Thank you for your keen observation and for letting us know!
We will try our best to reduce these noise in future Blitz puzzles.
However, since the number of bad samples here is low and because the ongoing AI Blitz 13 is about to end, we think it would not be wise to update the dataset at this point.
Thank you again for reporting.
Yes! We have a function added in the Baseline notebook of Face Recognition Puzzle named
get_target_face which simply takes the target
face_no ( which will be between “00” and “99” [ in string ] ) and
def get_target_face(face_no, target_image): # Top-Left x, y corrdinates of the specific face x, y = (int(face_no))*216, (int(face_no))*216 target_face = target_image[x:x+216, y:y+216] return target_face # Showing a sample face from a sample target image sample_target_face = get_target_face("96", sample_target)
You can find more about it in the baseline. I hope this helps!
For these image-to-image translation tasks, I would really suggest you to check out this TensorFlow notebook on Image-to-image translation using GANs, they have some really good implementation on this for getting started. You can also check out this blog on the topic.
Glad to hear that you liked the challenge And yes, you are allowed to use pre-trained networks, including the one you mentioned. You are also allowed to fine-tune its weights. The things that the rule doesn’t allow is using models that are not free to use and publically available. I hope this helps. Let me know if you have any more questions.
Hi, is this possible if you can share the notebook you used to make submissions in dms ? It will help us a lot to figure out the issue. In the meantime, you can also try looking at our Getting Started notebook for sentiment classification and see if there’s any major difference in terms of what commands you are using to make a submission. I hope it helps.
Hi. Can you please share with us the steps to reproduce the error so that we can figure out the exact cause of this issue ?
Welcome back to blitz . I can understand but sadly we cannot provide the images because the challenge is based on using the embeddings to classify the sentiment. Adding images will change a whole lot to the original challenge.
Other than that I looked at your submissions and found out you were having some issues. So the exact submission format is that you first put your
submission.csv file and
original_notebook.ipynb ( the notebook you generated the predictions from ) in a zip file. This is what the final
zip file should look like -
── submission.zip ├── submission.csv └── original_notebook.ipynb
And then you can submit your
submission.zip in on the challenge page. I hope this help Let me know if you have any more questions.
The exact layer depends on the model you are using and what layer you want to extract the features from. Commonly, for transfer learning, it is the layer just before the fully connected layer. So for ex. in
vgg16, it’s the last max-pooling layer. Same with
resnet18, the features are extracted from the layer before the fully connected layer.
Here’s another blog you can read if you want to learn more about vgg16 feature extraction. I hope this helps
So, to generate the embeddings, we first took all of the images which had their corresponding labels and put them into ( for ex.
resnet18 ) model, and then we extracted the features of the images from a certain layer of the model. This blog is also quite good if you want to understand the whole process.
Let me know if you have any questions. Enjoy Blitz
Apologies for the late reply here. You can freely use publicly available pre-trained models & embeddings for submission in Blitz XII. But use of external raw data is not allowed. The challenge rule generally prohibits the use of other public/private datasets which you can use to train your model or improve the scores. I hope this helps. Let me know if you still have any doubts
Hi, it’s fixed now!
For the -
- Why score = 0 & Why " Generating Predictions" has the variable ‘n’ in two for-cycles ?
I looked into the issue and turns out there were few mistakes we made in our baselines such as -
Not loading the model weights after training it.
cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth")
Bounding boxes were in the wrong format
The right format for bounding boxes is
(x, y, w, h)instead of
(x1, y1, x2, y2)( which detectron2 outputted )
As for the -
- and “image_id”:n+1?
image_id helps the evaluator identify predictions for each image. So there are 1000 unique images ids because there are 1000 testing images.
To know what is the
image_id of each image, it is simply the file number ( for ex.
345.jpg ) + 1 because the
image_id starts from 1 in our ground truth file.
I hope this helps, the getting started notebook is also updated! let me know if you still had any doubts
Thanks for letting us know I am looking into the notebook and will give you updates asap
Hi, it should be working now
Thanks for letting us know! We are looking into the issue
Yes, you can make submissions from your own/local machine, but the submission has to be a
.ipynb notebook. In case you want to import a custom python file, you can add that python file in the
assets directory and then import it in the
.ipynb notebook. I hope this helps :), let me know if you have any doubts!
Hi, can you please tell which specific puzzle you are having issues with ? We are looking into the issue however an another way you can make the submission is by wrapping up all necessary files in a zip file such such that the zip file contents tree looks something like this -
Using Docking ISS puzzle as an example
submission.zip ├── assets │ └── submission.csv └── original_notebook.ipynb
original_notebook.ipynbis the notebook you used to train & make the predictions.
submisison.csvcontains the predictions of the puzzle ( the format can be different for different puzzles ).
