Open Jacobsolawetz opened 3 years ago
Its so painful trying to make supervisely work with the current release. My team has already established workflows in supervisely but getting the necessary updates make it feel like a hassle. Would the tradeoff for a public workspace be worth it in this case? Were at the phase of reiterating assisted labelling to make it easier to improve the overall model. Would roboflow help better than supervisely? Are there any migration tutorials?
@Jacobsolawetz just noticed some broken links above. Can you send us some fixes? Thanks!
@Jacobsolawetz pinging you again about the broken links above
@Jacobsolawetz pinging you again about the broken links above
it seems you need to ping them again
@sezer-muhammed @glenn-jocher Thanks for flagging me! I must have nuked those gif links, updated now (and in our custom training notebook)
And @gembancud do I have a blog post for you!
I used roboflow to annotate my dataset and it shows there are 435 pictures, but after export there are only 290 pictures.
@lengdanlexin could you provide some details? @Jacobsolawetz from Roboflow might be able to help you.
@glenn-jocher Thank you for your reply. In the picture below you can see a total of 427 images, but the number of exported images is much less than this. I've seen in roboflow's forums that someone had a similar problem, but it doesn't seem like the issue has been resolved.
Thanks for bringing this to our attention, @lengdanlexin. When exporting your dataset, please ensure that you are not inadvertently applying any filters or criteria that could result in fewer images being exported. If the issue persists, I recommend reaching out to Roboflow's support for further assistance.
You can now use Roboflow to organize, label, prepare, version, and host your datasets for training YOLOv5 🚀 models. Roboflow is free to use with YOLOv5 if you make your workspace public. UPDATED 30 September 2021. ​
Upload
You can upload your data to Roboflow via web UI, rest API, or python. ​
Labeling
After uploading data to Roboflow, you can label your data and review previous labels. ​ ​
Versioning
You can make versions of your dataset with different preprocessing and offline augmentation options. YOLOv5 does online augmentations natively, so be intentional when layering Roboflow's offline augs on top. ​ ​
Exporting Data
You can download your data in YOLOv5 format to quickly begin training. ​
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Custom Training
We have released a custom training tutorial demonstrating all of the above capabilities. You can access the code here: ​ ​
Active Learning
The real world is messy and your model will invariably encounter situations your dataset didn't anticipate. Using active learning is an important strategy to iteratively improve your dataset and model. With the Roboflow and YOLOv5 integration, you can quickly make improvements on your model deployments by using a battle tested machine learning pipeline. ​
​ Please let us know of any curiosities or requests below 👇