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JorgeGIT
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finetuned-Leukemia-cell

Image Classification
Transformers
TensorBoard
Safetensors
convnext
generated_from_trainer
Eval Results
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  1. README.md +108 -23
  2. model.safetensors +1 -1
  3. runs/Dec03_08-57-44_be923f88cf5c/events.out.tfevents.1701593873.be923f88cf5c.792.0 +3 -0
  4. runs/Dec03_08-58-56_be923f88cf5c/events.out.tfevents.1701593940.be923f88cf5c.792.1 +3 -0
  5. runs/Dec03_08-59-48_be923f88cf5c/events.out.tfevents.1701593993.be923f88cf5c.792.2 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9661654135338346
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1249
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- - Accuracy: 0.9662
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  ## Model description
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@@ -58,30 +58,115 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 50
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  - mixed_precision_training: Native AMP
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.3396 | 2.94 | 100 | 0.2611 | 0.9060 |
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- | 0.2488 | 5.88 | 200 | 0.2651 | 0.9173 |
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- | 0.1783 | 8.82 | 300 | 0.1906 | 0.9323 |
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- | 0.0837 | 11.76 | 400 | 0.1773 | 0.9511 |
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- | 0.0934 | 14.71 | 500 | 0.2027 | 0.9361 |
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- | 0.1283 | 17.65 | 600 | 0.0602 | 0.9737 |
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- | 0.06 | 20.59 | 700 | 0.1383 | 0.9624 |
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- | 0.024 | 23.53 | 800 | 0.0773 | 0.9737 |
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- | 0.0446 | 26.47 | 900 | 0.1669 | 0.9549 |
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- | 0.0342 | 29.41 | 1000 | 0.1320 | 0.9624 |
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- | 0.0458 | 32.35 | 1100 | 0.1128 | 0.9662 |
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- | 0.0394 | 35.29 | 1200 | 0.2099 | 0.9436 |
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- | 0.0593 | 38.24 | 1300 | 0.0890 | 0.9774 |
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- | 0.0346 | 41.18 | 1400 | 0.1216 | 0.9662 |
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- | 0.0535 | 44.12 | 1500 | 0.1303 | 0.9662 |
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- | 0.0139 | 47.06 | 1600 | 0.1195 | 0.9624 |
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- | 0.0476 | 50.0 | 1700 | 0.1249 | 0.9662 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
22
  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9849624060150376
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
32
 
33
  This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
34
  It achieves the following results on the evaluation set:
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+ - Loss: 0.1128
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+ - Accuracy: 0.9850
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  ## Model description
39
 
 
58
  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
60
  - lr_scheduler_type: linear
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+ - num_epochs: 300
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  - mixed_precision_training: Native AMP
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:------:|:-----:|:---------------:|:--------:|
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+ | 0.3182 | 2.94 | 100 | 0.2301 | 0.9211 |
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+ | 0.2223 | 5.88 | 200 | 0.3411 | 0.8910 |
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+ | 0.1695 | 8.82 | 300 | 0.1168 | 0.9624 |
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+ | 0.0579 | 11.76 | 400 | 0.1632 | 0.9511 |
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+ | 0.1184 | 14.71 | 500 | 0.4665 | 0.8346 |
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+ | 0.0575 | 17.65 | 600 | 0.1563 | 0.9586 |
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+ | 0.1087 | 20.59 | 700 | 0.2023 | 0.9511 |
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+ | 0.1164 | 23.53 | 800 | 0.2283 | 0.9398 |
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+ | 0.1144 | 26.47 | 900 | 0.1130 | 0.9624 |
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+ | 0.1821 | 29.41 | 1000 | 0.1155 | 0.9737 |
