Update README.md

README.md

@@ -34,8 +34,23 @@ It achieves the following results on the evaluation set:
 - Accuracy: 0.875
 
 ## Model description
+Fine Tuning from google/vit-base-patch16-224-in21k and dataset from FastJobs/Visual_Emotional_Analysis
+
+Preprocessing : 
+
+Resize: Resizes the image to 224x224 pixels using bilinear interpolation. This ensures all images have consistent dimensions when fed into the model.
+
+RandomHorizontalFlip: Randomly flips the image horizontally with a 50% probability. This helps the model learn to recognize objects from different horizontal orientations.
+
+RandomVerticalFlip: Randomly flips the image vertically with a 50% probability. This helps the model learn to recognize objects from different vertical orientations.
+
+ColorJitter: Randomly alters the brightness, contrast, saturation, and hue of the image. This simulates variations in lighting and color conditions, allowing the model to learn from a wider range of color variations.
+
+ToTensor: Converts the image into a PyTorch tensor, which is the format required for processing images in deep learning frameworks like PyTorch.
+
+Normalize: Normalizes each pixel of the image by subtracting the mean (0.5) and dividing by the standard deviation (0.5). This normalization helps stabilize training and improve model convergence.
+
 
-More information needed
 
 ## Intended uses & limitations
 
@@ -43,12 +58,11 @@ More information needed
 
 ## Training and evaluation data
 
-{'eval_loss': 0.8208037614822388, 'eval_accuracy': 0.875, 'eval_runtime': 5.3137, 'eval_samples_per_second': 30.111, 'eval_steps_per_second': 0.941, 'epoch': 3.0}
-
 More information needed
 
 ## Training procedure
 
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -65,12 +79,73 @@ The following hyperparameters were used during training:
 
 ### Training results
 
+   Epoch	Training Loss	Validation  Loss Accuracy
+    1	        2.084400	2.090225	0.137500
+    2	        2.089700	2.086973	0.118750
+    3	        2.079400	2.086899	0.100000
+    4	        2.098200	2.086151	0.125000
+    5	        2.093100	2.082829	0.137500
+    6           2.083900	2.081236	0.137500
+    7	        2.082900	2.086800	0.081250
+    8	        2.060600	2.077514	0.162500
+    9           2.085300	2.068546	0.143750
+    10	        2.071300	2.076601	0.131250
+    11	        2.059300	2.063258	0.175000
+    12	        2.054800	2.067919	0.125000
+    13	        2.063700	2.059906	0.150000
+    14	        2.044800	2.059610	0.206250
+    15	        2.042200	2.055763	0.181250
+    16	        2.029200	2.058503	0.193750
+    17	        2.033300	2.042262	0.206250
+    18	        2.003300	2.043147	0.206250
+    19	        1.987800	2.035327	0.218750
+    20	        1.987600	2.015316	0.206250
+    21	        1.991800	2.010191	0.231250
+    22	        1.973300	1.999294	0.250000
+    23	        1.950500	1.980282	0.331250
+    24	        1.930900	1.963615	0.281250
+    25          1.887600	1.942629	0.325000
+    26	        1.870200	1.901906	0.381250
+    27	        1.836300	1.867780	0.387500
+    28	        1.804300	1.846487	0.393750
+    29	        1.752700	1.806786	0.431250
+    30	        1.681600	1.756060	0.437500
+    31	        1.660400	1.708304	0.475000
+    32	        1.624700	1.659365	0.493750
+    33	        1.567100	1.620911	0.481250
+    34	        1.503100	1.585212	0.506250
+    35	        1.482700	1.546996	0.518750
+    36          1.468600	1.519542	0.562500
+    37	        1.423800	1.493005	0.575000
+    38	        1.393500	1.469010	0.531250
+    39	        1.297800	1.446551	0.550000
+    40	        1.322000	1.407961	0.556250
+    41          1.322300	1.385930	0.562500
+    42	        1.254800	1.374024	0.562500
+    43	        1.183200	1.338247	0.531250
+    44          1.173300	1.316369	0.575000
+    45	        1.100100	1.283046	0.593750
+    46	        1.069300	1.298898	0.575000
+    47	        1.045900	1.297686	0.587500
+    48	        1.032000	1.269446	0.600000
+    49	        0.962800	1.252569	0.606250
+    50	        0.929700	1.248749	0.587500
+    51	        0.938900	1.213704	0.618750
+    52	        0.887200	1.219889	0.581250
+    53	        0.797000	1.228908	0.575000
+    54	        0.736100	1.185892	0.631250
+
+KeyboardInterrupt because disk full: --> continue from checkpoint 270 / epoch 54
+
+
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
 | 0.8456        | 1.0   | 5    | 0.8537          | 0.8562   |
 | 0.7982        | 2.0   | 10   | 0.8021          | 0.8875   |
 | 0.8028        | 3.0   | 15   | 0.8028          | 0.8438   |
 
+Result : 
+{'eval_loss': 0.8208037614822388, 'eval_accuracy': 0.875, 'eval_runtime': 5.3137, 'eval_samples_per_second': 30.111, 'eval_steps_per_second': 0.941, 'epoch': 3.0}
 
 ### Framework versions