After that you can simply submit the zip file by running
aicrowd submission create -c docking-iss -f YOUR_ZIP_FILE_PATH
I tried looking into the issue and also re-runned the submission but I couldn’t able to find anything wrong from our side, can you please check and see if the bounding boxes were in the right format (x, y, w, h) or the
ImageID corresponding to bounding box is correct.
The same question was also replied in discord too but i will also send it here in case another participant also had the same question
Yep, so the Distance Loss is mainly taken into account, but in case, there was a same score in Distance Loss, the second Docking Port Loss is taken into account.
Sorry for a little late reply but I looked into the notebook & the output
.mp4 predictions, and found out that the videos the
gen_predictions function is outputting contains only single frame.
And it’s because the
predictions variable, from the
gen_predictions function, has shape
torch.Size([1, 1, 128, 128]) which represents a single image, the
image_batch variable has also the same shape.
So i find out the main reason for this error is because you changed the batch size of
test_loader from 23 ( in getting started code ) to 1. The main reason of using batch size of 23 in getting started code was because that each batch will represent each video. So, you will change to change your batch size to 23 and hopefully it will all work.
Let me surely know if you have any more doubts.
[Getting Started Notebook] Semantic Segmentation A Getting Started notebook for Semantic Segmentation Puzzle of BlitzXI.
[Getting Started Notebook] Object Detection A Getting Started notebook for Object Detection Puzzle of BlitzXI.
[Getting Started Notebook] Environment Classification A Getting Started notebook for Environment Classification Puzzle of BlitzXI.
[Getting Started Notebook] Lidar Car Detection A Getting Started notebook for Car Detection using Lidar Puzzle of BlitzXI.
[Getting Started Notebook] Obstacle Prediction A Getting Started notebook for Obstacle Prediction Puzzle of BlitzXI.
[Minimal Submission] Iceberg Detection A minimal notebook for Iceberg Detection Puzzle of BlitzX.
[Getting Started Notebook] Iceberg Detection A Getting Started notebook for Icebeg Detection Puzzle of BlitzX.
[Random Submission] Cloud Removal A Random Submission notebook for Cloud Removal Puzzle of BlitzX.
[Getting Started Notebook] Starship Detection A Getting Started notebook for Starship Detection Puzzle of BlitzX.
[Getting Started Notebook] Trees Segmentation A Getting Started notebook for Trees Segmentation Puzzle of BlitzX.
[Getting Started Notebook] Docking ISS A Getting Started notebook for Docking ISS Puzzle of BlitzX.
[In Depth Code] Emotion Detection using spaCy This is a more In depth Code of the Emotion Detection for Blitz 9.
[Getting Started Code] Sound Prediction In this final challenge of Blitz 9, we need to predict the sentences spoken from sound.
[Getting Started Code] NLP Feature Engineering In this fourth challenge of Blitz 9, we are doing Feature Engineering on the Texts.
[Getting Started Code] De Scambling Text using Transformers In this third challenge, we are going to HuggingFace for Sequence to Sequence Prediction.
[Getting Started Code] Research Paper Classification In this second challenge of Blitz 9, we are going to use LSTM for multi class text classification
[Getting Started Code] Emotion Detection using spaCy In this first challenge, we are going to learn fundamentals of Natural Language Processing.
[Baseline] F1 Detection Baseline notebook for F1 Detection Challenge of Blitz 8
[Baseline] F1 Team Classification Baseline notebook for F1 Team Classification Challenge of Blitz 8
[Baseline] F1 Speed Recognition F1 Speed Recognition baseline notebook from F1 Speed Recognition Challenge of Blitz 8
[Baseline] F1 Car Rotation F1 Car Rotation baseline notebook from F1 Car Rotation Challenge of Blitz 8
[Baseline] F1 Smoke Elimination Remove Smoke baseline notebook from F1 Car Challenge of Blitz 8
[Baseline] Space Debris Detection A getting started code for the Space Debris Detection Challenge
[Baseline] Image Correction A getting started code for Image Correction Challenge.
[Baseline] Rover Stage Prediction A getting started code for the Rover Stage Prediction Challenge.
[Baseline] Mars Rotation Prediction A getting started code for the Mars Rotation Prediction Challenge.
[Baseline] Rover Classification A getting started code for the Rover Classification Challenge.