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+ | 0.0882 | 32.35 | 1100 | 0.0760 | 0.9850 |
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+ | 0.1099 | 35.29 | 1200 | 0.0894 | 0.9737 |
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+ | 0.053 | 38.24 | 1300 | 0.1248 | 0.9699 |
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+ | 0.0489 | 41.18 | 1400 | 0.1081 | 0.9774 |
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+ | 0.065 | 44.12 | 1500 | 0.1694 | 0.9549 |
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+ | 0.037 | 47.06 | 1600 | 0.1060 | 0.9699 |
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+ | 0.0281 | 50.0 | 1700 | 0.0892 | 0.9737 |
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+ | 0.0394 | 52.94 | 1800 | 0.1680 | 0.9624 |
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+ | 0.0828 | 55.88 | 1900 | 0.1404 | 0.9774 |
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+ | 0.0663 | 58.82 | 2000 | 0.1683 | 0.9662 |
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+ | 0.0698 | 61.76 | 2100 | 0.1517 | 0.9624 |
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+ | 0.0938 | 64.71 | 2200 | 0.1031 | 0.9737 |
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+ | 0.0324 | 67.65 | 2300 | 0.1251 | 0.9812 |
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+ | 0.0713 | 70.59 | 2400 | 0.1597 | 0.9662 |
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+ | 0.059 | 73.53 | 2500 | 0.1455 | 0.9699 |
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+ | 0.0404 | 76.47 | 2600 | 0.0924 | 0.9624 |
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+ | 0.0526 | 79.41 | 2700 | 0.0853 | 0.9812 |
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+ | 0.0439 | 82.35 | 2800 | 0.0815 | 0.9850 |
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+ | 0.0485 | 85.29 | 2900 | 0.1192 | 0.9774 |
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+ | 0.0498 | 88.24 | 3000 | 0.0958 | 0.9737 |
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+ | 0.0181 | 91.18 | 3100 | 0.1351 | 0.9699 |
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+ | 0.0226 | 94.12 | 3200 | 0.1458 | 0.9774 |
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+ | 0.1115 | 97.06 | 3300 | 0.1453 | 0.9737 |
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+ | 0.0349 | 100.0 | 3400 | 0.1257 | 0.9812 |
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+ | 0.0246 | 102.94 | 3500 | 0.1405 | 0.9662 |
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+ | 0.0084 | 105.88 | 3600 | 0.0666 | 0.9887 |
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+ | 0.0174 | 108.82 | 3700 | 0.1419 | 0.9662 |
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+ | 0.0432 | 111.76 | 3800 | 0.2027 | 0.9662 |
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+ | 0.0164 | 114.71 | 3900 | 0.0671 | 0.9812 |
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+ | 0.0223 | 117.65 | 4000 | 0.0722 | 0.9850 |
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+ | 0.012 | 120.59 | 4100 | 0.1285 | 0.9699 |
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+ | 0.0143 | 123.53 | 4200 | 0.1102 | 0.9812 |
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+ | 0.0254 | 126.47 | 4300 | 0.1139 | 0.9812 |
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+ | 0.018 | 129.41 | 4400 | 0.1056 | 0.9737 |
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+ | 0.0011 | 132.35 | 4500 | 0.1097 | 0.9774 |
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+ | 0.08 | 135.29 | 4600 | 0.1425 | 0.9662 |
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+ | 0.0292 | 138.24 | 4700 | 0.0871 | 0.9812 |
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+ | 0.0248 | 141.18 | 4800 | 0.1082 | 0.9699 |
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+ | 0.0064 | 144.12 | 4900 | 0.0644 | 0.9850 |
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+ | 0.0115 | 147.06 | 5000 | 0.0912 | 0.9812 |
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+ | 0.052 | 150.0 | 5100 | 0.0927 | 0.9850 |
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+ | 0.0103 | 152.94 | 5200 | 0.1129 | 0.9774 |
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+ | 0.0185 | 155.88 | 5300 | 0.1250 | 0.9699 |
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+ | 0.0185 | 158.82 | 5400 | 0.1226 | 0.9737 |
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+ | 0.0002 | 161.76 | 5500 | 0.1146 | 0.9812 |
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+ | 0.0249 | 164.71 | 5600 | 0.1945 | 0.9737 |
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+ | 0.0165 | 167.65 | 5700 | 0.1875 | 0.9586 |
