End of training

README.md

@@ -1,12 +1,12 @@
 ---
-base_model: nvidia/segformer-b1-finetuned-ade-512-512
 license: other
-metrics:
-- precision
+base_model: nvidia/segformer-b1-finetuned-ade-512-512
 tags:
 - vision
 - image-segmentation
 - generated_from_trainer
+metrics:
+- precision
 model-index:
 - name: segformer_b1_finetuned_segment_pv_p100_4batch
   results: []
@@ -15,14 +15,14 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/mouadn773/huggingface/runs/2mibijf2)
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/mouadn773/segformer-pv-4batches/runs/0aseuh20)
 # segformer_b1_finetuned_segment_pv_p100_4batch
 
 This model is a fine-tuned version of [nvidia/segformer-b1-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b1-finetuned-ade-512-512) on the mouadenna/satellite_PV_dataset_train_test_v1 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0066
-- Mean Iou: 0.8262
-- Precision: 0.8960
+- Loss: 0.0072
+- Mean Iou: 0.8692
+- Precision: 0.9162
 
 ## Model description
 
@@ -41,20 +41,60 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 0.0004
+- learning_rate: 4e-05
 - train_batch_size: 4
 - eval_batch_size: 4
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 1
+- num_epochs: 40
+- mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Precision |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|
-| 0.0432        | 1.0   | 917  | 0.0066          | 0.8262   | 0.8960    |
+| Training Loss | Epoch | Step  | Validation Loss | Mean Iou | Precision |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|
+| 0.4877        | 1.0   | 917   | 0.2178          | 0.5326   | 0.6037    |
+| 0.0992        | 2.0   | 1834  | 0.0258          | 0.7353   | 0.7913    |
+| 0.022         | 3.0   | 2751  | 0.0119          | 0.7764   | 0.9267    |
+| 0.0106        | 4.0   | 3668  | 0.0084          | 0.8093   | 0.8316    |
+| 0.0069        | 5.0   | 4585  | 0.0070          | 0.8327   | 0.8698    |
+| 0.0054        | 6.0   | 5502  | 0.0057          | 0.8431   | 0.8829    |
+| 0.0044        | 7.0   | 6419  | 0.0055          | 0.8494   | 0.8877    |
+| 0.0039        | 8.0   | 7336  | 0.0059          | 0.8474   | 0.9002    |
+| 0.0036        | 9.0   | 8253  | 0.0056          | 0.8505   | 0.8894    |
+| 0.0031        | 10.0  | 9170  | 0.0054          | 0.8575   | 0.8994    |
+| 0.0031        | 11.0  | 10087 | 0.0051          | 0.8620   | 0.9190    |
+| 0.0027        | 12.0  | 11004 | 0.0053          | 0.8646   | 0.9043    |
+| 0.0026        | 13.0  | 11921 | 0.0059          | 0.8643   | 0.9275    |
+| 0.0028        | 14.0  | 12838 | 0.0055          | 0.8641   | 0.9131    |
+| 0.0025        | 15.0  | 13755 | 0.0053          | 0.8661   | 0.9120    |
+| 0.0023        | 16.0  | 14672 | 0.0056          | 0.8644   | 0.9107    |
+| 0.0022        | 17.0  | 15589 | 0.0052          | 0.8668   | 0.9125    |
+| 0.0022        | 18.0  | 16506 | 0.0054          | 0.8700   | 0.9143    |
+| 0.0022        | 19.0  | 17423 | 0.0059          | 0.8685   | 0.9180    |
+| 0.002         | 20.0  | 18340 | 0.0057          | 0.8696   | 0.9157    |
+| 0.0019        | 21.0  | 19257 | 0.0061          | 0.8682   | 0.9136    |
+| 0.0019        | 22.0  | 20174 | 0.0069          | 0.8606   | 0.9262    |
+| 0.0019        | 23.0  | 21091 | 0.0062          | 0.8700   | 0.9172    |
+| 0.0018        | 24.0  | 22008 | 0.0061          | 0.8682   | 0.9258    |
+| 0.0019        | 25.0  | 22925 | 0.0062          | 0.8669   | 0.9124    |
+| 0.002         | 26.0  | 23842 | 0.0065          | 0.8672   | 0.9206    |
+| 0.0017        | 27.0  | 24759 | 0.0062          | 0.8688   | 0.9108    |
+| 0.0016        | 28.0  | 25676 | 0.0066          | 0.8686   | 0.9154    |
+| 0.0016        | 29.0  | 26593 | 0.0066          | 0.8704   | 0.9175    |
+| 0.0016        | 30.0  | 27510 | 0.0068          | 0.8664   | 0.9127    |
+| 0.0015        | 31.0  | 28427 | 0.0069          | 0.8679   | 0.9150    |
+| 0.0015        | 32.0  | 29344 | 0.0065          | 0.8691   | 0.9196    |
+| 0.0015        | 33.0  | 30261 | 0.0069          | 0.8676   | 0.9130    |
+| 0.0014        | 34.0  | 31178 | 0.0067          | 0.8691   | 0.9164    |
+| 0.0014        | 35.0  | 32095 | 0.0071          | 0.8679   | 0.9153    |
+| 0.0014        | 36.0  | 33012 | 0.0072          | 0.8687   | 0.9127    |
+| 0.0014        | 37.0  | 33929 | 0.0073          | 0.8689   | 0.9151    |
+| 0.0014        | 38.0  | 34846 | 0.0069          | 0.8696   | 0.9183    |
+| 0.0014        | 39.0  | 35763 | 0.0070          | 0.8694   | 0.9163    |
+| 0.0013        | 40.0  | 36680 | 0.0072          | 0.8692   | 0.9162    |
 
 
 ### Framework versions

model.safetensors

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runs/Aug01_03-06-59_a98a13071bff/events.out.tfevents.1722481641.a98a13071bff.24.0

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training_args.bin

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