I am working on object detection model to identify two classes. I am using Faster RCNN on customized dataset in tensorflow api. The dataset contains 20k images (augmented) with two classes. While training the model the loss is not decreasing properly as it reach to 100k steps. It has lot of variation as shown in image. Can someone tell me where i am making mistake.
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I trained a model using images I gathered from the web. Then, when inferences were made using images newly collected from the web, performance was poor.
I am wondering how I can improve my dataset using misclassified images. Can I add all the misclassified images to the training dataset? And then do I have to collect new images?
[Edit]
I added some of the misclassified images to the training dataset, although the performance evaluation got better.
It might be worth if you could provide more info on how you trained your model, and your network architecture.
However this are some general guidelines:
You can try to diversify your images in your train set by, yes, adding new images. The more different examples you provide to your network, the higher the chance that they will be similar to images you want to obtain prediction from.
Do data augmentation, it is pretty straightforward and usually improves quite a bit the accuracy. You can have a look at this Tensorflow tutorial for Data Augmentation. If you don’t know what data augmentation is, basically is a technique to perform minor changes to your images, that is by rotating the image a bit, resizing etc. This way the model is trained to learn your images even with slight changes, which usually makes it more robust to new images.
You could consider doing Transfer Learning. The main idea here is to leverage a model that has learned on a huge dataset and use it to fine-tune your specific problem. In the tutorial I linked they show the typical workflow of transfer learning, by taking a model pretrained on the ImageNet dataset (the huge dataset), and retraining it on the Kaggle "cats vs dogs" classification dataset (a smaller dataset, like the one you could have).
Objective: I am trying to train a Tensorflow Huggingface GPT2 model (language model training from scratch)
Model Description:
Huggingface GPT2 Tensorflow Model
Attached a pic of config. Model Config
Dataset Description:
I have a large dataset (~20GB),
the data is separated into multiple text files with each new line as a training example.
I am facing two issues.
The examples are of different length and I am not sure how to make all the example sizes of same length to feed to the model.
Solutions Tried: We can either pad them, but then I am not sure how to do that in batches in Tensorflow. I searched about data-collator
Doubt: Padding would have to be done to make all the examples of equal size in the batch or across the whole dataset. And would this be with tokens or some other information. (Different Data Collators for Language Modelling etc.)
Since the data is very large, it cannot be loaded in memory at once while training. (Doing model.fit). For that I am not sure how to proceed.
Solutions: I am thinking of training and saving the model on small files but that would require manual intervention or for looping and the model would not be trained on the whole dataset in one go, so if there are other alternatives. Help would be really appreciated.
for my ML project I want to use the faster_rcnn_resnet101_kitti model from tensorflow model zoo. As the number of images in the Kitti dataset is extremely small (about 7000 images) for a deep learning practice, I was wondering how this small amount of data leads to the decent inference performance (mAP#0.5=87)? One answer I can imagine is that the network was first trained on a different, rich dataset and fine tuned on the Kitti but I am not sure about it.
I am wondering how can I find out the exact underlying training procedure (apart from pipeline.config) for the models published on TF model zoo?
Thanks
I'm training a model to detect meteors within a picture of the night sky and I have a fairly small dataset with about 85 images and each image is annotated with a bounding box. I'm using the transfer learning technique starting with the ssd_mobilenet_v1_coco_11_06_2017 checkpoint and Tensorflow 1.4. I'm resizing images to 600x600pixels during training. I'm using data augmentation in the pipeline configuration to randomly flip the images horizontally, vertically and rotate 90 deg. After 5000 steps, the model converges to a loss of about 0.3 and will detect meteors but it seems to matter where in the image the meteor is located. Do I have to train the model by giving examples of every possible location? I've attached a sample of a detection run where I tiled a meteor over the entire image and received various levels of detection (filtered to 50%). How can I improve this?detected meteors in image example
It could very well be your data and I think you are making a prudent move by improving the heterogeneity of your dataset, BUT it could also be your choice of model.
It is worth noting that ssd_mobilenet_v1_coco has the lowest COCO mAP relative to the other models in the TensorFlow Object Detection API model zoo. You aren't trying to detect a COCO object, but the mAP numbers are a reasonable aproximation for generic model accuracy.
At the highest possible level, the choice of model is largely a tradeoff between speed/accuracy. The model you chose, ssd_mobilenet_v1_coco, favors speed over accuracy. Consequently, I would reccomend you try one of the Faster RCNN models (e.g., faster_rcnn_inception_v2_coco) before you spend a signifigant amount of time preprocessing images.
I have followed this Tensorflow tutorial on transfer learning with the Inception model using my own dataset of 640x360 images. My question comes in 2 parts
1) My data set conatains 640x360 images. Is the first operation that happens a downsampling to 299x299? I ask because I have a higher res version of the same dataset and I am wondering if training with the higher resolution images will result in different performance (hopefully better)
2) When running the network (using tf.sess.run()) is my input image down-sampled to 299x299?
Note: I have seen the 299x299 resolution stat listed many places online like this one and I am confused at exactly which images its referring to; the initial training dataset images (for Inception I think it was imagenet), the transfer learning dataset (my personal one), the input image when running the CNN, or a combination of the 3.
Thanks in advance :)
The inception model will resize your image to 299x299. This can be confirmed by visualizing the tensorflow graph. If you have enough samples to do the transfer learning, the accuracy will be good enough with resizing to 299x299. But if you really want to try out the training with actual resolution, the initial input layers of the graph size needs to be changed