Orion
- 5 Devlogs
- 10 Total hours
Classifying White Blood Cell from Scratch with custom CNN
38m 28s logged
Retrained the model as I have added ‘label_smoothing=0.1’ to criterion to lower the probability of image that is jot white blood cell to be classified and show as a type of wbc with high confident score, also removed static glassmorphism header from the site and also added grid table with some clustered gradients as the background to not make it static
Ship #1
Changes requested
I make an AI model that could classify white blood cell from scratch (one input at a time as I dont have enough compute unit), the challenging parts ware that I'm not really that good at math so yeah need some AI to help coding the model part to ensure as much accuracy as possible, although it's minimal but I tried to make it have the highest possible accuracy, the last trained one got 'Peak' at 99.19% quite good but it results in getting 100% confident score every time. Before testing, you can test it directly in the web, no downloading or installation required unless you want to make it locally (test at orion.phattar4phan.workers.dev), Proud to show that at least I can make an AI model, next one will be small language model with only ~7.8m parameters, just wait!
1h 44m 15s logged
Finale version, removed the 5 models comparison as I dont have time to create all that right now. Only 1 model 1 interference and it’s donee
3h 50m 13s logged
Created new landing page and web app page, also bunch of new objs
32m 39s logged
Modified some configurations and deploy or vercel (try it at orion-cnn.vercel.app)
3h 5m 30s logged
- Trained CNN from scratch model, and more
training logs:
Training Logs
┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━┓
┃ Epoch ┃ Loss ┃ Accuracy ┃ Valid ┃ Duration(s) ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━┩
│ 1/20 │ 0.8463 │ 66.54% │ 86.70% │ 48.77 │
│ 2/20 │ 0.5111 │ 81.43% │ 92.74% │ 47.21 │
│ 3/20 │ 0.3835 │ 86.39% │ 91.02% │ 47.00 │
│ 4/20 │ 0.3203 │ 88.78% │ 93.51% │ 46.79 │
│ 5/20 │ 0.2671 │ 90.80% │ 93.51% │ 47.19 │
│ 6/20 │ 0.2448 │ 91.36% │ 93.58% │ 47.44 │
│ 7/20 │ 0.2161 │ 92.34% │ 94.11% │ 47.21 │
│ 8/20 │ 0.1961 │ 93.18% │ 95.82% │ 47.16 │
│ 9/20 │ 0.1859 │ 93.48% │ 96.25% │ 47.15 │
│ 10/20 │ 0.1693 │ 93.97% │ 95.72% │ 47.82 │
│ 11/20 │ 0.1592 │ 94.35% │ 94.46% │ 47.51 │
│ 12/20 │ 0.1503 │ 94.60% │ 96.07% │ 47.39 │
│ 13/20 │ 0.1405 │ 95.24% │ 95.26% │ 47.59 │
│ 14/20 │ 0.1356 │ 95.07% │ 96.28% │ 47.66 │
│ 15/20 │ 0.1232 │ 95.79% │ 96.21% │ 48.00 │
│ 16/20 │ 0.1218 │ 95.91% │ 96.81% │ 47.60 │
│ 17/20 │ 0.1195 │ 95.93% │ 98.11% │ 47.72 │
│ 18/20 │ 0.1162 │ 96.10% │ 97.54% │ 48.10 │
│ 19/20 │ 0.1076 │ 96.49% │ 97.58% │ 47.76 │
│ 20/20 │ 0.1054 │ 96.36% │ 97.05% │ 47.81 │
└───────┴────────┴──────────┴────────┴─────────────┘
Best: E:17 | Validation Accuracy: 98.11
Resulted in the best validation accuracy at 98.11% and at epoch 17th, only need to train the remaining 4 models
