End of training

README.md

@@ -1,12 +1,12 @@
 ---
-base_model: nvidia/segformer-b2-finetuned-ade-512-512
 license: other
-metrics:
-- precision
+base_model: nvidia/segformer-b2-finetuned-ade-512-512
 tags:
 - vision
 - image-segmentation
 - generated_from_trainer
+metrics:
+- precision
 model-index:
 - name: segformer-b2-finetuned-segments-pv_v1_normalized_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/2c4v0grk)
+[<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/mmtwbyor)
 # segformer-b2-finetuned-segments-pv_v1_normalized_p100_4batch
 
 This model is a fine-tuned version of [nvidia/segformer-b2-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b2-finetuned-ade-512-512) on the mouadenna/satellite_PV_dataset_train_test_v1 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0102
-- Mean Iou: 0.8285
-- Precision: 0.8729
+- Loss: nan
+- Mean Iou: 0.0
+- Precision: 1.0
 
 ## Model description
 
@@ -50,14 +50,53 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.001
-- num_epochs: 1
+- num_epochs: 40
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch  | Step | Validation Loss | Mean Iou | Precision |
-|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|
-| 0.0107        | 0.9989 | 229  | 0.0102          | 0.8285   | 0.8729    |
+| Training Loss | Epoch   | Step | Validation Loss | Mean Iou | Precision |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|
+| 0.01          | 0.9989  | 229  | 0.0088          | 0.8105   | 0.8817    |
+| 0.0062        | 1.9978  | 458  | 0.0075          | 0.8201   | 0.8726    |
+| 0.0049        | 2.9967  | 687  | 0.0063          | 0.8297   | 0.8867    |
+| 0.0053        | 4.0     | 917  | 0.0055          | 0.8425   | 0.8845    |
+| 0.0037        | 4.9989  | 1146 | 0.0058          | 0.8380   | 0.8823    |
+| 0.0039        | 5.9978  | 1375 | 0.0211          | 0.6114   | 0.9766    |
+| 0.0037        | 6.9967  | 1604 | 0.3403          | 0.0      | 1.0       |
+| 0.0002        | 8.0     | 1834 | nan             | 0.0      | 1.0       |
+| 0.0003        | 8.9989  | 2063 | nan             | 0.0      | 1.0       |
+| 0.0864        | 9.9978  | 2292 | nan             | 0.0      | 1.0       |
+| 0.0035        | 10.9967 | 2521 | nan             | 0.0      | 1.0       |
+| 0.0045        | 12.0    | 2751 | nan             | 0.0      | 1.0       |
+| 0.0039        | 12.9989 | 2980 | nan             | 0.0      | 1.0       |
+| 0.8023        | 13.9978 | 3209 | nan             | 0.0      | 1.0       |
+| 0.0041        | 14.9967 | 3438 | nan             | 0.0      | 1.0       |
+| 7.0711        | 16.0    | 3668 | nan             | 0.0      | 1.0       |
+| 0.0039        | 16.9989 | 3897 | nan             | 0.0      | 1.0       |
+| 19.4385       | 17.9978 | 4126 | nan             | 0.0      | 1.0       |
+| 0.0001        | 18.9967 | 4355 | nan             | 0.0      | 1.0       |
+| 1.7398        | 20.0    | 4585 | nan             | 0.0      | 1.0       |
+| 0.2879        | 20.9989 | 4814 | nan             | 0.0      | 1.0       |
+| 0.0005        | 21.9978 | 5043 | nan             | 0.0      | 1.0       |
+| 5.8398        | 22.9967 | 5272 | nan             | 0.0      | 1.0       |
+| 0.0004        | 24.0    | 5502 | nan             | 0.0      | 1.0       |
+| 0.0002        | 24.9989 | 5731 | nan             | 0.0      | 1.0       |
+| 0.0016        | 25.9978 | 5960 | nan             | 0.0      | 1.0       |
+| 0.0034        | 26.9967 | 6189 | nan             | 0.0      | 1.0       |
+| 0.0004        | 28.0    | 6419 | nan             | 0.0      | 1.0       |
+| 0.0036        | 28.9989 | 6648 | nan             | 0.0      | 1.0       |
+| 0.0314        | 29.9978 | 6877 | nan             | 0.0      | 1.0       |
+| 0.0921        | 30.9967 | 7106 | nan             | 0.0      | 1.0       |
+| 89.1025       | 32.0    | 7336 | nan             | 0.0      | 1.0       |
+| 0.0073        | 32.9989 | 7565 | nan             | 0.0      | 1.0       |
+| 0.0126        | 33.9978 | 7794 | nan             | 0.0      | 1.0       |
+| 0.0094        | 34.9967 | 8023 | nan             | 0.0      | 1.0       |
+| 0.0001        | 36.0    | 8253 | nan             | 0.0      | 1.0       |
+| 4.3987        | 36.9989 | 8482 | nan             | 0.0      | 1.0       |
+| 0.0005        | 37.9978 | 8711 | nan             | 0.0      | 1.0       |
+| 0.0202        | 38.9967 | 8940 | nan             | 0.0      | 1.0       |
+| 0.1612        | 39.9564 | 9160 | nan             | 0.0      | 1.0       |
 
 
 ### Framework versions

model.safetensors

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training_args.bin

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