Model save

README.md

@@ -1,45 +1,25 @@
 ---
+library_name: transformers
 license: apache-2.0
+base_model: timm/resnet18.a1_in1k
 tags:
-- image-classification
-- vision
 - generated_from_trainer
-datasets:
-- AI-Lab-Makerere/beans
 metrics:
 - accuracy
-base_model: google/vit-base-patch16-224-in21k
 model-index:
 - name: vit-base-beans
-  results:
-  - task:
-      type: image-classification
-      name: Image Classification
-    dataset:
-      name: beans
-      type: beans
-      config: default
-      split: validation
-      args: default
-    metrics:
-    - type: accuracy
-      value: 0.9849624060150376
-      name: Accuracy
+  results: []
 ---
 
 <!-- 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. -->
 
-# THIS IS A TEST REPO FOR DEBUGGING! 
-
-This repo is here as a result of playing with and debugging training scripts and push to hub features. As such, the TesnorFlow and PyTorch models will be out of sync and different weights may be push at any time, including pushing models with very low performance. 
-
 # vit-base-beans
 
-This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
+This model is a fine-tuned version of [timm/resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0630
-- Accuracy: 0.9850
+- Loss: 1.0324
+- Accuracy: 0.6917
 
 ## Model description
 
@@ -70,16 +50,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.3038        | 1.0   | 130  | 0.2396          | 0.9624   |
-| 0.1609        | 2.0   | 260  | 0.1130          | 0.9774   |
-| 0.2313        | 3.0   | 390  | 0.0809          | 0.9850   |
-| 0.1436        | 4.0   | 520  | 0.0738          | 0.9850   |
-| 0.1086        | 5.0   | 650  | 0.0630          | 0.9850   |
+| 1.0884        | 1.0   | 130  | 1.0903          | 0.4060   |
+| 1.0721        | 2.0   | 260  | 1.0681          | 0.5188   |
+| 1.0623        | 3.0   | 390  | 1.0460          | 0.6391   |
+| 1.052         | 4.0   | 520  | 1.0410          | 0.6165   |
+| 1.0519        | 5.0   | 650  | 1.0324          | 0.6917   |
 
 
 ### Framework versions
 
-- Transformers 4.27.0.dev0
-- Pytorch 1.14.0.dev20221118
-- Datasets 2.9.1.dev0
-- Tokenizers 0.13.2
+- Transformers 4.46.0.dev0
+- Pytorch 2.4.0
+- Datasets 2.15.1.dev0
+- Tokenizers 0.20.0

emissions.csv

@@ -1,2 +1,2 @@
 timestamp,experiment_id,project_name,duration,emissions,energy_consumed,country_name,country_iso_code,region,on_cloud,cloud_provider,cloud_region
-2023-01-30T16:18:59,e62bec3c-2bd4-4b3f-8474-0de25a0fdd08,codecarbon,959.3560330867767,0.007588203375663657,0.011290137082590192,United Kingdom,GBR,england,N,,
+2024-09-30T20:12:46,a1b98ce0-5999-43a7-ae1d-c76c57ba4514,codecarbon,89.43079400062561,0.0007095535156443428,0.0010557118809902488,United Kingdom,GBR,england,N,,

runs/Sep30_20-11-09_Amys-MBP-2.communityfibre.co.uk/events.out.tfevents.1727723476.Amys-MBP-2.communityfibre.co.uk.11627.0

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-size 20489
+oid sha256:2175acb01da297e3533348e246f583eae4f510adffac1fd4eb3b4f49a998cbaf
+size 21166