I'm interested in training a YOLOv5 model. Currently, I'm using Roboflow to annotate and export the data into YOLOv5 format. I'm also using Roboflow's Colab Notebook for YOLOv5.
However, I'm not familiar with many of the commands used in the Roboflow Colab Notebook. I found on here that there appears to be a much more "Pythonic" way of using and manipulating the YOLOv5 model, which I would be much more familiar with.
My questions regarding this are as follows:
Is there an online resource that can show me how to train the YOLOv5 and extract results after importing the model from PyTorch with the "Pythonic" version (perhaps a snippet of code right here on StackOverflow would help)? The official documentation that I could find (here) also uses the "non-Pythonic" method for the model.
Is there any important functionality I would lose if I were to switch to this "Pythonic" method of using YOLOv5?
I found nothing in the documentation that suggests otherwise, but would I need to export my data in a different format from Roboflow for the data to be able to train the "Pythonic" model?
Similar to question 1), is there anywhere that can guide me how to use the trained model on test images? Do I simply do prediction=model(my_image.jpg)? What if I want predictions on multiple images at once?
Any guidance would be appreciated. Thanks!
You can use the GitHub repository of ultralytics to do what you want; if you want to understand the process, check out the train.py file to get a better understanding. There isn't a straightforward explanation you just have to learn by yourself.
For the training: if you want to write the code by yourself it will need a lot of ML knowledge; that's why train.py exist, same for test.py and export.py.
Related
I am currently following this github repo: https://github.com/Tianxiaomo/pytorch-YOLOv4 to implement a pytorch YOLOv4 model. However, this repo did not provide test.py/val.py. We know that YOLOv5 does provide val.py which purpose is to let us validate out trained result on validation dataset and testing dataset.
So I want to write a test.py/val.py for this purpose, but I am have really no idea how to write. Anyone have experience on how to write, can you please share some idea to write it?
You can take a look at YOLOv4-Scaled official repo, they use official pycocotools API for evaluation.
i understand that gpt2 is based on the transformer architecture but where is the source code, there are limited resources and no tutorial on how to write one..
I am new to NLP and also if i had to generate novels, would training the transformer on multiple novels help or one?
I think the best way to train GPT and other trasnformers is by using the library https://huggingface.co/docs/transformers. They also have a course that can help you to familiarize with the topic: https://huggingface.co/course/
Yes, transformer models, if they are not too large, can be trained on Colab.
And yes, GPT-like models can be trained to generate novels, but only short ones (like several paragraphs), because almost all such models can work only with texts of limited length.
Yes, it is possible, and it would be better if you use GPU for training. make sure modify num_train_epochs, per_device_train_batch_size and per_gpu_train_batch_size features in TrainingArguments to prevent runtime from crashing! >> RuntimeError: CUDA out of memory
most of the time it will use the whole GPU and RAM and the notebook would crash!
I've been looking to train my own ELMo model for the past week and came across these two implementations allenai/bilm-tf & allenai/allennlp. I've been facing a few roadblocks for a few techniques I've tried and would like to clarify my findings, so that I can get a clearer direction.
As my project revolves around healthcare, I would like to train the embeddings from scratch for better results. The dataset I am working on is MIMIC-III and the entire dataset is stored in one .csv, unlike 1 Billion Word Language Model Benchmark (data used in tutorials) where files are stored in separate .txt files.
I was following this "Using ELMo as a PyTorch Module to train a new model" tutorial but I figured out that one of the requirements is a .hdf5 weights_file.
(Question) Does this mean that I will have to train a bilm model first to get .hdf5 weights to input? Can I train an ELMo model from scratch using allennlp.modules.elmo.Elmo? Is there any other way where I can train a model this way with an empty .hdf5 as I was able to run this successfully with tutorial data.
(Question) What will be the best method for me to train my embeddings? (PS: some methods I've tried are documented below). In my case where I will probably need a custom DatasetReader, rather than converting the csv to txt files, wasting memory.
Here, let me go into the details of other methods I have tried so far. Serves as a backstory to the main question of what may be the best technique. Please let me know if you know of any other methods to train my own ELMo model, or if one of the following methods are preferred over the others.
I've tried training a model using the allennlp train ... command by following this tutorial. However, I was unable to run with tutorial data due to the following error which I am still unable to solve.
allennlp.common.checks.ConfigurationError: Experiment specified GPU device 1 but there are only 1 devices available.
Secondly, this is a technique that I found but have not tried. Similar to the technique above it uses the allennlp train ... command but instead I use allenai/allennlp-template-config-files as a template and modify the Model and DatasetReader.
Lastly, I tried using the TensorFlow implementation allenai/bilm-tf following tutorials like this. However, I would like to avoid this method as TF1 is quite outdated. Besides receiving tons of warnings, I faced an error for CUDA as well.
2021-09-14 17:31:36.222624: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 18.45M (19346432 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
I'm new to this topic, so forgive me my lack of knowledge. There is a very good model called inception resnet v2 that basically works like this, the input is an image and outputs a list of predictions with their positions and bounded rectangles. I find this very useful, and I thought of using the already worked model in order to recognize things that it now can't (for example if a human is wearing a mask or not). Yes, I wanted to add a new recognition class to the model.
import tensorflow as tf
import tensorflow_hub as hub
mod = hub.load("https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1")
mod is an object of type
tensorflow.python.training.tracking.tracking.AutoTrackable, reading the documentation (that was only available on the source code was a bit hard to understand without context)
and I tried to inspect some of it's properties in order to see if I could figure it out by myself.
And well, I didn't. How can I see the network, the layers, the weights? the fit methods, Is it's all abstracted away?. Can I convert it to keras? I want to experiment with it, see if I can modify it, and see if I could export the model to another representation, for example pytorch.
I wanted to do this because I thought it'd be better to modify an already working model instead of creating one from scratch. Also because I'm not good at training models myself.
I've run into this issue too. Tensorflow hub guide says:
This error frequently arises when loading models in TF1 Hub format with the hub.load() API in TF2. Adding the correct signature should fix this problem.
mod = hub.load(handle).signatures['default']
As an example, you can see this notebook.
You can dir the loaded model asset to see what's defined on it
m = hub.load(handle)
dir(model)
As mentioned in the other answer, you can also look at the signatures with print(m.signatures)
Hub models are SavedModel assets and do not have a keras .fit method on them. If you want to train the model from scratch, you'll need to go to the source code.
Some models have more extensive exported interfaces including access to individual layers, but this model does not.
Long story short:
How to prepare data for lstm object detection retraining of the tensorflow master github implementation.
Long story:
Hi all,
I recently found implementation a lstm object detection algorithm based on this paper:
http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Mobile_Video_Object_CVPR_2018_paper.pdf
at the tensorflow model master github repository (https://github.com/tensorflow/models/tree/master/research/lstm_object_detection)
I would like to retrain this implementation on my own dataset to evaluate the lstm improvement to other algorithms like SSD. But I keep struggling on how to prepare the data for the training. I've tried the config file of the authors and tried to prepare the data similar to the object-detection-api and also tried to use the same procedure as the inputs/seq_dataset_builder_test.py or inputs/tf_sequence_example_decoder_test.py does. Sadly the github Readme does not provide any information. Someone else created an issue with a similar question on the github repo (https://github.com/tensorflow/models/issues/5869) but the authors did not provide a helpful answer yet. I tried to contact the authors via email a month ago, but didn't got a response. I've also searched the internet but found no solution. Therefore I desperately write to you!
Is anybody out there who can explain how to prepare the data for the retraining and how to actually run the retraining.
Thank you for reading, any help is really appreciated!