Body: <p>I'm currently building an Object Detection Model to recognize food ingredients in refrigerator pictures. I use Google Colab to code and train my model, and I use the architecture of RESNET-50. I wiped the already existing classes and I'm training it on a dataset with about 6'000 images (already annotated, 70% train, 15% valid, 15% test) with Google Colab's best GPU. After training it's like my model still hasn't learnt anything. Is it normal to achieve such poor results or is there a way to train faster with better results? I've heard of Google Cloud but I don't know if it'll make a difference. Am I missing some hidden parameter?</p>
  <p>Here is the Google-Colab Notebook I coded everything on: <a href="https://colab.research.google.com/drive/1m4zbNE8-qVUkep-37Mz2S3URvELdGWX6?usp=sharing" rel="nofollow noreferrer">https://colab.research.google.com/drive/1m4zbNE8-qVUkep-37Mz2S3URvELdGWX6?usp=sharing</a></p>
  <p>To train my model, I use pytorch_lightning's Trainer. Training needs a really long time though, and even after training for 30 epochs the confidence levels on unseen data remain below 5% when the preds are even right (which is not usually the case).</p>
  <p>Edit: At the moment, I'm using a new, more qualitative Dataset containing 20k images divided into 68 classes. I haven't tested the model enough as I saw that the predictions were much too low, if you look at the Notebook you'll see that I have to set a CONFIDENCE_THRESHOLD_2 = 0.03 to get ANY results, but I'll assume that is due to the non-sufficient training.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-10-24T06:56:38.837Z
Comments.Text: Can you please provide information regarding the dataset as how many instances  per class you have mentioned preds are right so for if possible can you please share the insight of predictions like on which classes you are getting good results and bad results , and also which loss function and optimization you are using it would be best if you provide the colab file
Comments.UserDisplayName: Keval Pandya
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-10-24T06:57:04.940Z
Comments.Text: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
Comments.UserDisplayName: Community
Tags: <machine-learning><computer-vision><pytorch><object-detection>
Title: Is there a fast way to raise the confidence levels of an object detection model?
UserDisplayName: Matix R.
ThreadText: # Is there a fast way to raise the confidence levels of an object detection model?
  
  (Asked by: username_0 on 2024-10-21)
  
  I'm currently building an Object Detection Model to recognize food ingredients in refrigerator pictures. I use Google Colab to code and train my model, and I use the architecture of RESNET-50. I wiped the already existing classes and I'm training it on a dataset with about 6'000 images (already annotated, 70% train, 15% valid, 15% test) with Google Colab's best GPU. After training it's like my model still hasn't learnt anything. Is it normal to achieve such poor results or is there a way to train faster with better results? I've heard of Google Cloud but I don't know if it'll make a difference. Am I missing some hidden parameter?
  
  Here is the Google-Colab Notebook I coded everything on: <https://colab.research.google.com/drive/1m4zbNE8-qVUkep-37Mz2S3URvELdGWX6?usp=sharing>
  
  To train my model, I use pytorch\_lightning's Trainer. Training needs a really long time though, and even after training for 30 epochs the confidence levels on unseen data remain below 5% when the preds are even right (which is not usually the case).
  
  Edit: At the moment, I'm using a new, more qualitative Dataset containing 20k images divided into 68 classes. I haven't tested the model enough as I saw that the predictions were much too low, if you look at the Notebook you'll see that I have to set a CONFIDENCE\_THRESHOLD\_2 = 0.03 to get ANY results, but I'll assume that is due to the non-sufficient training.
  
  **Tags:** machine-learning, computer-vision, pytorch, object-detection
  > **Comments:**
  > **username_1**: Can you please provide information regarding the dataset as how many instances  per class you have mentioned preds are right so for if possible can you please share the insight of predictions like on which classes you are getting good results and bad results , and also which loss function and optimization you are using it would be best if you provide the colab file  
  > **username_2**: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.  
  
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  ## 0 answers
  
  