I am attempting to use a software called YOLOv5 in my python code for a Kaggle competition. The only thing is it requires you not to use the internet. I have the yolov5 file already loaded into the kaggle code. but am unsure how to call in in my code.
I've tried loading it in the following way but keep getting errors.
import torch
torch.save('yolov5x6.pt', 'yolov5')
yolov5x6_model = torch.load('yolov5')
Yolov5 is a follow up version of yolo which is a neural network library in c language, also known as Darknet created by pjreddie.
It is an object detector model which can be trained to recognise objects in images or videos.
If you just want to detect some daily life object then you can just run inference on images/videos using python and trained weights and config file. You will find these files under the pretrained checkpoints section at the following link.
https://github.com/ultralytics/yolov5
Related
I'm trying to run the Speech Recognition Project by Udacity (you can check the whole program in here) on Google Colab but I have a problem because Google Colab doesn't support Tensorflow 1.x anymore. I've tried running it before and it ran smoothly without any issues but now it can't.
Here are some things I have tried so far:
Scene 1
Using tensorflow.compat.v1 as instructed in TF2 migration from tensorflow
it worked when training the model
but failed to predict the model
Scene 2
I also have tried to convert the whole project using !tf_upgrade_v2 but there's no change (.py and .ipnyb still the same code)
Scene 3
Installing specific tensorflow (1.15), keras(2.0.5) and h5py(2.7.0) in Colab,
it worked too but when I predict the model it gave no transcription
I really need help how to fix this and able to execute this.
I'm trying to use both BertTokenizer and BertForTokenClassification offline. The goal of the project is to use these pretrained models and apply them to my own dataset for an NLP task. Due to network and security limitations, I am not able to install the transformers library from HuggingFace. I am only able to use PyTorch.
At this point, I have downloaded and saved the following bert-base-uncased files from the HuggingFace website to a local directory:
config.json
python_model.bin
vocab.txt
I've used the transformers library before, so I'm familiar with initializing the models from local files using something like BertTokenizer.from_pretrained('/path/to/local'). However, since I'm not able to install the package and call the model classes, I don't know how to use these downloaded local files in a similar manner. How do I use these local files to use BertTokenizer and BertForTokenClassification?
I've been instructed to use the following link to implement this: https://pytorch.org/tutorials/beginner/saving_loading_models.html
I'm trying to use tensorflow serving. However, any of the pretrained models that are available for download (like from here: the TF detection zoo) don't have any files in the saved_models/variables directory that is required by the serving model.
How do you create the files required in the saved_models/variables directory using the pretrained models available from the detection model zoo?
There is some information from the official documentation, but it doesn't cover my use case of converting a pretrained model to be served.
Other things I've tried is to use the tensorflow serving examples. However, most of the existing documentation uses the Resent implementation as an example, and the pretrained model for resnet has been removed by Tensorflow. This is the linked that tutorials use, note that there's no direct link to download the models. As an aside, but an additional funsy, the python examples in the Tensorflow Serving repo don't work with Tensorflow 2.0.
It appears that this link may be useful in the conversion: https://github.com/tensorflow/models/issues/1988
Ok, as of the time of writing the object detection tutorials only support tensorflow 1.12.0.
It's a little difficult to do this because it's so multitiered, but you need to:
clone the tensorflow open model zoo
patch the models/research/object_detection/exporter.py according to these instructions. Alternatively, you can use this patch which are the aforementioned instructions.
Follow the object detection installation instructions as found here in your cloned repo. It's important to both follow the protobuf compilation steps AND update your python path for the slim libraries.
Follow the instructions for exporting a trained model for inference. Note that the important part of the instruction that is important is that the downloaded model will come will three model.ckpt filenames. The filename that needs to be passed into the exporting script is the base filename of these three filenames. So if the three files are /path/to/model.ckpt.data-00000-of-00001, /path/to/model.ckpt.meta, and /path/to/model.ckpt.index, the parameter to pass into to the script is: /path/to/model.ckpt
Enjoy your new model!
Working on TensorFlow object detection API, I trained faster RCNN model for ship recognition at my PC on tensorFlow (GPU) and generated frozen_inference_graph.pb file.
Now I want to use this model on my laptop which has tensorFlow(CPU).
How could I use this frozen_inference_graph.pb file.
Is there any other way?
what files do I need?
simply run the detector, you need to follow the guide
I am using Torch7 library for implementing neural networks. Mostly, I rely on pre-trained models. In Lua I use torch.load function to load a model saved as torch .t7 file. I am curious about switching to PyTorch( http://pytorch.org) and I read the documents. I couldn't find any information regarding the mechanisms to load a pre-trained model. The only relevant information I was able to find is this page:http://pytorch.org/docs/torch.html
But the function torch.load described in the page seems to load a file saved with pickle. If someone has additional information on loading .t7 models in PyTorch, please share it here.
The correct function is load_lua:
from torch.utils.serialization import load_lua
x = load_lua('x.t7')
As of PyTorch 1.0 torch.utils.serialization is completely removed. Hence no one can import models from Lua Torch into PyTorch anymore. Instead, I would suggest installing PyTorch 0.4.1 through pip in a conda environment (so that you can remove it after this) and use this repo to convert your Lua Torch model to PyTorch model, not just the torch.nn.legacy model that you cannot use for training. Then use PyTorch 1.xx to do whatever with it. You can also train your converted Lua Torch models in PyTorch this way :)