Caffe and TensorFlow compute \\ngradients (sum vs mean across batches and GPUs). Or, maybe the official model uses gradient\\nclipping to avoid this issue. We do use gradient clipping, but don't set it too aggressively.\\nWe found that smaller learning rates converge faster anyway so we go with that.\\n## Citation\\nUse this bibtex to cite this repository:\\n```\\n@misc{matterport_maskrcnn_2017,\\n title={Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow},\\n author={Waleed Abdulla},\\n year={2017},\\n publisher={Github},\\n journal={GitHub repository},\\n howpublished={\\\\url{https://github.com/matterport/Mask_RCNN}},\\n}\\n```\\n## Contributing\\nContributions to this repository are welcome. Examples of things you can contribute:\\n* Speed Improvements. Like re-writing some Python code in TensorFlow or Cython.\\n* Training on other datasets.\\n* Accuracy Improvements.\\n* Visualizations and examples.\\nYou can also [join our team](https://matterport.com/careers/) and help us build even more projects like this one.\\n## Requirements\\nPython 3.4, TensorFlow 1.3, Keras 2.0.8 and other common packages listed in `requirements.txt`.\\n### MS COCO Requirements:\\nTo train or test on MS COCO, you'll also need:\\n* pycocotools (installation instructions below)\\n* [MS COCO Dataset](http://cocodataset.org/#home)\\n* Download the 5K [minival](https://dl.dropboxusercontent.com/s/o43o90bna78omob/instances_minival2014.json.zip?dl=0)\\n and the 35K [validation-minus-minival](https://dl.dropboxusercontent.com/s/s3tw5zcg7395368/instances_valminusminival2014.json.zip?dl=0)\\n subsets. More details in the original [Faster R-CNN implementation](https://github.com/rbgirshick/py-faster-rcnn/blob/master/data/README.md).\\nIf you use Docker, the code has been verified to work on\\n[this Docker container](https://hub.docker.com/r/waleedka/modern-deep-learning/).\\n## Installation\\n1. Clone this repository\\n2. Install dependencies\\n ```bash\\n pip3 install -r requirements.txt\\n ```\\n3. Run setup from the repository root directory\\n ```bash\\n python3 setup.py install\\n ``` \\n3. Download pre-trained COCO weights (mask_rcnn_coco.h5) from the [releases page](https://github.com/matterport/Mask_RCNN/releases).\\n4. (Optional) To train or test on MS COCO install `pycocotools` from one of these repos. They are forks of the original pycocotools with fixes for Python3 and Windows (the official repo doesn't seem to be active anymore).\\n * Linux: https://github.com/waleedka/coco\\n * Windows: https://github.com/philferriere/cocoapi.\\n You must have the Visual C++ 2015 build tools on your path (see the repo for additional details)\\n# Projects Using this Model\\nIf you extend this model to other datasets or build projects that use it, we'd love to hear from you.\\n### [4K Video Demo](https://www.youtube.com/watch?v=OOT3UIXZztE) by Karol Majek.\\n[![Mask RCNN on 4K Video](assets/4k_video.gif)](https://www.youtube.com/watch?v=OOT3UIXZztE)\\n### [Images to OSM](https://github.com/jremillard/images-to-osm): Improve OpenStreetMap by adding baseball, soccer, tennis, f\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["with open(\"train.txt\", \"w\") as f:\n"," f.write(train_text)\n","\n","with open(\"val.txt\", \"w\") as f:\n"," f.write(val_text)"],"metadata":{"id":"0HJdC_9zlPcj","executionInfo":{"status":"ok","timestamp":1727007727074,"user_tz":-330,"elapsed":5,"user":{"displayName":"Saman Khan","userId":"04820494405334513558"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["## Load pre- trained Tokenizer GP2Tokenizer\n","checkpoint = 'gpt2'\n","tokenizer = GPT2Tokenizer.from_pretrained(checkpoint)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":177,"referenced_widgets":["47bec0014cee43ba8bcc2e8a23894a45","0fabac018cea4f5f97fd42540a2927bd","8e7fb31e3fbe4a4b9f6f2a614453e31a","c889f4d6f75046e9acb0b1d7cde663f5","6eacb5bcdc444fccaa428b18736ac794","f35164244ff045b8a3c19dd8adb9f8c1","1adfe5b6309745648fc01848e9301036","3fe4c408d01746979ac5faa68d107bb5","42f190a88d634f8490f12f0a96d94680","227d5d174cd64f4d97d8f9a01eb15a2d","f4c73b3361c34c1fbec9bd6ba86c87c4","273284be5c854484a587192e87ad0b43","05689256320f49d28786e469128e1112","5ffd238465024cee989ee0426ed69689","4ed3df3101244949b96eb402395f6121","3647bc64b3784d969d106d456dca9fe9","e5fa503cd1f04d92892b5aa8d048be64","b901d90c2431441387bce1f69050dc66","f7e265c3c5bb4aa6abbc3e1b89de8937","e99ef29e3686447ea4b71da4c45afc59","352b53efa3fa4fa0b66b816bdffab449","8c6f58eb1b8a464fbd416588d80164a2","94f4e51af10841218a327ceb44bab220","5b0c869b313f4a7cb10a0aec1f6e847d","9ee4fb8dd12140f48feaa900b19881b0","130b094c72f74f9fb67bd8b6d591c4f3","0be60c946f6f4ae1acd8b4ce4abc419f","b79f693f5b3f416483fd0b28664584d6","f2944fcf9ee148b9a1eeb4a101d34ea1","dda7596723f24131ad174ca458b4e291","0fcea447affb400691b42eda64d2bec8","e39351f7c1464adbae633741c24337ff","445f375edbd1499590d7a84d30a67d6f","4836596449eb40a19978f7c0e65b77ce","cd3614d2eba148b99589000eac562685","857677cfb6d94bd3b17f36ff374a4f1d","9bdd2d3f7a164964a492a8b052e7f4dd","d68d1fdee93844fbb6293effb8a67d45","ea8861e69b8546f9820630b67d279842","edf0aef50b1043978ff38371cfc37d30","091360a0015e4032a78666b67c0541f4","40efb97ab94d4236850faf575a9a2da6","4dee5571218048ad893fe5d2840b1998","b4b45cc5154447e2b04d4346e023baa9","f465f16e54b84f8a8970c6216e2741bf","90f9dc9cbd194da1a3ec8592f2718ab6","c4d219abc74d439383ed76aff313c05c","359cc6b3a777451880a410b98fc6b73f","5b9ec1cb54f74627ac02703cdae6fc04","decdacbcbc6940bbba09f2dbb7c80311","4b4386af2e334bc99dbf832e2e6d9393","c95ad86f4bce442caf2291728a7ba25b","d586a57671784fba8f78cd4d97912d16","568de632cf3f4d0790d1c21ddbeae5f9","c43dfce1698a449085144f31be1967d2"]},"id":"wC7hXVrglP_I","executionInfo":{"status":"ok","timestamp":1727007731205,"user_tz":-330,"elapsed":4135,"user":{"displayName":"Saman Khan","userId":"04820494405334513558"}},"outputId":"3bec5507-2b79-415c-d6ee-e4a18727cc2f"},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 0%| | 0.00/26.0 [00:00 "],"text/html":["\n","
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"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["('/content/gpt_model/tokenizer_config.json',\n"," '/content/gpt_model/special_tokens_map.json',\n"," '/content/gpt_model/vocab.json',\n"," '/content/gpt_model/merges.txt',\n"," '/content/gpt_model/added_tokens.json')"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["# use Gradio App to take input and generate output:\n","\n","def generate_response(model, tokenizer, prompt, max_length=100):\n"," input_ids = tokenizer.encode(prompt, return_tensors='pt')\n","\n"," attention_mask = torch.ones_like(input_ids)\n"," pad_token_id = tokenizer.eos_token_id\n","\n"," output = model.generate(\n"," input_ids,\n"," attention_mask=attention_mask,\n"," pad_token_id=pad_token_id,\n"," max_length=max_length,\n"," num_return_sequences=1,\n"," no_repeat_ngram_size=2,\n"," do_sample=True,\n"," top_k=50,\n"," top_p=0.95,\n"," temperature=0.7,\n"," early_stopping=True\n"," )\n"," response = tokenizer.decode(output[0], skip_special_tokens=True)\n"," return response\n","\n"],"metadata":{"id":"mUySawrmrUzM","executionInfo":{"status":"ok","timestamp":1727008601834,"user_tz":-330,"elapsed":2,"user":{"displayName":"Saman Khan","userId":"04820494405334513558"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["\n","import gradio as gr\n","\n","# Load the fine-tuned model and tokenizer\n","model = GPT2LMHeadModel.from_pretrained(model_output_path)\n","tokenizer = GPT2Tokenizer.from_pretrained(model_output_path)\n","\n","iface = gr.Interface(\n"," fn=lambda prompt: generate_response(model, tokenizer, prompt, 250),\n"," inputs = \"textbox\",\n"," outputs=\"text\",\n"," title=\"GPT-2 Fine-Tuned Text Generator\",\n"," description=\"Enter a prompt and the model will generate text based on your input.\"\n",")\n","\n","iface.launch(share=True)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":591},"id":"tDij3ct5uhED","executionInfo":{"status":"ok","timestamp":1727008758191,"user_tz":-330,"elapsed":1761,"user":{"displayName":"Saman Khan","userId":"04820494405334513558"}},"outputId":"16b3e023-70c9-45f0-d5df-32f08038cd8c"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n","Running on public URL: https://fc087f7820fdecd3b1.gradio.live\n","\n","This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co./spaces)\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["

"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":[]},"metadata":{},"execution_count":20}]}]}