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+ | 0.0028 | 170.59 | 5800 | 0.1045 | 0.9774 |
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+ | 0.0044 | 173.53 | 5900 | 0.1279 | 0.9774 |
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+ | 0.0078 | 176.47 | 6000 | 0.0967 | 0.9774 |
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+ | 0.0093 | 179.41 | 6100 | 0.1450 | 0.9812 |
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+ | 0.0261 | 182.35 | 6200 | 0.0815 | 0.9850 |
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+ | 0.0218 | 185.29 | 6300 | 0.1586 | 0.9699 |
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+ | 0.1184 | 188.24 | 6400 | 0.1481 | 0.9812 |
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+ | 0.0011 | 191.18 | 6500 | 0.1698 | 0.9737 |
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+ | 0.0131 | 194.12 | 6600 | 0.2247 | 0.9662 |
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+ | 0.0156 | 197.06 | 6700 | 0.1205 | 0.9812 |
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+ | 0.007 | 200.0 | 6800 | 0.1864 | 0.9699 |
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+ | 0.015 | 202.94 | 6900 | 0.1684 | 0.9774 |
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+ | 0.0032 | 205.88 | 7000 | 0.0835 | 0.9850 |
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+ | 0.0017 | 208.82 | 7100 | 0.1174 | 0.9812 |
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+ | 0.0397 | 211.76 | 7200 | 0.1926 | 0.9662 |
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+ | 0.0015 | 214.71 | 7300 | 0.1646 | 0.9699 |
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+ | 0.0046 | 217.65 | 7400 | 0.1520 | 0.9774 |
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+ | 0.0193 | 220.59 | 7500 | 0.1436 | 0.9812 |
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+ | 0.0474 | 223.53 | 7600 | 0.1747 | 0.9737 |
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+ | 0.001 | 226.47 | 7700 | 0.1647 | 0.9812 |
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+ | 0.0005 | 229.41 | 7800 | 0.1992 | 0.9699 |
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+ | 0.0119 | 232.35 | 7900 | 0.1545 | 0.9699 |
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+ | 0.0153 | 235.29 | 8000 | 0.2018 | 0.9662 |
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+ | 0.0106 | 238.24 | 8100 | 0.1798 | 0.9774 |
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+ | 0.0012 | 241.18 | 8200 | 0.1896 | 0.9774 |
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+ | 0.0 | 244.12 | 8300 | 0.1500 | 0.9812 |
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+ | 0.0339 | 247.06 | 8400 | 0.1890 | 0.9662 |
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+ | 0.0016 | 250.0 | 8500 | 0.1410 | 0.9812 |
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+ | 0.0003 | 252.94 | 8600 | 0.1341 | 0.9812 |
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+ | 0.001 | 255.88 | 8700 | 0.1209 | 0.9850 |
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+ | 0.0071 | 258.82 | 8800 | 0.1191 | 0.9812 |
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+ | 0.0 | 261.76 | 8900 | 0.0960 | 0.9887 |
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+ | 0.0016 | 264.71 | 9000 | 0.1063 | 0.9850 |
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+ | 0.0048 | 267.65 | 9100 | 0.1583 | 0.9737 |
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+ | 0.0026 | 270.59 | 9200 | 0.1473 | 0.9774 |
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+ | 0.0006 | 273.53 | 9300 | 0.1325 | 0.9812 |
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+ | 0.0226 | 276.47 | 9400 | 0.1214 | 0.9812 |
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+ | 0.0075 | 279.41 | 9500 | 0.1399 | 0.9812 |
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+ | 0.0047 | 282.35 | 9600 | 0.1291 | 0.9850 |
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+ | 0.0 | 285.29 | 9700 | 0.1117 | 0.9812 |
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+ | 0.0001 | 288.24 | 9800 | 0.1137 | 0.9850 |
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+ | 0.0001 | 291.18 | 9900 | 0.1117 | 0.9850 |
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+ | 0.0 | 294.12 | 10000 | 0.1061 | 0.9850 |
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+ | 0.0 | 297.06 | 10100 | 0.1129 | 0.9850 |
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+ | 0.0057 | 300.0 | 10200 | 0.1128 | 0.9850 |
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  ### Framework versions
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